Which Comprehensive Geriatric Assessment (CGA) instruments are currently used in Germany: a survey

BACKGROUND

The Comprehensive Geriatric Assessment (CGA) records geriatric syndromes in a standardized manner, allowing individualized treatment tailored to the patient's needs and resources. Its use has shown a beneficial effect on the functional outcome and survival of geriatric patients. A recently published German S1 guideline for level 2 CGA provides recommendations for the use of a broad variety of different assessment instruments for each geriatric syndrome. However, the actual use of assessment instruments in routine geriatric clinical practice and its consistency with the guideline and the current state of literature has not been investigated to date.

METHODS

An online survey was developed by an expert group of geriatricians and sent to all licenced geriatricians (n = 569) within Germany. The survey included the following geriatric syndromes: motor function and self-help capability, cognition, depression, pain, dysphagia and nutrition, social status and comorbidity, pressure ulcers, language and speech, delirium, and frailty. Respondents were asked to report which geriatric assessment instruments are used to assess the respective syndromes.

RESULTS

A total of 122 clinicians participated in the survey (response rate: 21%); after data cleaning, 76 data sets remained for analysis. All participants regularly used assessment instruments in the following categories: motor function, self-help capability, cognition, depression, and pain. The most frequently used instruments in these categories were the Timed Up and Go (TUG), the Barthel Index (BI), the Mini Mental State Examination (MMSE), the Geriatric Depression Scale (GDS), and the Visual Analogue Scale (VAS). Limited or heterogenous assessments are used in the following categories: delirium, frailty and social status.

CONCLUSIONS

Our results show that the assessment of motor function, self-help capability, cognition, depression, pain, and dysphagia and nutrition is consistent with the recommendations of the S1 guideline for level 2 CGA. Instruments recommended for more frequent use include the Short Physical Performance Battery (SPPB), the Montreal Cognitive Assessment (MoCA), and the WHO-5 (depression). There is a particular need for standardized assessment of delirium, frailty and social status. The harmonization of assessment instruments throughout geriatric departments shall enable more effective treatment and prevention of age-related diseases and syndromes.

Machine learning-based detection of acute psychosocial stress from body posture and movements

Investigating acute stress responses is crucial to understanding the underlying mechanisms of stress. Current stress assessment methods include self-reports that can be biased and biomarkers that are often based on complex laboratory procedures. A promising additional modality for stress assessment might be the observation of body movements, which are affected by negative emotions and threatening situations. In this paper, we investigated the relationship between acute psychosocial stress induction and body posture and movements. We collected motion data from N = 59 individuals over two studies (Pilot Study: N = 20, Main Study: N = 39) using inertial measurement unit (IMU)-based motion capture suits. In both studies, individuals underwent the Trier Social Stress Test (TSST) and a stress-free control condition (friendly-TSST; f-TSST) in randomized order. Our results show that acute stress induction leads to a reproducible freezing behavior, characterized by less overall motion as well as more and longer periods of no movement. Based on these data, we trained machine learning pipelines to detect acute stress solely from movement information, achieving an accuracy of75.0 ± 17.7 % (Pilot Study) and73.4 ± 7.7 % (Main Study). This, for the first time, suggests that body posture and movements can be used to detect whether individuals are exposed to acute psychosocial stress. While more studies are needed to further validate our approach, we are convinced that motion information can be a valuable extension to the existing biomarkers and can help to obtain a more holistic picture of the human stress response. Our work is the first to systematically explore the use of full-body body posture and movement to gain novel insights into the human stress response and its effects on the body and mind.

Estimating 3D kinematics and kinetics from virtual inertial sensor data through musculoskeletal movement simulations

Portable measurement systems using inertial sensors enable motion capture outside the lab, facilitating longitudinal and large-scale studies in natural environments. However, estimating 3D kinematics and kinetics from inertial data for a comprehensive biomechanical movement analysis is still challenging. Machine learning models or stepwise approaches performing Kalman filtering, inverse kinematics, and inverse dynamics can lead to inconsistencies between kinematics and kinetics. We investigated the reconstruction of 3D kinematics and kinetics of arbitrary running motions from inertial sensor data using optimal control simulations of full-body musculoskeletal models. To evaluate the feasibility of the proposed method, we used marker tracking simulations created from optical motion capture data as a reference and for computing virtual inertial data such that the desired solution was known exactly. We generated the inertial tracking simulations by formulating optimal control problems that tracked virtual acceleration and angular velocity while minimizing effort without requiring a task constraint or an initial state. To evaluate the proposed approach, we reconstructed three trials each of straight running, curved running, and a v-cut of 10 participants. We compared the estimated inertial signals and biomechanical variables of the marker and inertial tracking simulations. The inertial data was tracked closely, resulting in low mean root mean squared deviations for pelvis translation (≤20.2 mm), angles (≤1.8 deg), ground reaction forces (≤1.1 BW%), joint moments (≤0.1 BWBH%), and muscle forces (≤5.4 BW%) and high mean coefficients of multiple correlation for all biomechanical variables ( ≥ 0.99 ) . Accordingly, our results showed that optimal control simulations tracking 3D inertial data could reconstruct the kinematics and kinetics of individual trials of all running motions. The simulations led to mutually and dynamically consistent kinematics and kinetics, which allows researching causal chains, for example, to analyze anterior cruciate ligament injury prevention. Our work proved the feasibility of the approach using virtual inertial data. When using the approach in the future with measured data, the sensor location and alignment on the segment must be estimated, and soft-tissue artifacts are potential error sources. Nevertheless, we demonstrated that optimal control simulation tracking inertial data is highly promising for estimating 3D kinematics and kinetics for a comprehensive biomechanical analysis.

Disgust-based approach-avoidance modification training for individuals suffering from elevated stress: A randomized controlled pilot study

Perceived stress, a global health problem associated with various mental disorders, is assumed to be influenced by dysfunctional beliefs. It can be hypothesized that these beliefs can be modified with the help of approach-avoidance modification trainings (AAMTs). In the present study (conducted 2020-2022), we aimed to clarify whether the efficacy of AAMTs can be enhanced by utilizing the expression of emotions to move AAMT stimuli. For this purpose, we tested the feasibility and acceptability of a new AAMT paradigm in which the expression of disgust is used to move stress-increasing beliefs away from oneself and the expression of positive emotions is used to move stress-reducing beliefs towards oneself (AAMT-DP). Additionally, we explored the therapeutic potential of the AAMT-DP intervention by comparing it to an inactive control condition and to a conventional AAMT in which stimuli are moved by swipe movements (n = 10 in each condition). The primary outcome was perceived stress 1 week after the training as assessed with the Perceived Stress Scale. Findings indicate sufficient feasibility and acceptability of the intervention and that the decrease in perceived stress in the AAMT-DP condition was greater than in the inactive control condition (g = 0.72 [0.10, 1.72]) and than in the swipe control condition (g = 0.64 [0.01, 1.41]). In sum, findings provide preliminary evidence for the feasibility, acceptability, and the therapeutic potential of the AAMT-DP intervention.

Gaitmap-An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking

Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987.

A content-based review of mobile health applications for breast cancer prevention and education: Characteristics, quality and functionality analysis

Objective

Mobile Health apps could be a feasible and effective tool to raise awareness for breast cancer prevention and to support women to change their behaviour to a healthier lifestyle. The aim of this study was to analyse the characteristics and quality of apps designed for breast cancer prevention and education.

Methods

We conducted a systematic search for apps covering breast cancer prevention topics in the Google Play and Apple App Store accessible from Germany using search terms either in German or in English. Only apps with a last update after June 2020 were included. The apps identified were downloaded and evaluated by two independent researchers. App quality was analysed using the Mobile Application Rating Scale (MARS). Associations of app characteristics and MARS rating were analysed.

Results

We identified 19 apps available in the Google Play Store and seven apps available in the Apple App Store that met all inclusion criteria. The mean MARS score was 3.07 and 3.50, respectively. Functionality was the highest-scoring domain. Operating system, developer (healthcare), download rates and time since the last update were significantly associated with overall MARS score. In addition, the presence of the following app functions significantly influenced MARS rating: breast self-examination tutorial, reminder for self-examination, documentation feature and education about breast cancer risk factors.

