Review of Wearable Devices and Data Collection Considerations for Connected Health

Distinguishing severe sleep apnea from habitual snoring using a neck-wearable piezoelectric sensor and deep learning: A pilot study

This study explores the development of a deep learning model using a neck-wearable piezoelectric sensor to accurately distinguish severe sleep apnea syndrome (SAS) from habitual snoring, addressing the underdiagnosis of SAS in adults. From 2018 to 2020, 60 adult habitual snorers underwent polysomnography while wearing a neck piezoelectric sensor that recorded snoring vibrations (70-250 Hz) and carotid artery pulsations (0.01-1.5 Hz). The initial dataset comprised 1167 silence, 1304 snoring, and 399 noise samples from 20 participants. Using a hybrid deep learning model comprising a one-dimensional convolutional neural network and gated-recurrent unit, the model identified snoring and apnea/hypopnea events, with sleep phases detected via pulse wave variability criteria. The model's efficacy in predicting severe SAS was assessed in the remaining 40 participants, achieving snoring detection rates of 0.88, 0.86, and 0.92, with respective loss rates of 0.39, 0.90, and 0.23. Classification accuracy for severe SAS improved from 0.85 for total sleep time to 0.90 for partial sleep time, excluding the first sleep phase, demonstrating precision of 0.84, recall of 1.00, and an F1 score of 0.91. This innovative approach of combining a hybrid deep learning model with a neck-wearable piezoelectric sensor suggests a promising route for early and precise differentiation of severe SAS from habitual snoring, aiding guiding further standard diagnostic evaluations and timely patient management. Future studies should focus on expanding the sample size, diversifying the patient population, and external validations in real-world settings to enhance the robustness and applicability of the findings.

The Interplay Between Sleep and Safety Outcomes in the Workplace: A Scoping Review and Bibliographic Analysis of the Literature

Occupational incidents comprising injuries and accidents remain a serious concern globally. With sleep deprivation and fatigue representing key drivers of many workplace incidents, one strategy to reduce occupational incidents is implementing effective sleep management systems. Yet, to date, there are complaints about the methodological approach in sleep-safety studies. The extent of work carried out with respect to the impact of sleep on safety outcomes needs to be reviewed to highlight the state of the art in the face of increasing technological advancement and changing lifestyle attitudes. A systematic search of the Scopus and PubMed databases retrieved 63 journal articles published up to 2023. The units of analysis included article performance and thematic analysis. It was deduced that workers in healthcare and construction have been the focus of most studies, pointing to the prevalence of safety issues in both these sectors. Most of the studies adopted a quantitative methodology employing validated sleep questionnaires, especially the Pittsburgh Sleep Quality Index. Using thematic analysis, the research focus was mapped into six areas, including sleep disorders, cognition and performance, and injury and accident prevention in the construction sector. In objective studies, alertness and cognitive performance were considered a proxy for sleep deprivation and safety performance. Harmonising sleep questionnaires is necessary to prevent excessive paperwork and ineffective safety systems. This study has the potential to provide occupational health and safety researchers outside of the medicine and psychology disciplines with knowledge on baseline information that could advance efforts to address sleep deprivation and the resulting safety concerns in the workplace.

Personalised Risk Modelling for Older Adult Cancer Survivors: Combining Wearable Data and Self-Reported Measures to Address Time-Varying Risks

Recent advancements in wearable devices have significantly enhanced remote patient monitoring, enabling healthcare professionals to evaluate conditions within home settings. While electronic health records (EHRs) offer extensive clinical data, they often lack crucial contextual information about patients' daily lives and symptoms. By integrating continuous self-reported outcomes related to vulnerability, anxiety, and depression from older adult cancer survivors with objective data from wearables, we can develop personalised risk models that address time-varying risk factors in cancer care. Our study combines real-world data from wearable devices with self-reported information, employing process mining techniques to analyse dynamic risk models for vulnerability and anxiety. Unlike traditional static assessments, this approach recognises that risk factors evolve. Collaborating with healthcare professionals, we analysed data from the LifeChamps study to create two dynamic risk models. This collaborative effort revealed how activity and sleep patterns influence self-reported vulnerability and anxiety among participants. It underscored the potential of wearable sensors and artificial intelligence techniques for deeper analysis and understanding, making us all part of a larger effort in cancer care. Overall, patients with prolonged sedentary activity had a higher risk of vulnerability, while those with highly dynamic sleep patterns were more likely to report anxiety and depression. Prostate-metastatic patients showed an increased risk of vulnerability compared to other cancer types.

Evaluating Heat Stress in Occupational Setting with No Established Safety Standards Using Collective Data from Wearable Biosensors

In recent years, living and occupational environments have been increasingly exposed to extreme heat. While the risk of heatstroke rises with greater heat stress, conventional knowledge and safety standards may no longer adequately assess heat stress under such extreme conditions. To address this issue, we propose a method for evaluating heat stress using collective data from wearable biosensors that monitor heart rate and physical activity in a group of workers. The novelty of this approach lies in utilizing collective data from wearable biosensors to assess environmental heat stress rather than individual health status. To quantify heat stress in specific environments or conditions, we introduce the heart rate response intercept, defined as the heart rate at 1 MET when the heart rate response to physical activity is approximated linearly. Using this heat stress index, we examined the effects of ambient temperature, aging, and obesity on heat stress. Our findings indicate that heat stress among obese workers was significantly high and should not be overlooked. Furthermore, because this method can quantify the effectiveness of heatstroke countermeasures, it serves as a valuable tool for improving occupational environments.

A Sensor-Based Classification for Neuromotor Robot-Assisted Rehabilitation

Neurological diseases leading to motor deficits constitute significant challenges to healthcare systems. Despite technological advancements in data acquisition, sensor development, data processing, and virtual reality (VR), a suitable framework for patient-centered neuromotor robot-assisted rehabilitation using collective sensor information does not exist. An extensive literature review was achieved based on 124 scientific publications regarding different types of sensors and the usage of the bio-signals they measure for neuromotor robot-assisted rehabilitation. A comprehensive classification of sensors was proposed, distinguishing between specific and non-specific parameters. The classification criteria address essential factors such as the type of sensors, the data they measure, their usability, ergonomics, and their overall impact on personalized treatment. In addition, a framework designed to collect and utilize relevant data for the optimal rehabilitation process efficiently is proposed. The proposed classifications aim to identify a set of key variables that can be used as a building block for a dynamic framework tailored for personalized treatments, thereby enhancing the effectiveness of patient-centered procedures in rehabilitation.

Harnessing Thermoelectric Power in Self-Healing Wearables: A Review

Wearable thermoelectric generators are sustainable devices that generate electricity from body heat to provide a continuous power supply for electronic devices. In healthcare, they are particularly valuable for powering wireless devices that transmit vital health signals, where maintaining an uninterrupted power source is a significant challenge. However, these generators are prone to failure over time or due to mechanical damage caused by mechanical stress or environmental factors, which can lead to the loss of critical healthcare data. To address these issues, the integration of self-healing capabilities alongside flexibility and longevity is essential for their reliable operation. To our knowledge, this review is one of the first to look in depth at self-healing materials specifically designed for wearable thermoelectric generators. It explores the latest innovations and applications in this field highlighting how these materials can improve the reliability and lifetime of such systems.

Validation of a novel mask-based device for monitoring of comprehensive sleep parameters and sleep disordered breathing

PURPOSE

This study aimed to validate the new DormoTech Vlab device's performance, usability, and validity as a sleep test and physiological data recorder. The novel device has been designed for patient comfort, ease of use, and home-based assessment of sleep disordered breathing and other sleep-related measurements.

