Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances

A Large Harmonized Upper and Lower Limb Accelerometry Dataset: A Resource for Rehabilitation Scientists

Wearable sensors can measure movement in daily life, an outcome that is salient to patients, and have been critical to accelerating progress in rehabilitation research and practice. However, collecting and processing sensor data is burdensome, leaving many scientists with limited access to such data. To address these challenges, we present a harmonized, wearable sensor dataset that combines 2,885 recording days of sensor data from the upper and lower limbs from eight studies. The dataset includes 790 individuals ages 0 - 90, nearly equal sex proportions (53% male, 47% female), and representation from a range of demographic backgrounds (69.4% White, 24.9% Black, 1.8% Asian) and clinical conditions (46% neurotypical, 31% stroke, 7% Parkinson's disease, 6% orthopedic conditions, and others). The dataset is publicly available and accompanied by open source code and an app that allows for interaction with the data. This dataset will facilitate the use of sensor data to advance rehabilitation research and practice, improve the reproducibility and replicability of wearable sensor studies, and minimize costs and duplicated scientific efforts.

Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study

BACKGROUND

Activities of daily living (ADL) are essential for independence and personal well-being, reflecting an individual's functional status. Impairment in executing these tasks can limit autonomy and negatively affect quality of life. The assessment of physical function during ADL is crucial for the prevention and rehabilitation of movement limitations. Still, its traditional evaluation based on subjective observation has limitations in precision and objectivity.

OBJECTIVE

The primary objective of this study is to use innovative technology, specifically wearable inertial sensors combined with artificial intelligence techniques, to objectively and accurately evaluate human performance in ADL. It is proposed to overcome the limitations of traditional methods by implementing systems that allow dynamic and noninvasive monitoring of movements during daily activities. The approach seeks to provide an effective tool for the early detection of dysfunctions and the personalization of treatment and rehabilitation plans, thus promoting an improvement in the quality of life of individuals.

METHODS

To monitor movements, wearable inertial sensors were developed, which include accelerometers and triaxial gyroscopes. The developed sensors were used to create a proprietary database with 6 movements related to the shoulder and 3 related to the back. We registered 53,165 activity records in the database (consisting of accelerometer and gyroscope measurements), which were reduced to 52,600 after processing to remove null or abnormal values. Finally, 4 deep learning (DL) models were created by combining various processing layers to explore different approaches in ADL recognition.

RESULTS

The results revealed high performance of the 4 proposed models, with levels of accuracy, precision, recall, and F1-score ranging between 95% and 97% for all classes and an average loss of 0.10. These results indicate the great capacity of the models to accurately identify a variety of activities, with a good balance between precision and recall. Both the convolutional and bidirectional approaches achieved slightly superior results, although the bidirectional model reached convergence in a smaller number of epochs.

CONCLUSIONS

The DL models implemented have demonstrated solid performance, indicating an effective ability to identify and classify various daily activities related to the shoulder and lumbar region. These results were achieved with minimal sensorization-being noninvasive and practically imperceptible to the user-which does not affect their daily routine and promotes acceptance and adherence to continuous monitoring, thus improving the reliability of the data collected. This research has the potential to have a significant impact on the clinical evaluation and rehabilitation of patients with movement limitations, by providing an objective and advanced tool to detect key movement patterns and joint dysfunctions.

Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation

OBJECTIVE

Recent deep learning techniques hold promise to enable IMU-driven kinetic assessment; however, they require large extents of ground reaction force (GRF) data to serve as labels for supervised model training. We thus propose using existing self-supervised learning (SSL) techniques to leverage large IMU datasets to pre-train deep learning models, which can improve the accuracy and data efficiency of IMU-based GRF estimation.

METHODS

We performed SSL by masking a random portion of the input IMU data and training a transformer model to reconstruct the masked portion. We systematically compared a series of masking ratios across three pre-training datasets that included real IMU data, synthetic IMU data, or a combination of the two. Finally, we built models that used pre-training and labeled data to estimate GRF during three prediction tasks: overground walking, treadmill walking, and drop landing.

RESULTS

When using the same amount of labeled data, SSL pre-training significantly improved the accuracy of 3-axis GRF estimation during walking compared to baseline models trained by conventional supervised learning. Fine-tuning SSL model with 1-10% of walking data yielded comparable accuracy to training baseline model with 100% of walking data. The optimal masking ratio for SSL is 6.25-12.5%.

CONCLUSION

SSL leveraged large real and synthetic IMU datasets to increase the accuracy and data efficiency of deep-learning-based GRF estimation, reducing the need for labeled data.

SIGNIFICANCE

This work, with its open-source code and models, may unlock broader use cases of IMU-driven kinetic assessment by mitigating the scarcity of GRF measurements in practical applications.

