Postural transitions detection and characterization in healthy and patient populations using a single waist sensor

Home-based monitoring of cerebral oxygenation in response to postural changes using near-infrared spectroscopy

Orthostatic hypotension (OH) is prevalent in older adults and can cause falls and hospitalization. Diagnostic intermittent blood pressure (BP) measurements are only a proxy for cerebral perfusion and do not reflect daily-life BP fluctuations. Near-infrared spectroscopy (NIRS)-measured cerebral oxygenation potentially overcomes these drawbacks. This study aimed to determine feasibility, face validity, and reliability of NIRS in the home environment. Ten participants with OH (2 female, mean age 77, SD 3.7) and 11 without OH (5 female, mean age 78, SD 6.7) wore a NIRS sensor at home on two different days for 10-11 h per day. Preceded by a laboratory-situated test, cerebral oxygenation was measured during three standardized supine-stand tests per day and during unsupervised daily life activities. Data availability, quality, and user experience were assessed (feasibility), as well as differences in posture-related oxygenation responses between participants with and without OH and between symptomatic (dizziness, light-headedness, blurred vision) and asymptomatic postural changes (face validity). Reliability was assessed through repetitive supine-stand tests. Up to 80% of the standardized home-based supine-stand tests could be analyzed. Oxygenation recovery values were lower for participants with OH (p = 0 .03-0.15); in those with OH, oxygenation showed a deeper maximum drop for symptomatic than asymptomatic postural changes (p = 0.04). Intra-class correlation coefficients varied from 0.07 to 0.40, with no consistent differences over measurements. This proof-of-concept study shows feasibility and face validity of at-home oxygenation monitoring using NIRS, confirming its potential value for diagnosis and monitoring in OH and OH-related symptoms. Further data are needed for conclusions about reliability.

A Novel CNN-BiLSTM Ensemble Model With Attention Mechanism for Sit-to-Stand Phase Identification Using Wearable Inertial Sensors

Sit-to-stand transition phase identification is vital in the control of a wearable exoskeleton robot for assisting patients to stand stably. In this study, we aim to propose a method for segmenting and identifying the sit-to-stand phase using two inertial sensors. First, we defined the sit-to-stand transition into five phases, namely, the initial sitting phase, the flexion momentum phase, the momentum transfer phase, the extension phase, and the stable standing phase based on the preprocessed acceleration and angular velocity data. We then employed a threshold method to recognize the initial sitting and the stable standing phases. Finally, we designed a novel CNN-BiLSTM-Attention algorithm to identify the three transition phases, namely, the flexion momentum phase, the momentum transfer phase, and the extension phase. Fifteen subjects were recruited to perform sit-to-stand transition experiments under a specific paradigm. A combination of the acceleration and angular velocity data features for the sit-to-stand transition phase identification were validated for the model performance improvements. The integration of the CNN, Bi-LSTM, and Attention modules demonstrated the reasonableness of the proposed algorithms. The experimental results showed that the proposed CNN-BiLSTM-Attention algorithm achieved the highest average classification accuracy of 99.5% for all five phases when compared to both traditional machine learning algorithms and deep learning algorithms on our customized dataset (STS-PD). The proposed sit-to-stand phase recognition algorithm could serve as a foundation for the control of wearable exoskeletons and is important for the further development of intelligent wearable exoskeleton rehabilitation robots.

Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults

Accurate measurement of sedentary behaviour in older adults is informative and relevant. Yet, activities such as sitting are not accurately distinguished from non-sedentary activities (e.g., upright activities), especially in real-world conditions. This study examines the accuracy of a novel algorithm to identify sitting, lying, and upright activities in community-dwelling older people in real-world conditions. Eighteen older adults wore a single triaxial accelerometer with an onboard triaxial gyroscope on their lower back and performed a range of scripted and non-scripted activities in their homes/retirement villages whilst being videoed. A novel algorithm was developed to identify sitting, lying, and upright activities. The algorithm's sensitivity, specificity, positive predictive value, and negative predictive value for identifying scripted sitting activities ranged from 76.9% to 94.8%. For scripted lying activities: 70.4% to 95.7%. For scripted upright activities: 75.9% to 93.1%. For non-scripted sitting activities: 92.3% to 99.5%. No non-scripted lying activities were captured. For non-scripted upright activities: 94.3% to 99.5%. The algorithm could, at worst, overestimate or underestimate sedentary behaviour bouts by ±40 s, which is within a 5% error for sedentary behaviour bouts. These results indicate good to excellent agreement for the novel algorithm, providing a valid measure of sedentary behaviour in community-dwelling older adults.