METHODS

Forty-seven adults (mean age = 52 years, 42% female, body mass index 29.4 kg/m2) underwent simultaneous testing with the DormoTech Vlab device and routine full polysomnography (PSG) using the Nox A1 system (K192469, Nox Medical). The sleep studies were manually and independently scored according to recommended guidelines. The primary outcome measure was the apnea-hypopnea index (AHI) and its corresponding conventional severity level (i.e., normal, mild, moderate, severe). Secondary endpoints included other standard PSG parameters.

RESULTS

The AHI was 21.7 ± 24.2 events/h (mean ± standard deviation) using the Vlab device versus 21.5 ± 23.9 events/h for gold standard PSG Nox A1 (p = 0.7). When AHI was grouped by severity, inter-test agreement was high (Cohen's kappa = 0.97). Results between the two systems were largely similar in the secondary endpoints, with high correlation between the two systems, and statistically significant (p < 0.05) differences only in REM latency measurements. The Vlab device provides similar sleep study data to conventional gold standard PSG and clinically near-identical test interpretation in almost all cases.

CONCLUSION

Based on these results, the Vlab device can be considered substantially equivalent to the reference Nox A1 system in terms of usability, efficacy, and validity.

CLINICAL TRIAL REGISTRATION

Trial name: Evaluation of the Usability and Performance Assessment of the DormoTech VLAB Device as a Home Sleep Test Identification number: NCT06224972. Date of Registration: 2023-12-06.

Participant Contributions to Person-Generated Health Data Research Using Mobile Devices: Scoping Review

BACKGROUND

Mobile devices offer an emerging opportunity for research participants to contribute person-generated health data (PGHD). There is little guidance, however, on how to best report findings from studies leveraging those data. Thus, there is a need to characterize current reporting practices so as to better understand the potential implications for producing reproducible results.

OBJECTIVE

The primary objective of this scoping review was to characterize publications' reporting practices for research that collects PGHD using mobile devices.

METHODS

We comprehensively searched PubMed and screened the results. Qualifying publications were classified according to 6 dimensions-1 covering key bibliographic details (for all articles) and 5 covering reporting criteria considered necessary for reproducible and responsible research (ie, "participant," "data," "device," "study," and "ethics," for original research). For each of the 5 reporting dimensions, we also assessed reporting completeness.

RESULTS

Out of 3602 publications screened, 100 were included in this review. We observed a rapid increase in all publications from 2016 to 2021, with the largest contribution from US authors, with 1 exception, review articles. Few original research publications used crowdsourcing platforms (7%, 3/45). Among the original research publications that reported device ownership, most (75%, 21/28) reported using participant-owned devices for data collection (ie, a Bring-Your-Own-Device [BYOD] strategy). A significant deficiency in reporting completeness was observed for the "data" and "ethics" dimensions (5 reporting factors were missing in over half of the research publications). Reporting completeness for data ownership and participants' access to data after contribution worsened over time.

CONCLUSIONS

Our work depicts the reporting practices in publications about research involving PGHD from mobile devices. We found that very few papers reported crowdsourcing platforms for data collection. BYOD strategies are increasingly popular; this creates an opportunity for improved mechanisms to transfer data from device owners to researchers on crowdsourcing platforms. Given substantial reporting deficiencies, we recommend reaching a consensus on best practices for research collecting PGHD from mobile devices. Drawing from the 5 reporting dimensions in this scoping review, we share our recommendations and justifications for 9 items. These items require improved reporting to enhance data representativeness and quality and empower participants.

Temperature-Responsive Hybrid Composite with Zero Temperature Coefficient of Resistance for Wearable Thermotherapy Pads

Carbon-based polymer composites are widely used in wearable devices due to their exceptional electrical conductivity and flexibility. However, their temperature-dependent resistance variations pose significant challenges to device safety and performance. A negative temperature coefficient (NTC) can lead to overcurrent risks, while a positive temperature coefficient (PTC) compromises accuracy. In this study, we present a novel hybrid composite combining carbon nanotubes (CNTs) with NTC properties and carbon black (CB) with PTC properties to achieve a near-zero temperature coefficient of resistance (TCR) at an optimal ratio. This innovation enhances the safety and reliability of carbon-based polymer composites for wearable heating applications. Furthermore, a thermochromic pigment layer is integrated into the hybrid composite, enabling visual temperature indication across three distinct zones. This bilayer structure not only addresses the TCR challenge but also provides real-time, user-friendly temperature monitoring. The resulting composite demonstrates consistent performance and high precision under diverse heating conditions, making it ideal for wearable thermotherapy pads. This study highlights a significant advancement in developing multifunctional, temperature-responsive materials, offering a promising solution for safer and more controllable wearable devices.

Developing a Health Support System to Promote Care for the Elderly

In light of the demographic shift towards an aging population, there is an increasing prevalence of dementia among the elderly. The negative impact on mental health is preventing individuals from taking proper care of themselves. For individuals requiring hospital care, those receiving home care, or as a precaution for a specific individual, it is advantageous to utilize monitoring equipment to track their biological parameters on an ongoing basis. This equipment can minimize the risk of serious accidents or severe health hazards. The objective of the present research project is to design an armband with an accurate location tracking system. This is of particular importance for individuals with dementia and Alzheimer's disease, who frequently leave their homes and are unable to find their way back. The proposed armband also includes a fingerprint identification system that allows only authorized personnel to use it. Furthermore, in hospitals and healthcare facilities the biometric identification system can be used to trace periodic medical or nursing visits. This process improves the reliability and transparency of healthcare. The test results indicate that the armband functions in accordance with the desired design specifications, with performance evaluation of the main features including fall detection, where a hit rate of 100% was obtained, a fingerprint recognition test demonstrating accuracy from 88% to 100% on high-quality samples, and a GPS tracking test determining position with a difference of between 1.8 and 2.1 m. The proposed solution may be of benefit to healthcare professionals, supported housing providers, elderly people as target users, or their family members.

Wearable Technology in Mobility and Falls Health Care: Finding Consensus on Their Clinical Utility and Identifying a Roadmap to Actual Use

BACKGROUND

Despite the promise wearable technology offers through detailed insight into mobility and fall risk, timely identification of high risk, assessment of risk severity, evaluation of clinical interventions, and potential to redefine the assessment of behaviours which influence health, they are not routinely used in clinical practice.

OBJECTIVE

Establish consensus on how wearable technology can be applied to support clinical care for people aged 50 and over experiencing changes to mobility and/or who are at increased risk of falling.

METHODS

A Delphi study was conducted among 17 hospital-based health professionals. Over three rounds, experts were asked about fall prevention, mobility assessment, the potential role of wearable sensors, and clinical considerations for implementing wearable technology into practice. Consensus was defined as 75% agreement. Data were analysed using qualitative and quantitative methods.

RESULTS

Experts found that wearable technology has short and long-term clinical utility, data should be shared with general practitioners to improve long-term health outcomes, and devices would need to fit all individuals with a preference for wrist or pendant-worn locations. Technological literacy was not a perceived barrier. However, cost and data accuracy were important for successful implementation.

CONCLUSION

This study provides a group consensus statement and guidance on the clinical implementation of wearable technology to support care for people aged 50 and over experiencing changes to mobility and/or who are at increased risk of falling. Health professionals are receptive to using wearable technologies to advance fall risk and mobility assessment and believe wearable technology has a role in clinical practice.

Depressive symptoms and heart rate variability in perinatal women: A narrative review

AIM

This study aims to review research on heart rate variability and psychiatric symptoms in perinatal women and explains how heart rate variability can be useful in preventing depressive symptoms in perinatal women.

METHODS

Data were collected from PubMed, CINAHL, PsycINFO, and Google Scholar. The literature search encompassed articles published until July 2024, with the inclusion criteria targeting studies on women within 1 year postpartum, starting from the gestation period. Further, articles exploring this population that discussed the relationship between anxiety, depression, stress, and heart rate variability were selected. The exclusion criterion was studies that confirmed a correlation between stressors and heart rate variability.