Conclusions

Although most of the apps offer important features for breast cancer prevention, none of the analysed apps combined all functions. The absence of healthcare professionals' expertise in developing apps negatively affects the overall quality.

Usability and Perception of a Wearable-Integrated Digital Maternity Record App in Germany: User Study

Background

Although digital maternity records (DMRs) have been evaluated in the past, no previous work investigated usability or acceptance through an observational usability study.

Objective

The primary objective was to assess the usability and perception of a DMR smartphone app for pregnant women. The secondary objective was to assess personal preferences and habits related to online information searching, wearable data presentation and interpretation, at-home examination, and sharing data for research purposes during pregnancy.

Methods

A DMR smartphone app was developed. Key features such as wearable device integration, study functionalities (eg, questionnaires), and common pregnancy app functionalities (eg, mood tracker) were included. Women who had previously given birth were invited to participate. Participants completed 10 tasks while asked to think aloud. Sessions were conducted via Zoom. Video, audio, and the shared screen were recorded for analysis. Task completion times, task success, errors, and self-reported (free text) feedback were evaluated. Usability was measured through the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). Semistructured interviews were conducted to explore the secondary objective.

Results

A total of 11 participants (mean age 34.6, SD 2.2 years) were included in the study. A mean SUS score of 79.09 (SD 18.38) was achieved. The app was rated "above average" in 4 of 6 UEQ categories. Sixteen unique features were requested. We found that 5 of 11 participants would only use wearables during pregnancy if requested to by their physician, while 10 of 11 stated they would share their data for research purposes.

Conclusions

Pregnant women rely on their medical caregivers for advice, including on the use of mobile and ubiquitous health technology. Clear benefits must be communicated if issuing wearable devices to pregnant women. Participants that experienced pregnancy complications in the past were overall more open toward the use of wearable devices in pregnancy. Pregnant women have different opinions regarding access to, interpretation of, and reactions to alerts based on wearable data. Future work should investigate personalized concepts covering these aspects.

Refinement and Usability Analysis of an eHealth App for Ankylosing Spondylitis as a Complementary Treatment to Physical Therapy: Development and Usability Study

BACKGROUND

Mobile eHealth apps have been used as a complementary treatment to increase the quality of life of patients and provide new opportunities for the management of rheumatic diseases. Telemedicine, particularly in the areas of prevention, diagnostics, and therapy, has become an essential cornerstone in the care of patients with rheumatic diseases.

OBJECTIVE

This study aims to improve the design and technology of YogiTherapy and evaluate its usability and quality.

METHODS

We newly implemented the mobile eHealth app YogiTherapy with a modern design, the option to change language, and easy navigation to improve the app's usability and quality for patients. After refinement, we evaluated the app by conducting a study with 16 patients with AS (4 female and 12 male; mean age 48.1, SD 16.8 y). We assessed the usability of YogiTherapy with a task performance test (TPT) with a think-aloud protocol and the quality with the German version of the Mobile App Rating Scale (MARS).

RESULTS

In the TPT, the participants had to solve 6 tasks that should be performed on the app. The overall task completion rate in the TPT was high (84/96, 88% completed tasks). Filtering for videos and navigating to perform an assessment test caused the largest issues during the TPT, while registering in the app and watching a yoga video were highly intuitive. Additionally, 12 (75%) of the 16 participants completed the German version of MARS. The quality of YogiTherapy was rated with an average MARS score of 3.79 (SD 0.51) from a maximum score of 5. Furthermore, results from the MARS questionnaire demonstrated a positive evaluation regarding functionality and aesthetics.

CONCLUSIONS

The refined and tested YogiTherapy app showed promising results among most participants. In the future, the app could serve its function as a complementary treatment for patients with AS. For this purpose, surveys with a larger number of patients should still be conducted. As a substantial advancement, we made the app free and openly available on the iOS App and Google Play stores.

Federated electronic health records for the European Health Data Space

The European Commission's draft for the European Health Data Space (EHDS) aims to empower citizens to access their personal health data and share it with physicians and other health-care providers. It further defines procedures for the secondary use of electronic health data for research and development. Although this planned legislation is undoubtedly a step in the right direction, implementation approaches could potentially result in centralised data silos that pose data privacy and security risks for individuals. To address this concern, we propose federated personal health data spaces, a novel architecture for storing, managing, and sharing personal electronic health records that puts citizens at the centre-both conceptually and technologically. The proposed architecture puts citizens in control by storing personal health data on a combination of personal devices rather than in centralised data silos. We describe how this federated architecture fits within the EHDS and can enable the same features as centralised systems while protecting the privacy of citizens. We further argue that increased privacy and control do not contradict the use of electronic health data for research and development. Instead, data sovereignty and transparency encourage active participation in studies and data sharing. This combination of privacy-by-design and transparent, privacy-preserving data sharing can enable health-care leaders to break the privacy-exploitation barrier, which currently limits the secondary use of health data in many cases.

Prevalence and course of pregnancy symptoms using self-reported pregnancy app symptom tracker data

During pregnancy, almost all women experience pregnancy-related symptoms. The relationship between symptoms and their association with pregnancy outcomes is not well understood. Many pregnancy apps allow pregnant women to track their symptoms. To date, the resulting data are primarily used from a commercial rather than a scientific perspective. In this work, we aim to examine symptom occurrence, course, and their correlation throughout pregnancy. Self-reported app data of a pregnancy symptom tracker is used. In this context, we present methods to handle noisy real-world app data from commercial applications to understand the trajectory of user and patient-reported data. We report real-world evidence from patient-reported outcomes that exceeds previous works: 1,549,186 tracked symptoms from 183,732 users of a smartphone pregnancy app symptom tracker are analyzed. The majority of users track symptoms on a single day. These data are generalizable to those users who use the tracker for at least 5 months. Week-by-week symptom report data are presented for each symptom. There are few or conflicting reports in the literature on the course of diarrhea, fatigue, headache, heartburn, and sleep problems. A peak in fatigue in the first trimester, a peak in headache reports around gestation week 15, and a steady increase in the reports of sleeping difficulty throughout pregnancy are found. Our work highlights the potential of secondary use of industry data. It reveals and clarifies several previously unknown or disputed symptom trajectories and relationships. Collaboration between academia and industry can help generate new scientific knowledge.

Evaluating the Effectiveness of Mobile Health in Breast Cancer Care: A Systematic Review

Breast cancer is affecting millions of people worldwide. If not appropriately handled, the side effects of different modalities of cancer treatment can negatively impact patients' quality of life and cause treatment interruptions. In recent years, mobile health (mHealth) interventions have shown promising opportunities to support breast cancer care. Numerous studies implemented mobile health interventions aiming to support patients with breast cancer, for example, through physical activity promotion or educational content. Nonetheless, current literature reveals that real-world evidence for the actual benefits remains unclear. In this systematic review, we focus on analyzing the methodology used in recent studies to determine the effects of mHealth applications and wearable devices on the outcome of patients with breast cancer. We followed the PRISMA guideline for the selection, analysis, and reporting of relevant studies found in the databases of Medline, Scopus, Web of Science, and Cochrane Library. A total of 276 unique records were identified, and 20 studies met the inclusion criteria. Study quality was assessed with the Effective Public Health Practice Project (EPHPP) Quality Assessment Tool for Quantitative Studies. While many of the studies used standardized questionnaires as patient-reported outcome measures, there was minimal use of objective measurements, such as activity sensors. Adoption, drop-out rates, and usage behavior of users of the mobile health intervention were often not reported. Future work should clearly define the focus and desired outcome of mHealth interventions and select outcome measures accordingly. Greater transparency facilitates the interpretation of results and conclusions about the real-world evidence of mobile health in breast cancer care.