An electromyographical comparison of torso muscle activity and ratio during modified side bridge exercises

BACKGROUND

Individualized exercise programs based on personal impairment could lead to successful rehabilitation. An effective way to train spine stability is to find exercises that take advantage of the synergistic relation between local and global stabilization systems.

OBJECTIVE

This study aimed to investigate synergistic relationship between the muscles of the local and global systems during three modified side bridge exercises compared with traditional side bridge (TSB).

METHODS

Twenty healthy participants performed TSB, both leg lift while side-lying (BLLS), torso lift on a 45∘ bench while side-lying (TLBS), and pelvic lift on side-lying (PLS) in random order. Surface electromyography data were analyzed.

RESULTS

The results indicate that PLS was effective as TSB on trunk muscle activity. However, BLLS and TLBS demonstrated significantly less rectus abdominal (RA) muscle activity compared to TSB (p< .001). Additionally, BLLS and TLBS had a higher internal oblique (IO)/RA muscle activity ratio than TSB (p< .001).

CONCLUSIONS

PLS could be a suitable alternative exercise for individuals who are unable to perform TSB, as it can effectively activate trunk muscles. BLLS and TLBS may be appropriate for training the local stability system, while limiting activation of the RA.

Effect of Lower Limb vs. Abdominal Compression on Mobility in Orthostatic Hypotension: A Single-Blinded, Randomized, Controlled, Cross-Over Pilot Study in Parkinson's Disease

BACKGROUND

Orthostatic hypotension (OH) in Parkinson's disease (PD) is frequent and associated with impairments in quality of life and reduced activities of daily living. Abdominal binders (AB) and compression stockings (CS) have been shown to be effective non-pharmacological treatment options.

OBJECTIVE

Here, we investigate the effect of AB versus CS on physical activity using a digital mobility outcome (sit to stand [STS] frequency) collected in the usual environment as a primary endpoint.

METHODS

We enrolled 16 PD patients with at least moderate symptomatic OH. In a randomized, single-blinded, controlled, crossover design, participants were assessed without OH treatment over 1 week (baseline), then were given AB or CS for 1 week and subsequently switched to the other treatment arm. The primary outcome was the number of real-life STS movements per hour as assessed with a lower back sensor. Secondary outcomes included real-life STS duration, mean/systolic/diastolic blood pressure drop (BPD), orthostatic hypotension questionnaire (OHQ), PD quality of life (PDQ-39), autonomic symptoms (SCOPA-AUT), non-motor symptoms (NMSS), MDS-UPDRS, and activities of daily living (ADL/iADL).

RESULTS

Real-life STS frequency on CS was 4.4±4.1 per hour compared with 3.6±2.2 on AB and 3.6±1.8 without treatment (p = 1.0). Concerning the secondary outcomes, NMSS showed significant improvement with CS and AB. OHQ and SCOPA-AUT improved significantly with AB but not CS, and mean BPD drop worsened with CS but not AB. Mean STS duration, PDQ-39, MDS-UPDRS, ADL, and iADL did not significantly change.

CONCLUSION

Both AB and CS therapies do not lead to a significant change of physical activity in PD patients with at least moderate symptomatic OH. Secondary results speak for an effect of both therapies concerning non-motor symptoms, with superiority of AB therapy over CS therapy.