RESULTS

We identified 36 relevant articles. The results demonstrated that, since 2022, research has been conducted using smartwatches, smartphones, and so on. The effectiveness of using heart rate variability has been confirmed, particularly in studies linking it to depression. However, some studies lacked controls during measurements. Intervention studies demonstrated the effectiveness of heart rate variability biofeedback.

CONCLUSIONS

This is the first review to investigate the relationship between psychiatric symptoms and heart rate variability in perinatal women. Understanding and using the characteristics of heart rate variability may lead to the detection of psychiatric symptoms in perinatal women and to self-care among women.

Unsupervised Assessment of Frailty Status Using Wearable Sensors: A Feasibility Study among Community-Dwelling Older Adults

Objectives

This study examined whether community-dwelling older adults can independently capture wearable sensor data that can be used to classify frailty status.

Methods

Fifty-one older adults (age 77.5 ± 8.4 years, height 163.6 77.5 ± 8.4, weight 72.0 ± 13.5 kg, female 76%) took part in this investigation. Participants independently captured physical activity and physical function data at home using a smartwatch and a research-grade inertial sensor system for 48-hours. Machine learning classifiers were used to determine whether the data obtained can discriminate between frailty levels.

Results

Models incorporating variables from both the smartwatch and inertial sensor system were successful in the prediction of frailty status.

Discussion

This study has demonstrated the ability of older adults to collect data which can be used to indicate their frailty risk. This may enable earlier intervention and lessen the impact of frailty on the individual and society as a whole.

Exploring the Applications of Explainability in Wearable Data Analytics: Systematic Literature Review

BACKGROUND

Wearable technologies have become increasingly prominent in health care. However, intricate machine learning and deep learning algorithms often lead to the development of "black box" models, which lack transparency and comprehensibility for medical professionals and end users. In this context, the integration of explainable artificial intelligence (XAI) has emerged as a crucial solution. By providing insights into the inner workings of complex algorithms, XAI aims to foster trust and empower stakeholders to use wearable technologies responsibly.

OBJECTIVE

This paper aims to review the recent literature and explore the application of explainability in wearables. By examining how XAI can enhance the interpretability of generated data and models, this review sought to shed light on the possibilities that arise at the intersection of wearable technologies and XAI.

METHODS

We collected publications from ACM Digital Library, IEEE Xplore, PubMed, SpringerLink, JMIR, Nature, and Scopus. The eligible studies included technology-based research involving wearable devices, sensors, or mobile phones focused on explainability, machine learning, or deep learning and that used quantified self data in medical contexts. Only peer-reviewed articles, proceedings, or book chapters published in English between 2018 and 2022 were considered. We excluded duplicates, reviews, books, workshops, courses, tutorials, and talks. We analyzed 25 research papers to gain insights into the current state of explainability in wearables in the health care context.

RESULTS

Our findings revealed that wrist-worn wearables such as Fitbit and Empatica E4 are prevalent in health care applications. However, more emphasis must be placed on making the data generated by these devices explainable. Among various explainability methods, post hoc approaches stand out, with Shapley Additive Explanations as a prominent choice due to its adaptability. The outputs of explainability methods are commonly presented visually, often in the form of graphs or user-friendly reports. Nevertheless, our review highlights a limitation in user evaluation and underscores the importance of involving users in the development process.

CONCLUSIONS

The integration of XAI into wearable health care technologies is crucial to address the issue of black box models. While wrist-worn wearables are widespread, there is a notable gap in making the data they generate explainable. Post hoc methods such as Shapley Additive Explanations have gained traction for their adaptability in explaining complex algorithms visually. However, user evaluation remains an area in which improvement is needed, and involving users in the development process can contribute to more transparent and reliable artificial intelligence models in health care applications. Further research in this area is essential to enhance the transparency and trustworthiness of artificial intelligence models used in wearable health care technology.

Integrating Patient-Generated Health Data from Mobile Devices into Electronic Health Records: Best Practice Recommendations by the IFCC Committee on Mobile Health and Bioengineering in Laboratory Medicine (C-MHBLM)

Background

An increasing number of wearable medical devices are being used for personal monitoring and professional health care purposes. These mobile health devices collect a variety of biometric and health data but do not routinely connect to a patient's electronic health record (EHR) or electronic medical record (EMR) for access by a patient's health care team.

Methods

The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Committee on Mobile Health and Bioengineering in Laboratory Medicine (C-MHBLM) developed consensus recommendations for consideration when interfacing mobile health devices to an EHR/EMR.

Results

IFCC C-MHBLM recommendations cover personalized monitoring and privacy concerns, data security, quality assurance of data transfer, and incorporation of alert triggers to warn users of important health conditions.

Conclusions

Considerations for interface ease-of-use, display of patient data in the EHR/EMR, and needs-based training programs for healthcare staff to understand the critical requirements, proper use, and integration of mobile health devices with EHR/EMRs are provided. Cooperation between healthcare providers, device manufacturers, and software developers is also recommended to drive future innovation in mobile health device technology development.

Wearable biosensors for pediatric hospitals: a scoping review

As wearable biosensors are increasingly used in healthcare settings, this review aimed to identify the types of wearable biosensors used for neonate and pediatric patients and how these biosensors were clinically evaluated. A literature search was conducted using PubMed, CINAHL, Embase, Web of Science, and Cochrane. The studies published between January 2010 and February 2024 were included. Descriptive statistics were used to present counts and percentages of types, locations, clinical evaluation methods, and their results. Seventy-nine studies were included. 104 wearable sensors and 40 devices were identified. The most common type of biosensor was optoelectrical sensors (n = 40, 38.5%), and used to measure heart rate (n = 22, 19.0%). The clinical evaluation was tested by a combination of validity (n = 68, 86.1%) and reliability (n = 14, 17.7%). Only two-thirds of the wearable devices were validated or reported acceptable reliability. The majority of the biosensor studies (n = 51, 64.5%) did not report any complications related to wearable biosensors. The current literature has gaps regarding clinical evaluation and safety of wearable biosensor devices with interchangeable use of validity and reliability terms. There is a lack of comprehensive reporting on complications, highlighting the need for standardized guidelines in the clinical evaluation of biosensor medical devices. IMPACT: The most common types of biosensors in pediatric settings were optoelectrical sensors and electrical sensors. Only two-thirds of the wearable devices were validated or reported acceptable reliability, and more than half of the biosensor studies did not report whether they assessed any complications related to wearable biosensors. This review discovered significant gaps in safety and clinical validation reporting, emphasizing the need for standardized guidelines. The findings advocate for improved reporting clinical validation processes to enhance the safety of wearable biosensors in pediatric care.

A Wireless Smart Adhesive Integrated with a Thin-Film Stretchable Inverted-F Antenna

In recent years, skin-mounted devices have gained prominence in personal wellness and remote patient care. However, the rigid components of many wearables often cause discomfort due to their mechanical mismatch with the skin. To address this, we extend the use of the solderable stretchable sensing system (S4) to develop a wireless skin temperature-sensing smart adhesive. This work introduces two novel types of progress in wearables: the first demonstration of Bluetooth-integration and development of a thin-film-based stretchable inverted-F antenna (SIFA). Characterized through RF simulations, vector network analysis under deformation, and anechoic chamber tests, SIFA demonstrated potential as a low-profile, on-body Bluetooth antenna with a resonant frequency of 2.45 GHz that helps S4 retain its thin overall profile. The final S4 system achieved high correlation (R = 0.95, p < 0.001, mean standard error = 0.04 °C) with commercial sensors during daily activities. These findings suggest that S4-based smart adhesives integrated with SIFAs could offer a promising platform for comfortable, efficient, and functional skin-integrated wearables, supporting a range of health monitoring applications.