Evaluating the feasibility and exploring the efficacy of an emotion-based approach-avoidance modification training (eAAMT) in the context of perceived stress in an adult sample - protocol of a parallel randomized controlled pilot study

BACKGROUND

Stress levels and thus the risk of developing related physical and mental health conditions are rising worldwide. Dysfunctional beliefs contribute to the development of stress. Potentially, such beliefs can be modified with approach-avoidance modification trainings (AAMT). As previous research indicates that effects of AAMTs are small, there is a need for innovative ways of increasing the efficacy of these interventions. For this purpose, we aim to evaluate the feasibility of the intervention and study design and explore the efficacy of an innovative emotion-based AAMT version (eAAMT) that uses the display of emotions to move stress-inducing beliefs away from and draw stress-reducing beliefs towards oneself.

METHODS

We will conduct a parallel randomized controlled pilot study at the Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany. Individuals with elevated stress levels will be randomized to one of eight study conditions (n = 10 per condition) - one of six variants of the eAAMT, an active control intervention (swipe-based AAMT), or an inactive control condition. Participants in the intervention groups will engage in four sessions of 20-30 min (e)AAMT training on consecutive days. Participants in the inactive control condition will complete the assessments via an online tool. Non-blinded assessments will be taken directly before and after the training and 1 week after training completion. The primary outcome will be perceived stress. Secondary outcomes will be dysfunctional beliefs, symptoms of depression, emotion regulation skills, and physiological stress measures. We will compute effect sizes and conduct mixed ANOVAs to explore differences in change in outcomes between the eAAMT and control conditions.

DISCUSSION

The study will provide valuable information to improve the intervention and study design. Moreover, if shown to be effective, the approach can be used as an automated smartphone-based intervention. Future research needs to identify target groups benefitting from this intervention utilized either as stand-alone treatment or an add-on intervention that is combined with other evidence-based treatments.

TRIAL REGISTRATION

The trial has been registered in the German Clinical Trials Register (Deutsches Register Klinischer Studien; DRKS00023007 ; September 7, 2020).

Association between cognition and gait in multiple sclerosis: A smartphone-based longitudinal analysis

BACKGROUND

Gait and cognition impairments are common problems among People with Multiple Sclerosis (PwMS). Previous studies have investigated cross-sectional associations between gait and cognition. However, there is a lack of evidence regarding the longitudinal association between these factors in PwMS. Therefore, the objective of this study was to explore this longitudinal relationship using smartphone-based data from the Floodlight study.

METHODS

Using the publicly available Floodlight dataset, which contains smartphone-based longitudinal data, we used a linear mixed model to investigate the longitudinal relationship between cognition, measured by the Symbol Digit Modalities Test (SDMT), and gait, measured by the 2 Minute Walking test (2 MW) step count and Five-U-Turn Test (FUTT) turning speed. Four mixed models were fitted to explore the association between: 1) SDMT and mean step count; 2) SDMT and variability of step count; 3) SDMT and mean FUTT turning speed; and 4) SDMT and variability of FUTT turningt speed.

RESULTS

After controlling for age, sex, weight, and height, there were significant correlations between SDMT and the variability of 2 MW step count, the mean of FUTT turning speed. No significant correlation was observed between SDMT and the 2 MW mean step count.

SIGNIFICANCE

Our findings support the evidence that gait and cognition are associated in PwMS. This may support clinicians to adjust treatment and intervention programs that address both gait and cognitive impairments.

Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning

BACKGROUND

Hereditary spastic paraplegias (HSPs) cause characteristic gait impairment leading to an increased risk of stumbling or even falling. Biomechanically, gait deficits are characterized by reduced ranges of motion in lower body joints, limiting foot clearance and ankle range of motion. To date, there is no standardized approach to continuously and objectively track the degree of dysfunction in foot elevation since established clinical rating scales require an experienced investigator and are considered to be rather subjective. Therefore, digital disease-specific biomarkers for foot elevation are needed.

METHODS

This study investigated the performance of machine learning classifiers for the automated detection and classification of reduced foot dorsiflexion and clearance using wearable sensors. Wearable inertial sensors were used to record gait patterns of 50 patients during standardized 4 [Formula: see text] 10 m walking tests at the hospital. Three movement disorder specialists independently annotated symptom severity. The majority vote of these annotations and the wearable sensor data were used to train and evaluate machine learning classifiers in a nested cross-validation scheme.

RESULTS

The results showed that automated detection of reduced range of motion and foot clearance was possible with an accuracy of 87%. This accuracy is in the range of individual annotators, reaching an average accuracy of 88% compared to the ground truth majority vote. For classifying symptom severity, the algorithm reached an accuracy of 74%.

CONCLUSION

Here, we show that the present wearable gait analysis system is able to objectively assess foot elevation patterns in HSP. Future studies will aim to improve the granularity for continuous tracking of disease severity and monitoring therapy response of HSP patients in a real-world environment.

Methods for integrating postural control into biomechanical human simulations: a systematic review

Understanding of the human body's internal processes to maintain balance is fundamental to simulate postural control behaviour. The body uses multiple sensory systems' information to obtain a reliable estimate about the current body state. This information is used to control the reactive behaviour to maintain balance. To predict a certain motion behaviour with knowledge of the muscle forces, forward dynamic simulations of biomechanical human models can be utilized. We aim to use predictive postural control simulations to give therapy recommendations to patients suffering from postural disorders in the future. It is important to know which types of modelling approaches already exist to apply such predictive forward dynamic simulations. Current literature provides different models that aim to simulate human postural control. We conducted a systematic literature research to identify the different approaches of postural control models. The different approaches are discussed regarding their applied biomechanical models, sensory representation, sensory integration, and control methods in standing and gait simulations. We searched on Scopus, Web of Science and PubMed using a search string, scanned 1253 records, and found 102 studies to be eligible for inclusion. The included studies use different ways for sensory representation and integration, although underlying neural processes still remain unclear. We found that for postural control optimal control methods like linear quadratic regulators and model predictive control methods are used less, when models' level of details is increasing, and nonlinearities become more important. Considering musculoskeletal models, reflex-based and PD controllers are mainly applied and show promising results, as they aim to create human-like motion behaviour considering physiological processes.

Fetal Re-Identification in Multiple Pregnancy Ultrasound Images Using Deep Learning

Ultrasound examinations during pregnancy can detect abnormal fetal development, which is a leading cause of perinatal mortality. In multiple pregnancies, the position of the fetuses may change between examinations. The individual fetus cannot be clearly identified. Fetal re-identification may improve diagnostic capabilities by tracing individual fetal changes. This work evaluates the feasibility of fetal re-identification on FETAL_PLANES_DB, a publicly available dataset of singleton pregnancy ultrasound images. Five dataset subsets with 6,491 images from 1,088 pregnant women and two re-identification frameworks (Torchreid, FastReID) are evaluated. FastReID achieves a mean average precision of 68.77% (68.42%) and mean precision at rank 10 score of 89.60% (95.55%) when trained on images showing the fetal brain (abdomen). Visualization with gradient-weighted class activation mapping shows that the classifiers appear to rely on anatomical features. We conclude that fetal re-identification in ultrasound images may be feasible. However, more work on additional datasets, including images from multiple pregnancies and several subsequent examinations, is required to ensure and investigate performance stability and explainability.Clinical relevance- To date, fetuses in multiple pregnancies cannot be distinguished between ultrasound examinations. This work provides the first evidence for feasibility of fetal re-identification in pregnancy ultrasound images. This may improve diagnostic capabilities in clinical practice in the future, such as longitudinal analysis of fetal changes or abnormalities.