Scoring the Sit-to-Stand Performance of Parkinson's Patients with a Single Wearable Sensor

Monitoring disease progression in Parkinson's disease is challenging. Postural transfers by sit-to-stand motions are adapted to trace the motor performance of subjects. Wearable sensors such as inertial measurement units allow for monitoring motion performance. We propose quantifying the sit-to-stand performance based on two scores compiling kinematics, dynamics, and energy-related variables. Three groups participated in this research: asymptomatic young participants (n = 33), senior asymptomatic participants (n = 17), and Parkinson's patients (n = 20). An unsupervised classification was performed of the two scores to differentiate the three populations. We found a sensitivity of 0.4 and a specificity of 0.96 to distinguish Parkinson's patients from asymptomatic subjects. In addition, seven Parkinson's patients performed the sit-to-stand task "ON" and "OFF" medication, and we noted the scores improved with the patients' medication states (MDS-UPDRS III scores). Our investigation revealed that Parkinson's patients demonstrate a wide spectrum of mobility variations, and while one inertial measurement unit can quantify the sit-to-stand performance, differentiating between PD patients and healthy adults and distinguishing between "ON" and "OFF" periods in PD patients is still challenging.

Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke

Background: Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data. We here develop and validate a solution for various sensor configurations specifically for a stroke population.

Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations.

Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN.

Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors.

A vision for the future of wearable sensors in spine care and its challenges: narrative review

OBJECTIVE

This review aimed to: (I) provide a brief overview of some topical areas of current literature regarding applications of wearable sensors in the management of low back pain (LBP); (II) present a vision for a future comprehensive system that integrates wearable sensors to measure multiple parameters in the real world that contributes data to guide treatment selection (aided by artificial intelligence), uses wearables to aid treatment support, adherence and outcome monitoring, and interrogates the response of the individual patient to the prescribed treatment to guide future decision support for other individuals who present with LBP; and (III) consider the challenges that will need to be overcome to make such a system a reality.

BACKGROUND

Advances in wearable sensor technologies are opening new opportunities for the assessment and management of spinal conditions. Although evidence of improvements in outcomes for individuals with LBP from the use of sensors is limited, there is enormous future potential.

METHODS

Narrative review and literature synthesis.

CONCLUSIONS

Substantial research is underway by groups internationally to develop and test elements of this system, to design innovative new sensors that enable recording of new data in new ways, and to fuse data from multiple sources to provide rich information about an individual's experience of LBP. Together this system, incorporating data from wearable sensors has potential to personalise care in ways that were hitherto thought impossible. The potential is high but will require concerted effort to develop and ultimately will need to be feasible and more effective than existing management.

Analysis of Relationship between Natural Standing Behavior of Elderly People and a Class of Standing Aids in a Living Space

As the world’s population ages, technology-based support for the elderly is becoming increasingly important. This study analyzes the relationship between natural standing behavior measured in a living space of elderly people and the classes of standing aids, as well as the physical and cognitive abilities contributing to household fall injury prevention. In total, 24 elderly standing behaviors from chairs, sofas, and nursing beds recorded in an RGB-D elderly behavior library were analyzed. The differences in standing behavior were analyzed by focusing on intrinsic and common standing aid characteristics among various seat types, including armrests of chairs or sofas and nursing bed handrails. The standing behaviors were categorized into two types: behaviors while leaning the trunk forward without using an armrest as a standing aid and those without leaning the trunk forward by using an arrest or handrail as a standing aid. The standing behavior clusters were distributed in a two-dimensional map based on the seat type rather than the physical or cognitive abilities. Therefore, to reduce the risk of falling, it would be necessary to implement a seat type that the elderly can unconsciously and naturally use as a standing aid even with impaired physical and cognitive abilities.

Effect of Fear of Falling on Mobility Measured During Lab and Daily Activity Assessments in Parkinson's Disease