Towards Wearable and Portable Spine Motion Analysis Through Dynamic Optimization of Smartphone Videos and IMU Data

BACKGROUND

Monitoring spine kinematics is crucial for applications like disease evaluation and ergonomics analysis. However, the small scale of vertebrae and the number of degrees of freedom present significant challenges for noninvasive and convenient spine kinematics estimation.

METHODS

This study developed a dynamic optimization framework for wearable spine motion tracking at the intervertebral joint level by integrating smartphone videos and Inertia Measurement Units (IMUs) with dynamic constraints from a thoracolumbar spine model. Validation involved motion data from 10 healthy males performing static standing, dynamic upright trunk rotations, and gait. This data included rotations of ten IMUs on vertebrae and virtual landmarks from three smartphone videos preprocessed by OpenCap, an application leveraging computer vision for pose estimation. The kinematic measures derived from the optimized solution were compared against simultaneously collected infrared optical marker-based measurements and in vivo literature data. Solutions only based on IMUs or videos were also compared for accuracy evaluation.

RESULTS

The proposed optimization approach closely matched the reference data in the intervertebral or segmental rotation range, demonstrating minimal angular differences across all motions and the highest correlation in 3D rotations (maximal Pearson and intraclass correlation coefficients of 0.92 and 0.94, respectively). Time-series changes of joint angles also aligned well with the optical-marker reference.

CONCLUSION

Dynamic optimization of the spine simulation that integrates IMUs and computer vision outperforms the single-modality method.

SIGNIFICANCE

This markerless 3D spine motion capture method holds potential for spinal health assessment in large cohorts in real-world settings without dedicated laboratories.

Improving the Accuracy of mmWave Radar for Ethical Patient Monitoring in Mental Health Settings

Monitoring patient safety in high-risk mental health environments is a challenge for clinical staff. There has been a recent increase in the adoption of contactless sensing solutions for remote patient monitoring. mmWave radar is a technology that has high potential in this field due it its low cost and protection of privacy; however, it is prone to multipath reflections and other sources of environmental noise. This paper discusses some of the challenges in mmWave remote sensing applications for patient safety in mental health wards. In line with these challenges, we propose a novel low-data solution to mitigate the impact of multipath reflections and other sources of noise in mmWave sensing. Our solution uses an unscented Kalman filter for target tracking over time and analyses features of movement to determine whether targets are human or not. We chose a commercial off-the-shelf radar and compared the accuracy and reliability of sensor measurements before and after applying our solution. Our results show a marked decrease in false positives and false negatives during human target tracking, as well as an improvement in spatial location detection in a two-dimensional space. These improvements demonstrate how a simple low-data solution can improve existing mmWave sensors, making them more suitable for patient safety solutions in high-risk environments.

The analysis of art design under improved convolutional neural network based on the Internet of Things technology

This work aims to explore the application of an improved convolutional neural network (CNN) combined with Internet of Things (IoT) technology in art design education and teaching. The development of IoT technology has created new opportunities for art design education, while deep learning and improved CNN models can provide more accurate and effective tools for image processing and analysis. In order to enhance the effectiveness of art design teaching and students' creative expression, this work proposes an improved CNN model. In model construction, it increases the number of convolutional layers and neurons, and incorporates the batch normalization layer and dropout layer to enhance feature extraction capabilities and reduce overfitting. Besides, this work creates an experimental environment using IoT technology, capturing art image samples and environmental data using cameras, sensors, and other devices. In the model application phase, image samples undergo preprocessing and are input into the CNN for feature extraction. Sensor data are concatenated with image feature vectors and input into the fully connected layers to comprehensively understand the artwork. Finally, this work trains the model using techniques such as cross-entropy loss functions and L2 regularization and adjusts hyperparameters to optimize model performance. The results indicate that the improved CNN model can effectively acquire art sample data and student creative expression data, providing accurate and timely feedback and guidance for art design education and teaching, with promising applications. This work offers new insights and methods for the development of art design education.

The Role of Wearable Devices in Chronic Disease Monitoring and Patient Care: A Comprehensive Review

Wearable health devices are becoming vital in chronic disease management because they offer real-time monitoring and personalized care. This review explores their effectiveness and challenges across medical fields, including cardiology, respiratory health, neurology, endocrinology, orthopedics, oncology, and mental health. A thorough literature search identified studies focusing on wearable devices' impact on patient outcomes. In cardiology, wearables have proven effective for monitoring hypertension, detecting arrhythmias, and aiding cardiac rehabilitation. In respiratory health, these devices enhance asthma management and continuous monitoring of critical parameters. Neurological applications include seizure detection and Parkinson's disease management, with wearables showing promising results in improving patient outcomes. In endocrinology, wearable technology advances thyroid dysfunction monitoring, fertility tracking, and diabetes management. Orthopedic applications include improved postsurgical recovery and rehabilitation, while wearables help in early complication detection in oncology. Mental health benefits include anxiety detection, post-traumatic stress disorder management, and stress reduction through wearable biofeedback. In conclusion, wearable health devices offer transformative potential for managing chronic illnesses by enhancing real-time monitoring and patient engagement. Despite significant improvements in adherence and outcomes, challenges with data accuracy and privacy persist. However, with ongoing innovation and collaboration, we can all be part of the solution to maximize the benefits of wearable technologies in healthcare.

Predicting the Healing of Lower Extremity Fractures Using Wearable Ground Reaction Force Sensors and Machine Learning

Lower extremity fractures pose challenges due to prolonged healing times and limited assessment methods. Integrating wearable sensors with machine learning can help overcome these challenges by providing objective assessment and predicting fracture healing. In this retrospective study, data from a gait monitoring insole on 25 patients with closed lower extremity fractures were analyzed. Continuous underfoot loading data were processed to isolate steps, extract metrics, and feed them into three white-box machine learning models. Decision tree and Lasso regression aided feature selection, while a logistic regression classifier predicted days until fracture healing within a 30-day range. Evaluations via 10-fold cross-validation and leave-one-out validation yielded stable metrics, with the model achieving a mean accuracy, precision, recall, and F1-score of approximately 76%. Feature selection revealed the importance of underfoot loading distribution patterns, particularly on the medial surface. Our research facilitates data-driven decisions, enabling early complication detection, potentially shortening recovery times, and offering accurate rehabilitation timeline predictions.

A Comprehensive Review on Harnessing Wearable Technology for Enhanced Depression Treatment

Depression is a prevalent and debilitating mental health disorder that significantly impacts individuals, families, and societies worldwide. Despite advancements in treatment, challenges remain in effectively managing and monitoring depressive symptoms. Wearable technology, which encompasses devices that can monitor physiological and behavioral parameters in real time, offers promising new avenues for enhancing depression treatment. This comprehensive review explores the potential of wearable technology in managing and treating depression. It examines how wearables can monitor depressive symptoms, improve patient engagement and adherence to treatment plans, and provide valuable data for personalized treatment strategies. The review covers the integration of wearable technology in clinical settings, the role of wearables in remote monitoring and telemedicine, and the ethical and privacy considerations associated with their use. Additionally, it highlights case studies and pilot programs demonstrating the practical applications and outcomes of wearable technology interventions. Future directions and innovations are discussed, identifying potential advancements and challenges in this emerging field. This review aims to inform healthcare professionals, researchers, and policymakers about the opportunities and challenges of integrating wearable technology into depression treatment, ultimately contributing to improved mental healthcare outcomes.

The acceptability of using wearable electronic devices to monitor physical activity of patients with Multiple Myeloma undergoing treatment: a systematic review

Introduction

Multiple myeloma (MM) is diagnosed in 6,000 people in the UK yearly. A performance status measure, based on the patients' reported level of physical activity, is used to assess patients' fitness for treatment. This systematic review aims to explore the current evidence for the acceptability of using wearable devices in patients treated for MM to measure physical activity directly.