WebPPG: Feasibility and Usability of Self-Performed, Browser-Based Smartphone Photoplethysmography

Smartphones enable and facilitate biomedical studies as they allow the recording of various biomedical signals, including photoplethysmograms (PPG). However, user engagement rates in mobile health studies are reduced when an application (app) needs to be installed. This could be alleviated by using installation-free web apps. We evaluate the feasibility of browser-based PPG recording, conducting the first usability study on smartphone-based PPG. We present an at-home study using a web app and library for PPG recording using the rear camera and flash. The underlying library is freely made available to researchers. 25 Android users participated, using their own smartphones. The study consisted of a demographic and anamnestic questionnaire, the signal recording itself (60 s), and a consecutive usability questionnaire. After filtering, heart rate was extracted (14/17 successful), signal-to-noise ratios assessed (0.64 ± 0.50 dB, mean ± standard deviation), and quality was visually inspected (12/17 usable for diagnosis). Recording was not supported in 9 cases. This was due to the browser's insufficient support for the flash light API. The app received a System Usability Scale score of 82 ± 9, which is above the 90th percentile. Overall, browser flash light support is the main limiting factor for broad device support. Thus, browser-based PPG is not yet widely applicable, although most participants feel comfortable with the recording itself. The utilization of the user-facing camera might represent a more promising approach. This study contributes to the development of low-barrier, user-friendly, installation-free smartphone signal acquisition. This enables profound, comprehensive data collection for research and clinical practice.Clinical relevance- WebPPG offers low-barrier remote diagnostic capabilities without the need for app installation.

Monitoring medication optimization in patients with Parkinson's disease

Medication optimization is a common component of the treatment strategy in patients with Parkinson's disease. As the disease progresses, it is essential to compensate for the movement deterioration in patients. Conventionally, examining motor deterioration and prescribing medication requires the patient's onsite presence in hospitals or practices. Home-monitoring technologies can remotely deliver essential information to physicians and help them devise a treatment decision according to the patient's need. Additionally, they help to observe the patient's response to these changes. In this regard, we conducted a longitudinal study to collect gait data of patients with Parkinson's disease while they received medication changes. Using logistic regression classifier, we could detect the annotated motor deterioration during medication optimization with an accuracy of 92%. Moreover, an in-depth examination of the best features illustrated a decline in gait speed and swing phase duration in the deterioration phases due to suboptimal medication.Clinical relevance- Our proposed gait analysis method in this study provides objective, detailed, and punctual information to physicians. Revealing clinically relevant time points related to the patient's need for medical adaption alleviates therapy optimization for physicians and reduces the duration of suboptimal treatment for patients. As the home-monitoring system acts remotely, embedding it in the medical care pathways could improve patients' quality of life.

Feasibility and Acceptance of Self-Guided Mobile Ultrasound among Pregnant Women in Routine Prenatal Care

BACKGROUND AND OBJECTIVES

Mobile and remote ultrasound devices are becoming increasingly available. The benefits and possible risks of self-guided ultrasound examinations conducted by pregnant women at home have not yet been well explored. This study investigated aspects of feasibility and acceptance, as well as the success rates of such examinations.

METHODS

In this prospective, single-center, interventional study, forty-six women with singleton pregnancies between 17 + 0 and 29 + 6 weeks of gestation were included in two cohorts, using two different mobile ultrasound systems. The participants examined the fetal heartbeat, fetal profile and amniotic fluid. Aspects of feasibility and acceptance were evaluated using a questionnaire. Success rates in relation to image and video quality were evaluated by healthcare professionals.

RESULTS

Two thirds of the women were able to imagine performing the self-guided examination at home, but 87.0% would prefer live support by a professional. Concerns about their own safety and that of the child were expressed by 23.9% of the women. Success rates for locating the target structure were 52.2% for videos of the fetal heartbeat, 52.2% for videos of the amniotic fluid in all four quadrants and 17.9% for videos of the fetal profile.

CONCLUSION

These results show wide acceptance of self-examination using mobile systems for fetal ultrasonography during pregnancy. Image quality was adequate for assessing the amniotic fluid and fetal heartbeat in most participants. Further studies are needed to determine whether ultrasound self-examinations can be implemented in prenatal care and how this would affect the fetomaternal outcome.

Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine

Artificial intelligence (AI) and machine learning (ML) methods are currently widely employed in medicine and healthcare. A PubMed search returns more than 100,000 articles on these topics published between 2018 and 2022 alone. Notwithstanding several recent reviews in various subfields of AI and ML in medicine, we have yet to see a comprehensive review around the methods' use in longitudinal analysis and prediction of an individual patient's health status within a personalized disease pathway. This review seeks to fill that gap. After an overview of the AI and ML methods employed in this field and of specific medical applications of models of this type, the review discusses the strengths and limitations of current studies and looks ahead to future strands of research in this field. We aim to enable interested readers to gain a detailed impression of the research currently available and accordingly plan future work around predictive models for deterioration in health status.

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions

Introduction: Accurately assessing people's gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72-4.87 steps/min, stride length 0.04-0.06 m, walking speed 0.03-0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.

An Integrated Framework for Data Quality Fusion in Embedded Sensor Systems

The advancement of embedded sensor systems allowed the monitoring of complex processes based on connected devices. As more and more data are produced by these sensor systems, and as the data are used in increasingly vital areas of applications, it is of growing importance to also track the data quality of these systems. We propose a framework to fuse sensor data streams and associated data quality attributes into a single meaningful and interpretable value that represents the current underlying data quality. Based on the definition of data quality attributes and metrics to determine real-valued figures representing the quality of the attributes, the fusion algorithms are engineered. Methods based on maximum likelihood estimation (MLE) and fuzzy logic are used to perform data quality fusion by utilizing domain knowledge and sensor measurements. Two data sets are used to verify the proposed fusion framework. First, the methods are applied to a proprietary data set targeting sample rate inaccuracies of a micro-electro-mechanical system (MEMS) accelerometer and second, to the publicly available Intel Lab Data set. The algorithms are verified against their expected behavior based on data exploration and correlation analysis. We prove that both fusion approaches are capable of detecting data quality issues and providing an interpretable data quality indicator.

CARWatch - A smartphone application for improving the accuracy of cortisol awakening response sampling

BACKGROUND

Many studies investigating the cortisol awakening response (CAR) suffer from low adherence to the study protocol as well as from the lack of precise and objective methods for assessing the awakening and saliva sampling times which leads to measurement bias on CAR quantification.

METHODS

To address this issue, we have developed "CARWatch", a smartphone application that aims to enable low-cost and objective assessment of saliva sampling times as well as to concurrently increase protocol adherence. As proof-of-concept study, we assessed the CAR of N = 117 healthy participants (24.2 ± 8.7 years, 79.5% female) on two consecutive days. During the study, we recorded awakening times (AW) using self-reports, the CARWatch application, and a wrist-worn sensor, and saliva sampling times (ST) using self-reports and the CARWatch application. Using combinations of different AW and ST modalities, we derived different reporting strategies and compared the reported time information to a Naive sampling strategy assuming an ideal sampling schedule. Additionally, we compared the AUC_I, computed using information from different reporting strategies, against each other to demonstrate the effect of inaccurate sampling on the CAR.

RESULTS

The use of CARWatch led to a more consistent sampling behavior and reduced sampling delay compared to self-reported saliva sampling times. Additionally, we observed that inaccurate saliva sampling times, as resulting from self-reports, were associated with an underestimation of CAR measures. Our findings also revealed potential error sources for inaccuracies in self-reported sampling times and showed that CARWatch can help in better identifying, and possibly excluding, sampling outliers that would remain undiscovered by self-reported sampling.

CONCLUSION

The results from our proof-of-concept study demonstrated that CARWatch can be used to objectively record saliva sampling times. Further, it suggests its potential of increasing protocol adherence and sampling accuracy in CAR studies and might help to reduce inconsistencies in CAR literature resulting from inaccurate saliva sampling. For that reason, we published CARWatch and all necessary tools under an open-source license, making it freely accessible to every researcher.