In chronic disorders such as Parkinson’s disease (PD), fear of falling (FOF) is associated with falls and reduced quality of life. With inertial measurement units (IMUs) and dedicated algorithms, different aspects of mobility can be obtained during supervised tests in the lab and also during daily activities. To our best knowledge, the effect of FOF on mobility has not been investigated in both of these settings simultaneously. Our goal was to evaluate the effect of FOF on the mobility of 26 patients with PD during clinical assessments and 14 days of daily activity monitoring. Parameters related to gait, sit-to-stand transitions, and turns were extracted from IMU signals on the lower back. Fear of falling was assessed using the Falls Efficacy Scale-International (FES-I) and the patients were grouped as with (PD-FOF+) and without FOF (PD-FOF−). Mobility parameters between groups were compared using logistic regression as well as the effect size values obtained using the Wilcoxon rank-sum test. The peak angular velocity of the turn-to-sit transition of the timed-up-and-go (TUG) test had the highest discriminative power between PD-FOF+ and PD-FOF− (r-value of effect size = 0.61). Moreover, PD-FOF+ had a tendency toward lower gait speed at home and a lower amount of walking bouts, especially for shorter walking bouts. The combination of lab and daily activity parameters reached a higher discriminative power [area under the curve (AUC) = 0.75] than each setting alone (AUC = 0.68 in the lab, AUC = 0.54 at home). Comparing the gait speed between the two assessments, the PD-FOF+ showed higher gait speeds in the capacity area compared with their TUG test in the lab. The mobility parameters extracted from both lab and home-based assessments contribute to the detection of FOF in PD. This study adds further evidence to the usefulness of mobility assessments that include different environments and assessment strategies. Although this study was limited in the sample size, it still provides a helpful method to consider the daily activity measurement of the patients with PD into clinical evaluation. The obtained results can help the clinicians with a more accurate prevention and treatment strategy.

A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities

In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision.

Instrumented 5-Time Sit-To-Stand Test: Parameters Predicting Serious Falls beyond the Duration of the Test

BACKGROUND

Falls are a major cause of injuries in older adults. To evaluate the risk of falls in older adults, clinical assessments such as the 5-time sit-to-stand (5xSTS) test can be performed. The development of inertial measurement units (IMUs) has provided the possibility of a more in-depth analysis of the movements' biomechanical characteristics during this test. The goal of the present study was to investigate whether an instrumented 5xSTS test provides additional information to predict multiple or serious falls compared to the conventional stopwatch-based method.

METHODS

Data from 458 community-dwelling older adults were analyzed. The participants were equipped with an IMU on the trunk to extract temporal, kinematic, kinetic, and smoothness movement parameters in addition to the total duration of the test by the stopwatch.

RESULTS

The total duration of the test obtained by the IMU and the stopwatch was in excellent agreement (Pearson's correlation coefficient: 0.99), while the total duration obtained by the IMU was systematically 0.52 s longer than the stopwatch. In multivariable analyses that adjusted for potential confounders, fallers had slower vertical velocity, reduced vertical acceleration, lower vertical power, and lower vertical jerk than nonfallers. In contrast, the total duration of the test measured by either the IMU or the stopwatch did not differ between the 2 groups.

CONCLUSIONS

An instrumented 5xSTS test provides additional information that better discriminates among older adults those at risk of multiple or serious falls than the conventional stopwatch-based assessment.

Proposed Mobility Assessments with Simultaneous Full-Body Inertial Measurement Units and Optical Motion Capture in Healthy Adults and Neurological Patients for Future Validation Studies: Study Protocol

Healthy adults and neurological patients show unique mobility patterns over the course of their lifespan and disease. Quantifying these mobility patterns could support diagnosing, tracking disease progression and measuring response to treatment. This quantification can be done with wearable technology, such as inertial measurement units (IMUs). Before IMUs can be used to quantify mobility, algorithms need to be developed and validated with age and disease-specific datasets. This study proposes a protocol for a dataset that can be used to develop and validate IMU-based mobility algorithms for healthy adults (18–60 years), healthy older adults (>60 years), and patients with Parkinson’s disease, multiple sclerosis, a symptomatic stroke and chronic low back pain. All participants will be measured simultaneously with IMUs and a 3D optical motion capture system while performing standardized mobility tasks and non-standardized activities of daily living. Specific clinical scales and questionnaires will be collected. This study aims at building the largest dataset for the development and validation of IMU-based mobility algorithms for healthy adults and neurological patients. It is anticipated to provide this dataset for further research use and collaboration, with the ultimate goal to bring IMU-based mobility algorithms as quickly as possible into clinical trials and clinical routine.