Methods

Three databases were searched (MEDLINE, EMBASE and CINAHL) up until 7th September 2023. Prospective studies using wearable devices to monitor physical activity in patients on treatment for MM were included. Bias across the studies was assessed using the CASP tool.

Results

Nine studies, with 220 patients on treatment for MM, were included. Only two studies had a low risk of bias. Different wearable device brands were used for varying lengths of time and were worn on either the wrist, upper arm, or chest. Adherence, reported in seven studies, ranged from 50% to 90%. Six studies reported an adherence greater than 75%. Although physical activity was also measured in a heterogenous manner, most studies reported reduced physical activity during treatment, associated with a higher symptom burden.

Conclusion

Monitoring patients receiving treatment for MM with a wearable device appears acceptable as an objective measure to evaluate physical activity. Due to the heterogeneity of the methods used, the generalisability of the results is limited. Future studies should explore the data collected prospectively and their ability to predict relevant clinical outcomes.

Integrating Artificial Intelligence-Driven Wearable Technology in Oncology Decision-Making: A Narrative Review

BACKGROUND

Clinical decision-making in oncology is a complex process influenced by numerous disease-related factors, patient demographics, and logistical considerations. With the advent of artificial intelligence (AI), precision medicine is undergoing a shift toward more precise and personalized care. Wearable device technology complements this paradigm shift by offering continuous monitoring of patient vitals, facilitating early intervention, and improving treatment adherence. The integration of these technologies promises to enhance the quality of oncological care, making it more responsive and tailored to individual patient needs, thereby enabling wider implementation of such applications in the clinical setting.

SUMMARY

This review article addresses the integration of wearable devices and AI in oncology, exploring their role in patient monitoring, treatment optimization, and research advancement along with an overview of completed clinical trials and utility in different aspects. The vast applications have been exemplified using several studies, and all the clinical trials completed till date have been summarized in Table 2. Additionally, we discuss challenges in implementation, regulatory considerations, and future perspectives for leveraging these technologies to enhance cancer care and radically changing the global health sector.

KEY MESSAGES

AI is transforming cancer care by enhancing diagnostic, prognostic, and treatment planning tools, thus making precision medicine more effective. Wearable technology facilitates continuous, noninvasive monitoring, improving patient engagement and adherence to treatment protocols. The combined use of AI and wearables aids in monitoring patient activity, assessing frailty, predicting chemotherapy tolerance, detecting biomarkers, and managing treatment adherence. Despite these advancements, challenges such as data security, privacy, and the need for standardized devices persist. In the foreseeable future, wearable technology can hold significant potential to revolutionize personalized oncology care, empowering clinicians to deliver comprehensive and tailored treatments alongside standard therapy.

A critical examination of sport discipline typology: identifying inherent limitations and deficiencies in contemporary classification systems

Exercise scientists (especially in the field of biomolecular research) frequently classify athletic cohorts into categories such as endurance, strength, or mixed, and create a practical framework for studying diverse athletic populations between seemingly similar groups. It is crucial to recognize the limitations and complexities of these classifications, as they may oversimplify the multidimensional characteristics of each sport. If so, the validity of studies dealing with such approaches may become compromised and the comparability across different studies challenging or impossible. This perspective critically examines and highlights the issues associated with current sports typologies, critiques existing sports classification systems, and emphasizes the imperative for a universally accepted classification model to enhance the quality of biomolecular research of sports in the future.

Neuro Receptor Signal Detecting and Monitoring Smart Devices for Biological Changes in Cognitive Health Conditions

Background

Currently, wearable sensors significantly impact health care through continuous monitoring and event prediction. The types and clinical applications of wearable technology for the prevention of mental illnesses, as well as associated health authority rules, are covered in the current review.

Summary

The technologies behind wearable ECG monitors, biosensors, electronic skin patches, neural interfaces, retinal prosthesis, and smart contact lenses were discussed. We described how sensors will examine neuronal impulses using verified machine-learning algorithms running in real-time. These sensors will closely monitor body signals and demonstrate continuous sensing with wireless functionality. The wearable applications in the following medical fields were covered in our review: sleep, neurology, mental health, anxiety, depression, Parkinson's disease, epilepsy, seizures, and schizophrenia. These mental health conditions can cause serious issues, even death. Inflammation brought on by mental health problems can worsen hypothalamic-pituitary-adrenal axis dysfunction and interfere with certain neuroregulatory systems such as the neural peptide Y, serotonergic, and cholinergic systems. Severe depressive disorder symptoms are correlated with elevated Interleukin (IL-6) levels. On the basis of previous and present data collected utilizing a variety of sensory modalities, researchers are currently investigating ways to identify or detect the current mental state.

Key message

This review explores the potential of various mental health monitoring technologies. The types and clinical uses of wearable technology, such as ECG monitors, biosensors, electronic skin patches, brain interfaces, retinal prostheses, and smart contact lenses, were covered in the current review will be beneficial for patients with mental health problems like Alzheimer, epilepsy, dementia. The sensors will closely monitor bodily signals with wireless functionality while using machine learning algorithms to analyse neural impulses in real time.

Use of wearable technology in cardiac monitoring after cryptogenic stroke or embolic stroke of undetermined source: a systematic review

INTRODUCTION

Prolonged cardiac monitoring after cryptogenic stroke or embolic stroke of undetermined source (ESUS) is necessary to identify atrial fibrillation (AF) that requires anticoagulation. Wearable devices may improve AF detection compared to conventional management. We aimed to review the evidence for the use of wearable devices in post-cryptogenic stroke and post-ESUS monitoring.

METHODS

We performed a systematic search of PubMed, EMBASE, Scopus and clinicaltrials.gov on 21 July 2022, identifying all studies that investigated the use of wearable devices in patients with cryptogenic stroke or ESUS. The outcomes of AF detection were analysed. Literature reports on electrocardiogram (ECG)-based (external wearable, handheld, patch, mobile cardiac telemetry [MCT], smartwatch) and photoplethysmography (PPG)-based (smartwatch, smartphone) devices were summarised.

RESULTS

A total of 27 relevant studies were included (two randomised controlled trials, seven prospective trials, 10 cohort studies, six case series and two case reports). Only four studies compared wearable technology to Holter monitoring or implantable loop recorder, and these studies showed no significant differences on meta-analysis (odds ratio 2.35, 95% confidence interval [CI] 0.74-7.48, I 2 = 70%). External wearable devices detected AF in 20.7% (95% CI 14.9-27.2, I 2 = 76%) of patients and MCT detected new AF in 9.6% (95% CI 7.4%-11.9%, I 2 = 56%) of patients. Other devices investigated included patch sensors, handheld ECG recorders and PPG-based smartphone apps, which demonstrated feasibility in the post-cryptogenic stroke and post-ESUS setting.

CONCLUSION

Wearable devices that are ECG or PPG based are effective for paroxysmal AF detection after cryptogenic stroke and ESUS, but further studies are needed to establish how they compare with Holter monitors and implantable loop recorder.

florio® HAEMO: A Longitudinal Survey of Patient Preference, Adherence and Wearable Functionality in Central Europe

INTRODUCTION

florio® HAEMO is a hemophilia treatment monitoring application (app) offering activity tracking and wearable device connectivity. Its use might support everyday activities for people with hemophilia. The aim of this study was to evaluate user satisfaction, long-term usage and the impact on data entry when pairing a wearable with a hemophilia monitoring app.

METHODS

This is a follow-up of a two-part user survey conducted in Central Europe. People with hemophilia and parents/caregivers of children with hemophilia using florio HAEMO and who completed part one were invited to complete a second online questionnaire at least 4 months later.