Change the direction: 3D optimal control simulation by directly tracking marker and ground reaction force data

Optimal control simulations of musculoskeletal models can be used to reconstruct motions measured with optical motion capture to estimate joint and muscle kinematics and kinetics. These simulations are mutually and dynamically consistent, in contrast to traditional inverse methods. Commonly, optimal control simulations are generated by tracking generalized coordinates in combination with ground reaction forces. The generalized coordinates are estimated from marker positions using, for example, inverse kinematics. Hence, inaccuracies in the estimated coordinates are tracked in the simulation. We developed an approach to reconstruct arbitrary motions, such as change of direction motions, using optimal control simulations of 3D full-body musculoskeletal models by directly tracking marker and ground reaction force data. For evaluation, we recorded three trials each of straight running, curved running, and a v-cut for 10 participants. We reconstructed the recordings with marker tracking simulations, coordinate tracking simulations, and inverse kinematics and dynamics. First, we analyzed the convergence of the simulations and found that the wall time increased three to four times when using marker tracking compared to coordinate tracking. Then, we compared the marker trajectories, ground reaction forces, pelvis translations, joint angles, and joint moments between the three reconstruction methods. Root mean squared deviations between measured and estimated marker positions were smallest for inverse kinematics (e.g., 7.6 ± 5.1 mm for v-cut). However, measurement noise and soft tissue artifacts are likely also tracked in inverse kinematics, meaning that this approach does not reflect a gold standard. Marker tracking simulations resulted in slightly higher root mean squared marker deviations (e.g., 9.5 ± 6.2 mm for v-cut) than inverse kinematics. In contrast, coordinate tracking resulted in deviations that were nearly twice as high (e.g., 16.8 ± 10.5 mm for v-cut). Joint angles from coordinate tracking followed the estimated joint angles from inverse kinematics more closely than marker tracking (e.g., root mean squared deviation of 1.4 ± 1.8 deg vs. 3.5 ± 4.0 deg for v-cut). However, we did not have a gold standard measurement of the joint angles, so it is unknown if this larger deviation means the solution is less accurate. In conclusion, we showed that optimal control simulations of change of direction running motions can be created by tracking marker and ground reaction force data. Marker tracking considerably improved marker accuracy compared to coordinate tracking. Therefore, we recommend reconstructing movements by directly tracking marker data in the optimal control simulation when precise marker tracking is required.

Exploring the interrelationship between the skin microbiome and skin volatiles: A pilot study

Unravelling the interplay between a human’s microbiome and physiology is a relevant task for understanding the principles underlying human health and disease. With regard to human chemical communication, it is of interest to elucidate the role of the microbiome in shaping or generating volatiles emitted from the human body. In this study, we characterized the microbiome and volatile organic compounds (VOCs) sampled from the neck and axilla of ten participants (five male, five female) on two sampling days, by applying different methodological approaches. Volatiles emitted from the respective skin site were collected for 20 min using textile sampling material and analyzed on two analytical columns with varying polarity of the stationary phase. Microbiome samples were analyzed by a culture approach coupled with MALDI-TOF-MS analysis and a 16S ribosomal RNA gene (16S RNA) sequencing approach. Statistical and advanced data analysis methods revealed that classification of body sites was possible by using VOC and microbiome data sets. Higher classification accuracy was achieved by combination of both data pools. Cutibacterium, Staphylococcus, Micrococcus, Streptococcus, Lawsonella, Anaerococcus, and Corynebacterium species were found to contribute to classification of the body sites by the microbiome. Alkanes, esters, ethers, ketones, aldehydes and cyclic structures were used by the classifier when VOC data were considered. The interdisciplinary methodological platform developed here will enable further investigations of skin microbiome and skin VOCs alterations in physiological and pathological conditions.

ADABase: A Multimodal Dataset for Cognitive Load Estimation

Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase (Autonomous Driving Cognitive Load Assessment Database) As a reference method to induce cognitive load onto subjects, we use the well-established n-back test, in addition to our novel simulator-based k-drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.

xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning

With tracking systems becoming more widespread in sports research and regular training and competitions, more data are available for sports analytics and performance prediction. We analyzed 2523 ski jumps from 205 athletes on five venues. For every jump, the dataset includes the 3D trajectory, 3D velocity, skis' orientation, and metadata such as wind, starting gate, and ski jumping hill data. Using this dataset, we aimed to predict the expected jump length (xLength) inspired by the expected goals metric in soccer (xG). We evaluate the performance of a fully connected neural network, a convolutional neural network (CNN), a long short-term memory (LSTM), and a ResNet architecture to estimate the xLength. For the prediction of the jump length one second after take-off, we achieve a mean absolute error (MAE) of 5.3 m for the generalization to new athletes and an MAE of 5.9 m for the generalization to new ski jumping hills using ResNet architectures. Additionally, we investigated the influence of the input time after the take-off on the predictions' accuracy. As expected, the MAE becomes smaller with longer inputs. Due to the real-time transmission of the sensor's data, xLength can be updated during the flight phase and used in live TV broadcasting. xLength could also be used as an analysis tool for experts to quantify the quality of the take-off and flight phases.

MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data

Developing machine learning algorithms for time-series data often requires manual annotation of the data. To do so, graphical user interfaces (GUIs) are an important component. Existing Python packages for annotation and analysis of time-series data have been developed without addressing adaptability, usability, and user experience. Therefore, we developed a generic open-source Python package focusing on adaptability, usability, and user experience. The developed package, Machine Learning and Data Analytics (MaD) GUI, enables developers to rapidly create a GUI for their specific use case. Furthermore, MaD GUI enables domain experts without programming knowledge to annotate time-series data and apply algorithms to it. We conducted a small-scale study with participants from three international universities to test the adaptability of MaD GUI by developers and to test the user interface by clinicians as representatives of domain experts. MaD GUI saves up to 75% of time in contrast to using a state-of-the-art package. In line with this, subjective ratings regarding usability and user experience show that MaD GUI is preferred over a state-of-the-art package by developers and clinicians. MaD GUI reduces the effort of developers in creating GUIs for time-series analysis and offers similar usability and user experience for clinicians as a state-of-the-art package.

Machine learning-based improvement of an online rheumatology referral and triage system

Introduction

Rheport is an online rheumatology referral system allowing automatic appointment triaging of new rheumatology patient referrals according to the respective probability of an inflammatory rheumatic disease (IRD). Previous research reported that Rheport was well accepted among IRD patients. Its accuracy was, however, limited, currently being based on an expert-based weighted sum score. This study aimed to evaluate whether machine learning (ML) models could improve this limited accuracy.

Materials and methods

Data from a national rheumatology registry (RHADAR) was used to train and test nine different ML models to correctly classify IRD patients. Diagnostic performance was compared of ML models and the current algorithm was compared using the area under the receiver operating curve (AUROC). Feature importance was investigated using shapley additive explanation (SHAP).

Results

A complete data set of 2265 patients was used to train and test ML models. 30.5% of patients were diagnosed with an IRD, 69.3% were female. The diagnostic accuracy of the current Rheport algorithm (AUROC of 0.534) could be improved with all ML models, (AUROC ranging between 0.630 and 0.737). Targeting a sensitivity of 90%, the logistic regression model could double current specificity (17% vs. 33%). Finger joint pain, inflammatory marker levels, psoriasis, symptom duration and female sex were the five most important features of the best performing logistic regression model for IRD classification.

Conclusion

In summary, ML could improve the accuracy of a currently used rheumatology online referral system. Including further laboratory parameters and enabling individual feature importance adaption could increase accuracy and lead to broader usage.

Surface-Free Multi-Stroke Trajectory Reconstruction and Word Recognition Using an IMU-Enhanced Digital Pen

Efficient handwriting trajectory reconstruction (TR) requires specific writing surfaces for detecting movements of digital pens. Although several motion-based solutions have been developed to remove the necessity of writing surfaces, most of them are based on classical sensor fusion methods limited, by sensor error accumulation over time, to tracing only single strokes. In this work, we present an approach to map the movements of an IMU-enhanced digital pen to relative displacement data. Training data is collected by means of a tablet. We propose several pre-processing and data-preparation methods to synchronize data between the pen and the tablet, which are of different sampling rates, and train a convolutional neural network (CNN) to reconstruct multiple strokes without the need of writing segmentation or post-processing correction of the predicted trajectory. The proposed system learns the relative displacement of the pen tip over time from the recorded raw sensor data, achieving a normalized error rate of 0.176 relative to unit-scaled tablet ground truth (GT) trajectory. To test the effectiveness of the approach, we train a neural network for character recognition from the reconstructed trajectories, which achieved a character error rate of 19.51%. Finally, a joint model is implemented that makes use of both the IMU data and the generated trajectories, which outperforms the sensor-only-based recognition approach by 0.75%.