Toward a remote assessment of walking bout and speed: application in patients with multiple sclerosis

Gait speed as a powerful biomarker of mobility is mostly assessed in controlled environments, e.g. in the clinic. With wearable inertial sensors, gait speed can be estimated in an objective manner. However, most of the previous works have validated the gait speed estimation algorithms in clinical settings which can be different than the home assessments in which the patients demonstrate their actual performance. Moreover, to provide comfort for the users, devising an algorithm based on a single sensor setup is essential. To this end, the goal of this study was to develop and validate a new gait speed estimation method based on a machine learning approach to predict gait speed in both clinical and home assessments by a sensor on the lower back. Moreover, two methods were introduced to detect walking bouts during daily activities at home. We have validated the algorithms in 35 patients with multiple sclerosis as it often presents with mobility difficulties. Therefore, the robustness of the algorithm can be shown in an impaired or slow gait. Against silver standard multi-sensor references, we achieved a bias close to zero and a precision of 0.15 m/s for gait speed estimation. Furthermore, the proposed machine learning-based locomotion detection method had a median of 96.8% specificity, 93.0% sensitivity, 96.4% accuracy, and 78.6% F1-score in detecting walking bouts at home. The high performance of the proposed algorithm showed the feasibility of the unsupervised mobility assessment introduced in this study.

Quantitative Comparison of Human and Software Reliability in the Categorization of Sit-to-Stand Motion Pattern

The Sit-to-Stand (STS) test is used in clinical practice as an indicator of lower-limb functionality decline, especially for older adults. Due to its high variability, there is no standard approach for categorising the STS movement and recognising its motion pattern. This paper presents a comparative analysis between visual assessments and an automated-software for the categorisation of STS, relying on registrations from a force plate. 5 participants (30 ± 6 years) took part in 2 different sessions of visual inspections on 200 STS movements under self-paced and controlled speed conditions. Assessors were asked to identify three specific STS events from the Ground Reaction Force, simultaneously with the software analysis: the start of the trunk movement (Initiation), the beginning of the stable upright stance (Standing) and the sitting movement (Sitting). The absolute agreement between the repeated raters' assessments as well as between the raters' and software's assessment in the first trial, were considered as indexes of human and software performance, respectively. No statistical differences between methods were found for the identification of the Initiation and the Sitting events at self-paced speed and for only the Sitting event at controlled speed. The estimated significant values of maximum discrepancy between visual and automated assessments were 0.200 [0.039; 0.361] s in unconstrained conditions and 0.340 [0.014; 0.666] s for standardised movements. The software assessments displayed an overall good agreement against visual evaluations of the Ground Reaction Force, relying, at the same time, on objective measures.

An Experimental Study on the Validity and Reliability of a Smartphone Application to Acquire Temporal Variables during the Single Sit-to-Stand Test with Older Adults

Smartphone sensors have often been proposed as pervasive measurement systems to assess mobility in older adults due to their ease of use and low-cost. This study analyzes a smartphone-based application’s validity and reliability to quantify temporal variables during the single sit-to-stand test with institutionalized older adults. Forty older adults (20 women and 20 men; 78.9 ± 8.6 years) volunteered to participate in this study. All participants performed the single sit-to-stand test. Each sit-to-stand repetition was performed after an acoustic signal was emitted by the smartphone app. All data were acquired simultaneously with a smartphone and a digital video camera. The measured temporal variables were stand-up time and total time. The relative reliability and systematic bias inter-device were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. In contrast, absolute reliability was assessed using the standard error of measurement and coefficient of variation (CV). Inter-device concurrent validity was assessed through correlation analysis. The absolute percent error (APE) and the accuracy were also calculated. The results showed excellent reliability (ICC = 0.92–0.97; CV = 1.85–3.03) and very strong relationships inter-devices for the stand-up time (r = 0.94) and the total time (r = 0.98). The APE was lower than 6%, and the accuracy was higher than 94%. Based on our data, the findings suggest that the smartphone application is valid and reliable to collect the stand-up time and total time during the single sit-to-stand test with older adults.