RESULTS

Fifty participants (83.3%) who completed part one of the survey continued to use the florio HAEMO app and completed part two. Of 14 participants who chose to use the app with a wearable, more than half (57.1%) were aged between 13 and 25 years. Overall, the results demonstrated that florio HAEMO is very easy or rather easy to use, especially for individuals pairing the app with a wearable. Most people using a wearable indicated that florio HAEMO was very or rather important in bringing certainty to daily activities (85.7%). Notably, 14 of 36 (38.9%) non-wearable users indicated that they would prefer to pair the app with a wearable in the future.

CONCLUSIONS

Adherence to the florio HAEMO app is maintained over an extended period of use. Pairing the app with a wearable might enable easier access to app features, increase data entry motivation and provide more certainty about daily activities for people with hemophilia.

Heart rate estimation using on-nail wearable photoplethysmography

Heart Rate (HR) measurements in current wearables are mostly derived from photoplethysmography (PPG). PPG signals have been measured at various locations on the body, however, to date, limited studies have investigated wearable, reflective mode, PPG signals from the finger- and toe- nails. Being rigid surfaces, they may provide comparatively motion robust measurements compared to sensors placed on flexible and stretchable skin. Here, we present an on-nail wearable PPG sensor to estimate HR from nail locations in motion-free and motion-present recordings. We compare to commercial electrocardiogram (ECG) and pulse oximeter (PO) units for 20 participants. PPG HR estimation demonstrated strong correlations with the ECG estimated HR, with a root mean square error of 1.6 beats per minute (bpm) and 2.2 bpm, for finger and toenail locations respectively. During motion these figures increased to 5.6 bpm and 12.8 bpm. No substantial difference in accuracy was found across the skin tone of participants. These results demonstrate the potential feasibility of HR monitoring from nail locations. With sensors placed, for example, inside a shoe, this may offer very discrete monitoring for long term applications.

Comprehensive profiling of social mixing patterns in resource poor countries: A mixed methods research protocol

BACKGROUND

Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling.

METHODS

To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures. We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member.

DISCUSSION

Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.

Exploring the Potentials of Wearable Technologies in Managing Vestibular Hypofunction

The vestibular system is dedicated to gaze stabilization, postural balance, and spatial orientation; this makes vestibular function crucial for our ability to interact effectively with our environment. Vestibular hypofunction (VH) progresses over time, and it presents differently in its early and advanced stages. In the initial stages of VH, the effects of VH are mitigated using vestibular rehabilitation therapy (VRT), which can be facilitated with the aid of technology. At more advanced stages of VH, novel techniques that use wearable technologies for sensory augmentation and sensory substitution have been applied to manage VH. Despite this, the potential of assistive technologies for VH management remains underexplored over the past decades. Hence, in this review article, we present the state-of-the-art technologies for facilitating early-stage VRT and for managing advanced-stage VH. Also, challenges and strategies on how these technologies can be improved to enable long-term ambulatory and home use are presented.

Cardiac interoception in the museum: A novel measure of experience

Interoception is the perception of the body's internal signals in response to various external and internal stimuli. The present study uses a novel method adapted from the CARdiac Elevation Detection Task to examine cardiac interoception objectively and subjectively in a unique context-in the presence of art. Self-report questionnaires were used to measure subjective interoceptive awareness, subjective interoceptive accuracy, and aesthetic appreciation. For objective interoceptive accuracy and sensibility, a wearable device (Shimmer) measured heart rate (HR) and connected to a mobile application to prompt two questions: "Is your heart beating faster than usual?" and "How confident are you in your previous response?" Participants explored an art gallery for 40 minutes while the Shimmer measured their HR and randomly prompted them to answer the questions. Using a Generalized Estimating Equation model, interoceptive sensibility was not found to predict the odds of submitting a correct response. It was also found that art does not improve participants' perceptions of their HR. Finally, there was no relation between aesthetic appreciation and subjective or objective cardiac interoception. Despite lack of statistical significance, the current study's method presents an improved method by examining interoceptive accuracy in the moment under ecological conditions. To date, findings and methods used in interoception are inconsistent or flawed; the value in the current study lies in the development and demonstration of a method to examine how the environment influences the body and self-awareness across a wide variety of contexts, thereby offering a possible standardized measure of interoception for investigators to adopt.

Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine

Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing sleep health, considering the bidirectional relationship between sleep and health. This field moves beyond conventional methods, tailoring care to the unique physiological and psychological needs of individuals to improve sleep quality and manage disorders. Key to this approach is the consideration of diverse factors like genetic predispositions, lifestyle habits, environmental factors, and underlying health conditions. This enables more accurate diagnoses, targeted treatments, and proactive management. Technological advancements play a pivotal role in this field: wearable devices, mobile health applications, and advanced diagnostic tools collect detailed sleep data for continuous monitoring and analysis. The integration of machine learning and artificial intelligence enhances data interpretation, offering personalized treatment plans based on individual sleep profiles. Moreover, research on circadian rhythms and sleep physiology is advancing our understanding of sleep's impact on overall health. The next generation of wearable technology will integrate more seamlessly with IoT and smart home systems, facilitating holistic sleep environment management. Telemedicine and virtual healthcare platforms will increase accessibility to specialized care, especially in remote areas. Advancements will also focus on integrating various data sources for comprehensive assessments and treatments. Genomic and molecular research could lead to breakthroughs in understanding individual sleep disorders, informing highly personalized treatment plans. Sophisticated methods for sleep stage estimation, including machine learning techniques, are improving diagnostic precision. Computational models, particularly for conditions like obstructive sleep apnea, are enabling patient-specific treatment strategies. The future of personalized sleep medicine will likely involve cross-disciplinary collaborations, integrating cognitive behavioral therapy and mental health interventions. Public awareness and education about personalized sleep approaches, alongside updated regulatory frameworks for data security and privacy, are essential. Longitudinal studies will provide insights into evolving sleep patterns, further refining treatment approaches. In conclusion, personalized sleep medicine is revolutionizing sleep disorder treatment, leveraging individual characteristics and advanced technologies for improved diagnosis, treatment, and management. This shift towards individualized care marks a significant advancement in healthcare, enhancing life quality for those with sleep disorders.

Teleneurology and Artificial Intelligence in Clinical Practice

ABSTRACT

As teleheath becomes integrated into the practice of medicine, it is important to understand the benefits, limitations, and variety of applications. Telestroke was an early example of teleneurology that arose from a need for urgent access to neurologists for time-sensitive treatments for stroke. It made a scarce resource widely available via video conferencing technologies. Additionally, applications such as outpatient video visits, electronic consultation (e-consult), and wearable devices developed in neurology, as well. Telehealth dramatically increased during the COVID-19 pandemic when offices were closed and hospitals were overwhelmed; a multitude of both outpatient and inpatient programs developed and matured during this time. It is helpful to explore what has been learned regarding the quality of telehealth, disparities in care, and how artificial intelligence can interact with medical practices in the teleneurology context.

Development of a Low-Cost Markerless Optical Motion Capture System for Gait Analysis and Anthropometric Parameter Quantification

Technological advancements have expanded the range of methods for capturing human body motion, including solutions involving inertial sensors (IMUs) and optical alternatives. However, the rising complexity and costs associated with commercial solutions have prompted the exploration of more cost-effective alternatives. This paper presents a markerless optical motion capture system using a RealSense depth camera and intelligent computer vision algorithms. It facilitates precise posture assessment, the real-time calculation of joint angles, and acquisition of subject-specific anthropometric data for gait analysis. The proposed system stands out for its simplicity and affordability in comparison to complex commercial solutions. The gathered data are stored in comma-separated value (CSV) files, simplifying subsequent analysis and data mining. Preliminary tests, conducted in controlled laboratory environments and employing a commercial MEMS-IMU system as a reference, revealed a maximum relative error of 7.6% in anthropometric measurements, with a maximum absolute error of 4.67 cm at average height. Stride length measurements showed a maximum relative error of 11.2%. Static joint angle tests had a maximum average error of 10.2%, while dynamic joint angle tests showed a maximum average error of 9.06%. The proposed optical system offers sufficient accuracy for potential application in areas such as rehabilitation, sports analysis, and entertainment.