A Generalized Framework for the Study of Spinal Motor Neurons Controlling the Human Hand During Dynamic Movements

The human hand possesses a large number of degrees of freedom. Hand dexterity is encoded by the discharge times of spinal motor units (MUs). Most of our knowledge on the neural control of movement is based on the discharge times of MUs during isometric contractions. Here we designed a noninvasive framework to study spinal motor neurons during dynamic hand movements with the aim to understand the neural control of MUs during sinusoidal hand digit flexion and extension at different rates of force development. The framework included 320 high-density surface EMG electrodes placed on the forearm muscles, with markerless 3D hand kinematics extracted with deep learning, and a realistic virtual hand that displayed the motor tasks. The movements included flexion and extension of individual hand digits at two different speeds (0.5 Hz and 1.5 Hz) for 40 seconds. We found on average 4.7±1.7 MUs across participants and tasks. Most MUs showed a biphasic pattern closely mirroring the flexion and extension kinematics. Indeed, a factor analysis method (non-negative matrix factorization) was able to learn the two components (flexion/extension) with high accuracy at the individual MU level ( R=0.87±0.12). Although most MUs were highly correlated with either flexion or extension movements, there was a smaller proportion of MUs that was not task-modulated and controlled by a different neural module (7.1% of all MUs with ). This work shows a noninvasive visually guided framework to study motor neurons controlling the movement of the hand in human participants during dynamic hand digit movements.

Real-World Stair Ambulation Characteristics Differ Between Prospective Fallers and Non-Fallers in Parkinson's Disease

Falls are among the leading causes of injuries or death for individuals from the age of 65 and the prevalence of falls is especially high for patients suffering from neurological diseases like Parkinson's disease (PD). Due to advancements in wearable sensor technology, inertial measurement units (IMUs) can be integrated unobtrusively into patients' everyday lives to monitor various mobility and gait parameters, which are related to common risk factors like reduced balance and reduced lower-limb muscle strength, or lower range of joints. Although stair ambulation is a fundamental part of our daily lives and is known for its unique challenges for the gait and balance system, long-term gait analysis studies have not investigated real-world stair ambulation parameters yet. Therefore, we applied a recently published gait analysis pipeline on real-world foot-worn IMU data of 40 PD patients over a recording period of two weeks to extract objective gait parameters from level walking but also from stair ascending and stair descending gait. In combination with fall records from a prospective three-month follow-up phase, we investigated group differences in gait parameters of future fallers compared to non-fallers for each individual gait activity. We found significant differences in stair ascending and descending parameters. Stance time was increased by up to 20% and gait speed reduced by up to 16% for fallers compared to non-fallers during stair walking. These differences were not present in level walking parameters. Hence, these results suggest that real-world stair ambulation provides sensitive parameters for mobility and fall risk due to the unique challenges stairs add to the balance and control system. Our work complements existing gait analysis studies by adding new insights into mobility and gait performance during real-world gait.

A Yoga Exercise App Designed for Patients With Axial Spondylarthritis: Development and User Experience Study

BACKGROUND

Besides anti-inflammatory medication, physical exercise represents a cornerstone of modern treatment for patients with axial spondyloarthritis (AS). Digital health apps (DHAs) such as the yoga app YogiTherapy could remotely empower patients to autonomously and correctly perform exercises.

OBJECTIVE

This study aimed to design and develop a smartphone-based app, YogiTherapy, for patients with AS. To gain additional insights into the usability of the graphical user interface (GUI) for further development of the app, this study focused exclusively on evaluating users' interaction with the GUI.

METHODS

The development of the app and the user experience study took place between October 2020 and March 2021. The DHA was designed by engineering students, rheumatologists, and patients with AS. After the initial development process, a pilot version of the app was evaluated by 5 patients and 5 rheumatologists. The participants had to interact with the app's GUI and complete 5 navigation tasks within the app. Subsequently, the completion rate and experience questionnaire (attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty) were completed by the patients.

RESULTS

The results of the posttest questionnaires showed that most patients were already familiar with digital apps (4/5, 80%). The task completion rates of the usability test were 100% (5/5) for the tasks T1 and T2, which included selecting and starting a yoga lesson and navigating to an information page. Rheumatologists indicated that they were even more experienced with digital devices (2/5, 40% experts; 3/5, 60% intermediates). In this case, they scored task completion rates of 100% (5/5) for all 5 usability tasks T1 to T5. The mean results from the User Experience Questionnaire range from -3 (most negative) to +3 (most positive). According to rheumatologists' evaluations, attractiveness (mean 2.267, SD 0.401) and stimulation (mean 2.250, SD 0.354) achieved the best mean results compared with dependability (mean 2.000, SD 0.395). Patients rated attractiveness at a mean of 2.167 (SD 0.565) and stimulation at a mean of 1.950 (SD 0.873). The lowest mean score was reported for perspicuity (mean 1.250, SD 1.425).

CONCLUSIONS

The newly developed and tested DHA YogiTherapy demonstrated moderate usability among rheumatologists and patients with rheumatic diseases. The app can be used by patients with AS as a complementary treatment. The initial evaluation of the GUI identified significant usability problems that need to be addressed before the start of a clinical evaluation. Prospective trials are also needed in the second step to prove the clinical benefits of the app.

Cross-sectional survey on researchers' experience in using accelerometers in health-related studies

Objectives

Accelerometers are widely applied in health studies, but lack of standardisation regarding device placement, sampling and data processing hampers comparability between studies. The objectives of this study were to assess how accelerometers are applied in health-related research and problems with accelerometer hardware and software encountered by researchers.

Methods

Researchers applying accelerometry in a health context were invited to a cross-sectional web-based survey (August 2020–September 2020). The questionnaire included quantitative questions regarding the application of accelerometers and qualitative questions on encountered hardware and software problems. Descriptive statistics were calculated for quantitative data and content analysis was applied to qualitative data.

Results

In total, 116 health researchers were included in the study (response: 13.7%). The most used brand was ActiGraph (67.2%). Independently of brand, the main reason for choosing a device was that it was the standard in the field (57.1%–83.3%). In children and adolescent populations, sampling frequency was higher (mean: 73.3 Hz ±29.9 Hz vs 47.6 Hz ±29.4 Hz) and epoch length (15.0s±15.6s vs 30.1s±25.9s) and non-wear time (42.9 min ±23.7 min vs 65.3 min ±35.4 min) were shorter compared with adult populations. Content analysis revealed eight categories of hardware problems (battery problems, compliance issues, data loss, mechanical problems, electronic problems, sensor problems, lacking waterproofness, other problems) and five categories of software problems (lack of user-friendliness, limited possibilities, bugs, high computational burden, black box character).

Conclusions

The study confirms heterogeneity regarding accelerometer use in health-related research. Moreover, several hardware and software problems were documented. Both aspects must be tackled to increase validity, practicability and comparability of research.

Stereopsis Only: Validation of a Monocular Depth Cues Reduced Gamified Virtual Reality with Reaction Time Measurement

The visual depth perception is composed of monocular and binocular depth cues. Studies show that in absence of binocular depth cues the performance of visuomotor tasks like pointing to or grasping objects is limited. Thus, binocular depth cues are of great importance for motor control required in everyday life. However, binocular depth cues like retinal disparity (basis for stereopsis) might be influenced due to developmental disorders of the visual system. For example, amblyopia in which one eye's visual input is not processed leads to loss of stereopsis. The primary amblyopia treatment is occlusion of the healthy eye to force the amblyopic eye to train. However, improvements in stereopsis are poor. Therefore, binocular treatments arose that equilibrate both eyes' visual input to enable binocular vision. However, most approaches rely on divided stimuli which do not account for loss of stereopsis. We created a Virtual Reality (VR) with reduced monocular depth cues in which a stereoscopic task is shown to both eyes simultaneously, consisting of two balls jumping towards the user. One ball appears closer to the user which must be identified. To evaluate the task performance the reaction time is measured. We validated our approach with 18 participants with stereopsis under three contrast settings including one leading to monocular vision. The number of correct responses reduces from 90% under binocular vision to 52% under monocular vision corresponding to random guessing. Our results indicate that it is possible to disable monocular depth cues and create a dynamic stereoscopic task inside a VR.

Framework for Simultaneous Indoor Localization, Mapping, and Human Activity Recognition in Ambient Assisted Living Scenarios

Indoor localization and human activity recognition are two important sources of information to provide context-based assistance. This information is relevant in ambient assisted living (AAL) scenarios, where older adults usually need supervision and assistance in their daily activities. However, indoor localization and human activity recognition have been mostly considered isolated problems. This work presents and evaluates a framework that takes advantage of the relationship between location and activity to simultaneously perform indoor localization, mapping, and human activity recognition. The proposed framework provides a non-intrusive configuration, which fuses data from an inertial measurement unit (IMU) placed in the person's shoe, with proximity and human activity-related data from Bluetooth low energy beacons (BLE) deployed in the indoor environment. A variant of the simultaneous location and mapping (SLAM) framework was used to fuse the location and human activity recognition (HAR) data. HAR was performed using data streaming algorithms. The framework was evaluated in a pilot study, using data from 22 people, 11 young people, and 11 older adults (people aged 65 years or older). As a result, seven activities of daily living were recognized with an F1 score of 88%, and the in-door location error was 0.98 ± 0.36 m for the young and 1.02 ± 0.24 m for the older adults. Furthermore, there were no significant differences between the groups, indicating that our proposed method works adequately in broad age ranges.

Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study

Background

Fitness trackers and smart watches are frequently used to collect data in longitudinal medical studies. They allow continuous recording in real-life settings, potentially revealing previously uncaptured variabilities of biophysiological parameters and diseases. Adequate device accuracy is a prerequisite for meaningful research.

Objective

This study aims to assess the heart rate recording accuracy in two previously unvalidated devices: Fitbit Charge 4 and Samsung Galaxy Watch Active2.

Methods

Participants performed a study protocol comprising 5 resting and sedentary, 2 low-intensity, and 3 high-intensity exercise phases, lasting an average of 19 minutes 27 seconds. Participants wore two wearables simultaneously during all activities: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Reference heart rate data were recorded using a medically certified Holter electrocardiogram. The data of the reference and evaluated devices were synchronized and compared at 1-second intervals. The mean, mean absolute error, mean absolute percentage error, Lin concordance correlation coefficient, Pearson correlation coefficient, and Bland-Altman plots were analyzed.

Results

A total of 23 healthy adults (mean age 24.2, SD 4.6 years) participated in our study. Overall, and across all activities, the Fitbit Charge 4 slightly underestimated the heart rate, whereas the Samsung Galaxy Watch Active2 overestimated it (−1.66 beats per minute [bpm]/3.84 bpm). The Fitbit Charge 4 achieved a lower mean absolute error during resting and sedentary activities (seated rest: 7.8 vs 9.4; typing: 8.1 vs 11.6; laying down [left]: 7.2 vs 9.4; laying down [back]: 6.0 vs 8.6; and walking slowly: 6.8 vs 7.7 bpm), whereas the Samsung Galaxy Watch Active2 performed better during and after low- and high-intensity activities (standing up: 12.3 vs 9.0; walking fast: 6.1 vs 5.8; stairs: 8.8 vs 6.9; squats: 15.7 vs 6.1; resting: 9.6 vs 5.6 bpm).

Conclusions

Device accuracy varied with activity. Overall, both devices achieved a mean absolute percentage error of just <10%. Thus, they were considered to produce valid results based on the limits established by previous work in the field. Neither device reached sufficient accuracy during seated rest or keyboard typing. Thus, both devices may be eligible for use in respective studies; however, researchers should consider their individual study requirements.

Smartphone-Based Colorimetric Analysis of Urine Test Strips for At-Home Prenatal Care

Objective: Clinical urine tests are a key component of prenatal care. As of now, urine test strips are evaluated through a time consuming, often error-prone and operator-dependent visual color comparison of test strips and reference cards by medical staff. Methods and procedures: This work presents an automated pipeline for urinalysis with urine test strips using smartphone camera images in home environments, combining several image processing and color combination techniques. Our approach is applicable to off-the-shelf test strips in home conditions with no additional hardware required. For development and evaluation of our pipeline we collected image data from two sources: i) A user study (26 participants, 150 images) and ii) a lab study (135 images). Results: We trained a region-based convolutional neural network that is able to detect the urine test strip location and orientation in images with a wide variety of light conditions, backgrounds and perspectives with an accuracy of 85.5%. The reference card can be robustly detected through a feature matching approach in 98.6% of the images. Color comparison by Hue channel (0.81 F1-Score), Matching factor (0.80 F1-Score) and Euclidean distance (0.70 F1-Score) were evaluated to determine the urinalysis results. Conclusion: We show that an automated smartphone-based colorimetric analysis of urine test strips in a home environment is feasible. It facilitates examinations and provides the possibility to shift care into an at-home environment. Clinical impact: The findings demonstrate that routine urine examinations can be transferred into the home environment using a smartphone. Simultaneously, human error is avoided, accuracy is increased and medical staff is relieved.

Fall Risk Prediction in Parkinson's Disease Using Real-World Inertial Sensor Gait Data

Falls are an eminent risk for older adults and especially patients with neurodegenerative disorders, such as Parkinson's disease (PD). Recent advancements in wearable sensor technology and machine learning may provide a possibility for an individualized prediction of fall risk based on gait recordings from standardized gait tests or from unconstrained real-world scenarios. However, the most effective aggregation of continuous real-world data as well as the potential of unsupervised gait tests recorded over multiple days for fall risk prediction still need to be investigated. Therefore, we present a data set containing real-world gait and unsupervised 4x10-Meter-Walking-Tests of 40 PD patients, continuously recorded with foot-worn inertial sensors over a period of two weeks. In this prospective study, falls were self-reported during a three-month follow-up phase, serving as ground truth for fall risk prediction. The purpose of this study was to compare different data aggregation approaches and machine learning models for the prospective prediction of fall risk using gait parameters derived either from continuous real-world recordings or from unsupervised gait tests. The highest balanced accuracy of 74.0% (sensitivity: 60.0%, specificity: 88.0%) was achieved with a Random Forest Classifier applied to the real-world gait data when aggregating all walking bouts and days of each participant. Our findings suggest that fall risk can be predicted best by merging the entire two-week real-world gait data of a patient, outperforming the prediction using unsupervised gait tests (68.0% balanced accuracy) and contribute to an improved understanding of fall risk prediction.

Do We Walk Differently at Home? A Context-Aware Gait Analysis System in Continuous Real-World Environments

Driven by the advancements of wearable sensors and signal processing algorithms, studies on continuous real-world monitoring are of major interest in the field of clinical gait and motion analysis. While real-world studies enable a more detailed and realistic insight into various mobility parameters such as walking speed, confounding and environmental factors might skew those digital mobility outcomes (DMOs), making the interpretation of results challenging. To consider confounding factors, context information needs to be included in the analysis. In this work, we present a context-aware mobile gait analysis system that can distinguish between gait recorded at home and not at home based on Bluetooth proximity information. The system was evaluated on 9 healthy subjects and 6 Parkinsons disease (PD) patients. The classification of the at home/not at home context reached an average F1-score of 98.2 ± 3.2 %. A context-aware analysis of gait parameters revealed different walking bout length distributions between the two environmental conditions. Furthermore, a reduction of gait speed within the at home context compared to walking not at home of 8.9 ± 9.4 % and 8.7 ±5.9 % on average for healthy and PD subjects was found, respectively. Our results indicate the influence of the recording environment on DMOs and, therefore, emphasize the importance of context in the analysis of continuous motion data. Hence, the presented work contributes to a better understanding of confounding factors for future real-world studies.