Sacroiliac Joint Dysfunction in Endurance Runners Using Wearable Technology as a Clinical Monitoring Tool: Systematic Review

BACKGROUND

In recent years, researchers have delved into the relationship between the anatomy and biomechanics of sacroiliac joint (SIJ) pain and dysfunction in endurance runners to elucidate the connection between lower back pain and the SIJ. However, the majority of SIJ pain and dysfunction cases are diagnosed and managed through a traditional athlete-clinician arrangement, where the athlete must attend regular in-person clinical appointments with various allied health professionals. Wearable sensors (wearables) are increasingly serving as a clinical diagnostic tool to monitor an athlete's day-to-day activities remotely, thus eliminating the necessity for in-person appointments. Nevertheless, the extent to which wearables are used in a remote setting to manage SIJ dysfunction in endurance runners remains uncertain.

OBJECTIVE

This study aims to conduct a systematic review of the literature to enhance our understanding regarding the use of wearables in both in-person and remote settings for biomechanical-based rehabilitation in SIJ dysfunction among endurance runners. In addressing this issue, the overarching goal was to explore how wearables can contribute to the clinical diagnosis (before, during, and after) of SIJ dysfunction.

METHODS

Three online databases, including PubMed, Scopus, and Google Scholar, were searched using various combinations of keywords. Initially, a total of 4097 articles were identified. After removing duplicates and screening articles based on inclusion and exclusion criteria, 45 articles were analyzed. Subsequently, 21 articles were included in this study. The quality of the investigation was assessed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) evidence-based minimum set of items for reporting in systematic reviews.

RESULTS

Among the 21 studies included in this review, more than half of the investigations were literature reviews focusing on wearable sensors in the diagnosis and treatment of SIJ pain, wearable movement sensors for rehabilitation, or a combination of both for SIJ gait analysis in an intelligent health care setting. As many as 4 (19%) studies were case reports, and only 1 study could be classified as fully experimental. One paper was classified as being at the "pre" stage of SIJ dysfunction, while 6 (29%) were identified as being at the "at" stage of classification. Significantly fewer studies attempted to capture or classify actual SIJ injuries, and no study directly addressed the injury recovery stage.

CONCLUSIONS

SIJ dysfunction remains underdiagnosed and undertreated in endurance runners. Moreover, there is a lack of clear diagnostic or treatment pathways using wearables remotely, despite the availability of validated technology. Further research of higher quality is recommended to investigate SIJ dysfunction in endurance runners and explore the use of wearables for rehabilitation in remote settings.

A Comprehensive Review on Advancements in Wearable Technologies: Revolutionizing Cardiovascular Medicine

Wearable technologies have emerged as powerful tools in healthcare, offering continuous monitoring and personalized insights outside traditional clinical settings. These devices have garnered significant attention in cardiovascular medicine for their potential to transform patient care and improve outcomes. This comprehensive review provides an overview of wearable technologies' evolution, advancements, and applications in cardiovascular medicine. We examine the miniaturization of sensors, integration of artificial intelligence (AI), and proliferation of remote patient monitoring solutions. Key findings include the role of wearables in the early detection of cardiovascular conditions, personalized health tracking, and remote patient management. Challenges such as data privacy concerns and regulatory hurdles are also addressed. The adoption of wearable technologies holds promise for shifting healthcare from reactive to proactive, enabling precision diagnostics, treatment optimization, and preventive strategies. Collaboration among healthcare stakeholders is essential to harnessing the full potential of wearables in cardiovascular medicine and ushering in a new era of personalized, proactive healthcare.

Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks

Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. The detection, recognition, and subsequent classification of physical activity based on type and intensity are integral components of DM treatment. The continuous glucose monitoring system (CGMS) signal provides the blood glucose (BG) level, and the combination of CGMS and heart rate (HR) signals are potential targets for detecting relevant physical activity from the BG variation point of view. The main objective of the present research is the developing of an artificial intelligence (AI) algorithm capable of detecting physical activity using these signals. Using multiple recurrent models, the best-achieved performance of the different classifiers is a 0.99 area under the receiver operating characteristic curve. The application of recurrent neural networks (RNNs) is shown to be a powerful and efficient solution for accurate detection and analysis of physical activity in patients with DM. This approach has great potential to improve our understanding of individual activity patterns, thus contributing to a more personalized and effective management of DM.

Smartwatch Versus Routine Tremor Documentation: Descriptive Comparison

We addressed the limitations of subjective clinical tremor assessment by comparing routine neurological evaluation with a Tremor Occurrence Score derived from smartwatch sensor data, among 142 participants with Parkinson disease and 77 healthy controls. Our findings highlight the potential of smartwatches for automated tremor detection as a valuable addition to conventional assessments, applicable in both clinical and home settings.

Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review

Over the past five years, interest in the literature regarding the security of the Internet of Medical Things (IoMT) has increased. Due to the enhanced interconnectedness of IoMT devices, their susceptibility to cyber-attacks has proportionally escalated. Motivated by the promising potential of AI-related technologies to improve certain cybersecurity measures, we present a comprehensive review of this emerging field. In this review, we attempt to bridge the corresponding literature gap regarding modern cybersecurity technologies that deploy AI techniques to improve their performance and compensate for security and privacy vulnerabilities. In this direction, we have systematically gathered and classified the extensive research on this topic. Our findings highlight the fact that the integration of machine learning (ML) and deep learning (DL) techniques improves both the performance of cybersecurity measures and their speed, reliability, and effectiveness. This may be proven to be useful for improving the security and privacy of IoMT devices. Furthermore, by considering the numerous advantages of AI technologies as opposed to their core cybersecurity counterparts, including blockchain, anomaly detection, homomorphic encryption, differential privacy, federated learning, and so on, we provide a structured overview of the current scientific trends. We conclude with considerations for future research, emphasizing the promising potential of AI-driven cybersecurity in the IoMT landscape, especially in patient data protection and in data-driven healthcare.

Preliminary Study of Novel Bio-Crypto Key Generation Using Clustering-Based Binarization of ECG Features

In modern society, the popularity of wearable devices has highlighted the need for data security. Bio-crypto keys (bio-keys), especially in the context of wearable devices, are gaining attention as a next-generation security method. Despite the theoretical advantages of bio-keys, implementing such systems poses practical challenges due to their need for flexibility and convenience. Electrocardiograms (ECGs) have emerged as a potential solution to these issues but face hurdles due to intra-individual variability. This study aims to evaluate the possibility of a stable, flexible, and convenient-to-use bio-key using ECGs. We propose an approach that minimizes biosignal variability using normalization, clustering-based binarization, and the fuzzy extractor, enabling the generation of personalized seeds and offering ease of use. The proposed method achieved a maximum entropy of 0.99 and an authentication accuracy of 95%. This study evaluated various parameter combinations for generating effective bio-keys for personal authentication and proposed the optimal combination. Our research holds potential for security technologies applicable to wearable devices and healthcare systems.

Wearable technology use in long-term care facilities for older adults: a scoping review protocol

OBJECTIVE

The objective of this scoping review is to explore how wearable technology is being used to care for older adults in long-term care facilities.

INTRODUCTION

The use of digital health technologies to support care delivery in long-term care facilities for older adults has grown significantly in recent years, especially since the COVID-19 pandemic. Wearable technology refers to devices worn or attached to the body that can track a variety of health-related data, such as vital signs, falls, and sleep patterns. Despite the evidence that wearable devices are playing an increasing role in older adults' care, no review has been conducted on how wearable technology is being used in long-term care facilities.