Evaluation of Orthostatic Reactions in Real-World Environments Using Wearable Sensors

As global life expectancy is constantly rising, the early detection of age-related, neurodegenerative diseases, such as Parkinson's disease, is becoming increasingly important. Patients suffering from Parkinson's disease often show autonomic nervous system dysfunction which is why its examination is an important diagnostic tool. Measuring the response of the heart rate (variability) to postural transitions and thereby assessing the orthostatic reaction is a common indicator of autonomic nervous system functioning. However, since these measurements are commonly performed in a clinical environment, results can be impaired by the white coat effect. To reduce this influence as well as inter- and intra-day variations, our work aims to investigate the assessment of orthostatic reactions in free-living environments. We collected IMU and ECG data of seven healthy participants over four days and evaluated differences in orthostatic reactions between standardized tests at lab, at home, as well as unsupervised recordings during real-world conditions. Except for the first lab recording, we detected significant changes in heart rate due to postural transitions in all recording settings, with the strongest response occurring during standardized tests at home. Our findings show that real-world assessment of orthostatic reactions is possible and provides comparable results to supervised assessments in lab settings. Additionally, our results indicate high inter- and intra-day variability which motivates the continuous orthostatic reaction measurement over the span of multiple days. We are convinced that our presented approach provides a first step towards unobtrusive assessment of orthostatic reactions in real-world environments, which might enable a more reliable early detection of disorders of the autonomic nervous system.

Machine learning-based distinction of left and right foot contacts in lower back inertial sensor gait data

Digital gait measures derived from wearable inertial sensors have been shown to support the treatment of patients with motor impairments. From a technical perspective, the detection of left and right initial foot contacts (ICs) is essential for the computation of stride-by-stride outcome measures including gait asymmetry. However, in a majority of studies only one sensor close to the center of mass is used, complicating the assignment of detected ICs to the respective foot. Therefore, we developed an algorithm including supervised machine learning (ML) models for the robust classification of left and right ICs using multiple features from the gyroscope located at the lower back. The approach was tested on a data set including 40 participants (ten healthy controls, ten hemiparetic, ten Parkinson's disease, and ten Huntington's disease patients) and reached an accuracy of 96.3% for the overall data set and up to 100.0% for the Parkinson's sub data set. These results were compared to a state-of-the-art algorithm. The ML approaches outperformed this traditional algorithm in all subgroups. Our study contributes to an improved classification of left and right ICs in inertial sensor signals recorded at the lower back and thus enables a reliable computation of clinically relevant mobility measures.

Rigid and Non-rigid Motion Compensation in Weight-bearing CBCT of the Knee using Simulated Inertial Measurements

OBJECTIVE

Involuntary subject motion is the main source of artifacts in weight-bearing cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the motion needs to be compensated. We propose to use inertial measurement units (IMUs) attached to the leg for motion estimation.

METHODS

We perform a simulation study using real motion recorded with an optical tracking system. Three IMU-based correction approaches are evaluated, namely rigid motion correction, non-rigid 2D projection deformation and non-rigid 3D dynamic reconstruction. We present an initialization process based on the system geometry. With an IMU noise simulation, we investigate the applicability of the proposed methods in real applications.

RESULTS

All proposed IMU-based approaches correct motion at least as good as a state-of-the-art marker-based approach. The structural similarity index and the root mean squared error between motion-free and motion corrected volumes are improved by 24-35% and 78-85%, respectively, compared with the uncorrected case. The noise analysis shows that the noise levels of commercially available IMUs need to be improved by a factor of 10⁵ which is currently only achieved by specialized hardware not robust enough for the application.

CONCLUSION

Our simulation study confirms the feasibility of this novel approach and defines improvements necessary for a real application.

SIGNIFICANCE

The presented work lays the foundation for IMU-based motion compensation in cone-beam CT of the knee and creates valuable insights for future developments.

A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human-Robot Interaction

Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add safety measures to human-robot interaction applications. With the advancement of machine learning methods in resource-constrained environments, smart sensor systems have become increasingly powerful. As more data-driven approaches are deployed on the sensors, it is of growing importance to monitor data quality at all times of system operation. We introduce a smart capacitive sensor system with an embedded data quality monitoring algorithm to enhance the safety of human-robot interaction scenarios. The smart capacitive skin sensor is capable of detecting the distance and angle of objects nearby by utilizing consumer-grade sensor electronics. To further acknowledge the safety aspect of the sensor, a dedicated layer to monitor data quality in real-time is added to the embedded software of the sensor. Two learning algorithms are used to implement the sensor functionality: (1) a fully connected neural network to infer the position and angle of objects nearby and (2) a one-class SVM to account for the data quality assessment based on out-of-distribution detection. We show that the sensor performs well under normal operating conditions within a range of 200 mm and also detects abnormal operating conditions in terms of poor data quality successfully. A mean absolute distance error of 11.6mm was achieved without data quality indication. The overall performance of the sensor system could be further improved to 7.5mm by monitoring the data quality, adding an additional layer of safety for human-robot interaction.

Detection of Unsupervised Standardized Gait Tests From Real-World Inertial Sensor Data in Parkinson's Disease

Gait tests as part of home monitoring study protocols for patients with movement disorders may provide valuable standardized anchor-points for real-world gait analysis using inertial measurement units (IMUs). However, analyzing unsupervised gait tests relies on reliable test annotations by the patients requiring a potentially error-prone interaction with the recording system. To overcome this limitation, this work presents a novel algorithmic pipeline for the automated detection of unsupervised standardized gait tests from continuous real-world IMU data. In a study with twelve Parkinson's disease patients, we recorded real-world gait data over two weeks using foot-worn IMUs. During continuous daily recordings, the participants performed series of three consecutive 4×10 -Meters-Walking-Tests ( 4×10 MWTs) at different walking speeds, besides their usual daily-living activities. The algorithm first detected these gait test series using a gait sequence detection algorithm, a peak enhancement pipeline, and subsequence Dynamic Time Warping and then decomposed them into single 4×10 MWTs based on the walking speed. In the evaluation with 419 available gait test series, the detection reached an F1-score of 88.9% and the decomposition an F1-score of 94.0%. A concurrent validity evaluation revealed very good agreement between spatio-temporal gait parameters derived from manually labelled and automatically detected 4×10 MWTs. Our algorithm allows to remove the burden of system interaction from the patients and reduces the time for manual data annotation for researchers. The study contributes to an improved automated processing of real-world IMU gait data and enables a simple integration of standardized tests into continuous long-term recordings. This will help to bridge the gap between supervised and unsupervised gait assessment.

Consensus based framework for digital mobility monitoring

Digital mobility assessment using wearable sensor systems has the potential to capture walking performance in a patient's natural environment. It enables monitoring of health status and disease progression and evaluation of interventions in real-world situations. In contrast to laboratory settings, real-world walking occurs in non-conventional environments and under unconstrained and uncontrolled conditions. Despite the general understanding, there is a lack of agreed definitions about what constitutes real-world walking, impeding the comparison and interpretation of the acquired data across systems and studies. The goal of this study was to obtain expert-based consensus on specific aspects of real-world walking and to provide respective definitions in a common terminological framework. An adapted Delphi method was used to obtain agreed definitions related to real-world walking. In an online survey, 162 participants from a panel of academic, clinical and industrial experts with experience in the field of gait analysis were asked for agreement on previously specified definitions. Descriptive statistics was used to evaluate whether consent (> 75% agreement as defined a priori) was reached. Of 162 experts invited to participate, 51 completed all rounds (31.5% response rate). We obtained consensus on all definitions ("Walking" > 90%, "Purposeful" > 75%, "Real-world" > 90%, "Walking bout" > 80%, "Walking speed" > 75%, "Turning" > 90% agreement) after two rounds. The identification of a consented set of real-world walking definitions has important implications for the development of assessment and analysis protocols, as well as for the reporting and comparison of digital mobility outcomes across studies and systems. The definitions will serve as a common framework for implementing digital and mobile technologies for gait assessment and are an important link for the transition from supervised to unsupervised gait assessment.