INCLUSION CRITERIA

This review will consider studies that include people aged over 65, with any health condition or level of disability, who live in long-term care facilities. Primary and secondary studies using quantitative, qualitative, and mixed methods study designs will be included. Dissertations and policy documents will also be considered.

METHODS

Data sources will include comprehensive searches of electronic databases (MEDLINE, Embase, CINAHL, and Scopus), gray literature, and reference scanning of relevant studies. Two independent reviewers will screen titles, abstracts, and full texts of the selected studies. Data extraction will be performed using a tool developed by the researchers. Data will be mapped and analyzed. Descriptive frequencies and content analysis will be included, along with the tabulated results, which will be used to present the findings with regard to the review objectives.

REVIEW REGISTRATION

Open Science Framework https://osf.io/r9qtd.

Artificial intelligence assisted patient blood and urine droplet pattern analysis for non-invasive and accurate diagnosis of bladder cancer

Bladder cancer is one of the most common cancer types in the urinary system. Yet, current bladder cancer diagnosis and follow-up techniques are time-consuming, expensive, and invasive. In the clinical practice, the gold standard for diagnosis remains invasive biopsy followed by histopathological analysis. In recent years, costly diagnostic tests involving the use of bladder cancer biomarkers have been developed, however these tests have high false-positive and false-negative rates limiting their reliability. Hence, there is an urgent need for the development of cost-effective, and non-invasive novel diagnosis methods. To address this gap, here we propose a quick, cheap, and reliable diagnostic method. Our approach relies on an artificial intelligence (AI) model to analyze droplet patterns of blood and urine samples obtained from patients and comparing them to cancer-free control subjects. The AI-assisted model in this study uses a deep neural network, a ResNet network, pre-trained on ImageNet datasets. Recognition and classification of complex patterns formed by dried urine or blood droplets under different conditions resulted in cancer diagnosis with a high specificity and sensitivity. Our approach can be systematically applied across droplets, enabling comparisons to reveal shared spatial behaviors and underlying morphological patterns. Our results support the fact that AI-based models have a great potential for non-invasive and accurate diagnosis of malignancies, including bladder cancer.

Wearable Sensors as a Preoperative Assessment Tool: A Review

Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool.

Wearables for persons with blindness and low vision: form factor matters

Based on statistics from the WHO and the International Agency for the Prevention of Blindness, an estimated 43.3 million people have blindness and 295 million have moderate and severe vision impairment globally as of 2020, statistics expected to increase to 61 million and 474 million respectively by 2050, staggering numbers. Blindness and low vision (BLV) stultify many activities of daily living, as sight is beneficial to most functional tasks. Assistive technologies for persons with blindness and low vision (pBLV) consist of a wide range of aids that work in some way to enhance one's functioning and support independence. Although handheld and head-mounted approaches have been primary foci when building new platforms or devices to support function and mobility, this perspective reviews potential shortcomings of these form factors or embodiments and posits that a body-centered approach may overcome many of these limitations.

Wearable devices for gait and posture monitoring via telemedicine in people with movement disorders and multiple sclerosis: a systematic review

INTRODUCTION

Wearable devices and telemedicine are increasingly used to track health-related parameters across patient populations. Since gait and postural control deficits contribute to mobility deficits in persons with movement disorders and multiple sclerosis, we thought it interesting to evaluate devices in telemedicine for gait and posture monitoring in such patients.

METHODS

For this systematic review, we searched the electronic databases MEDLINE (PubMed), SCOPUS, Cochrane Library, and SPORTDiscus. Of the 452 records retrieved, 12 met the inclusion/exclusion criteria. Data about (1) study characteristics and clinical aspects, (2) technical, and (3) telemonitoring and teleconsulting were retrieved, The studies were quality assessed.

RESULTS

All studies involved patients with Parkinson's disease; most used triaxial accelerometers for general assessment (n = 4), assessment of motor fluctuation (n = 3), falls (n = 2), and turning (n = 3). Sensor placement and count varied widely across studies. Nine used lab-validated algorithms for data analysis. Only one discussed synchronous patient feedback and asynchronous teleconsultation.

CONCLUSIONS

Wearable devices enable real-world patient monitoring and suggest biomarkers for symptoms and behaviors related to underlying gait disorders. thus enriching clinical assessment and personalized treatment plans. As digital healthcare evolves, further research is needed to enhance device accuracy, assess user acceptability, and integrate these tools into telemedicine infrastructure.

PROSPERO REGISTRATION

CRD42022355460.

Passive sweat wearable: A new paradigm in the wearable landscape toward enabling "detect to treat" opportunities

Growing interest over recent years in personalized health monitoring coupled with the skyrocketing popularity of wearable smart devices has led to the increased relevance of wearable sweat-based sensors for biomarker detection. From optimizing workouts to risk management of cardiovascular diseases and monitoring prediabetes, the ability of sweat sensors to continuously and noninvasively measure biomarkers in real-time has a wide range of applications. Conventional sweat sensors utilize external stimulation of sweat glands to obtain samples, however; this stimulation influences the expression profile of the biomarkers and reduces the accuracy of the detection method. To address this limitation, our laboratory pioneered the development of the passive sweat sensor subfield, which allowed for our progress in developing a sweat chemistry panel. Passive sweat sensors utilize nanoporous structures to confine and detect biomarkers in ultra-low sweat volumes. The ability of passive sweat sensors to use smaller samples than conventional sensors enable users with sedentary lifestyles who perspire less to benefit from sweat sensor technology not previously afforded to them. Herein, the mechanisms and strategies of current sweat sensors are summarized with an emphasis on the emerging subfield of passive sweat-based diagnostics. Prospects for this technology include discovering new biomarkers expressed in sweat and expanding the list of relevant detectable biomarkers. Moreover, the accuracy of biomarker detection can be enhanced with machine learning using prediction algorithms trained on clinical data. Applying this machine learning in conjunction with multiplex biomarker detection will allow for a more holistic approach to trend predictions. This article is categorized under: Diagnostic Tools > Diagnostic Nanodevices Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Diagnostic Tools > Biosensing.

Switched CMOS current source compared to enhanced Howland circuit for bio-impedance applications

Bio-impedance Spectroscopy (BIS) is a technique that allows tissue analysis to diagnose a variety of diseases, such as medical imaging, cancer diagnosis, muscle fatigue detection, glucose measurement, and others under research. The development of CMOS integrated circuit front-ends for bioimpedance analysis is required by the increasing use of wearable devices in the healthcare field, as they offer key features for battery-powered wearable devices. These features include high miniaturization, low power consumption, and low voltage power supply. A key circuit in BIS systems is the current source, and one of the most common topology is the Enhanced Howland Current Source (EHCS). EHCS is also used when the current driver is driven by a pseudo-random signal like discrete interval binary sequences (DIBS), which, due to its broadband nature, requires high performance operational amplifiers. These facts lead to the need for a current source more compatible with DIBS signals, ultra-low power supply, standard CMOS integrated circuit, output current amplitude independent of input voltage amplitude, high output impedance, high load capability, high output voltage swing, and the possibility of tetra-polar BIS analysis, that is a pseudotetra-polar in the case of EHCS. The objective of this work is to evaluate the performance of the Switching CMOS Current Source (SCMOSCS) over EHCS using a Cole-skin model as a load using SPICE simulations (DC and AC sweeps and transient analysis). The SCMOSCS demonstrated an output impedance of more than 20 MΩ, a ± 2.5 V output voltage swing from a +3.3 V supply, a 275 μA current consumption, and a 10 kΩ load capacity. These results contrast with the + 1.5 V output voltage swing, the 3 kΩ load capacity, and the 4.9 mA current of the EHCS case.