Research on Monitoring Assistive Devices for Rehabilitation of Movement Disorders through Multi-Sensor Analysis Combined with Deep Learning

This study aims to integrate a convolutional neural network (CNN) and the Random Forest Model into a rehabilitation assessment device to provide a comprehensive gait analysis in the evaluation of movement disorders to help physicians evaluate rehabilitation progress by distinguishing gait characteristics under different walking modes. Equipped with accelerometers and six-axis force sensors, the device monitors body symmetry and upper limb strength during rehabilitation. Data were collected from normal and abnormal walking groups. A knee joint limiter was applied to subjects to simulate different levels of movement disorders. Features were extracted from the collected data and analyzed using a CNN. The overall performance was scored with Random Forest Model weights. Significant differences in average acceleration values between the moderately abnormal (MA) and severely abnormal (SA) groups (without vehicle assistance) were observed (p < 0.05), whereas no significant differences were found between the MA with vehicle assistance (MA-V) and SA with vehicle assistance (SA-V) groups (p > 0.05). Force sensor data showed good concentration in the normal walking group and more scatter in the SA-V group. The CNN and Random Forest Model accurately recognized gait conditions, achieving average accuracies of 88.4% and 92.3%, respectively, proving that the method mentioned above provides more accurate gait evaluations for patients with movement disorders.

Quantifying the effects of sleep on sensor-derived variables from upper limb accelerometry in people with and without upper limb impairment

BACKGROUND

Despite the promise of wearable sensors for both rehabilitation research and clinical care, these technologies pose significant burden on data collectors and analysts. Investigations of factors that may influence the wearable sensor data processing pipeline are needed to support continued use of these technologies in rehabilitation research and integration into clinical care settings. The purpose of this study was to investigate the effect of one such factor, sleep, on sensor-derived variables from upper limb accelerometry in people with and without upper limb impairment and across a two-day wearing period.

METHODS

This was a secondary analysis of data collected during a prospective, longitudinal cohort study (n = 127 individuals, 62 with upper limb impairment and 65 without). Participants wore a wearable sensor on each wrist for 48 h. Five upper limb sensor variables were calculated over the full wear period (sleep included) and with sleep time removed (sleep excluded): preferred time, non-preferred time, use ratio, non-preferred magnitude and its standard deviation. Linear mixed effects regression was used to quantify the effect of sleep on each sensor variable and determine if the effect differed between people with and without upper limb impairment and across a two-day wearing period.

RESULTS

There were significant differences between sleep included and excluded for the variables preferred time (p < 0.001), non-preferred time (p < 0.001), and non-preferred magnitude standard deviation (p = 0.001). The effect of sleep was significantly different between people with and without upper limb impairment for one variable, non-preferred magnitude (p = 0.02). The effect of sleep was not substantially different across wearing days for any of the variables.

CONCLUSIONS

Overall, the effects of sleep on sensor-derived variables of upper limb accelerometry are small, similar between people with and without upper limb impairment and across a two-day wearing period, and can likely be ignored in most contexts. Ignoring the effect of sleep would simplify the data processing pipeline, facilitating the use of wearable sensors in both research and clinical practice.

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.

Requirements for home-based upper extremity rehabilitation using wearable motion sensors for stroke patients: a user-centred approach

PURPOSE

Telerehabilitation systems have the potential to enable therapists to monitor and assist stroke patients in achieving high-intensity upper extremity exercise in the home environment. We adopted an iterative user-centred approach, including multiple data sources and meetings with end-users and stakeholders to define the user requirements for home-based upper extremity rehabilitation using wearable motion sensors for subacute stroke patients.

METHODS

We performed a requirement analysis consisting of the following steps: 1) context & groundwork; 2) eliciting requirements; 3) modelling & analysis; 4) agreeing requirements. During these steps, a pragmatic literature search, interviews and focus groups with stroke patients, physiotherapists and occupational therapists were performed. The results were systematically analysed and prioritised into "must-haves", "should-haves", and "could-haves".

RESULTS

We formulated 33 functional requirements: eighteen must-have requirements related to blended care (2), exercise principles (7), exercise delivery (3), exercise evaluation (4), and usability (2); ten should-haves; and five could-haves. Six movement components, including twelve exercises and five combination exercises, are required. For each exercise, appropriate exercise measures were defined.

CONCLUSION

This study provides an overview of functional requirements, required exercises, and required exercise measures for home-based upper extremity rehabilitation using wearable motion sensors for stroke patients, which can be used to develop home-based upper extremity rehabilitation interventions. Moreover, the comprehensive and systematic requirement analysis used in this study can be applied by other researchers and developers when extracting requirements for designing a system or intervention in a medical context.

Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices

Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.

Janus CoMOF-SEBS Membrane for Bifunctional Dielectric Layer in Triboelectric Nanogenerators

Considerable research has been conducted on the application of functional nano-fillers to enhance the power generation capabilities of triboelectric nanogenerators (TENGs). However, these additives often exhibit a decrease in output power at higher concentration. Here, a Janus cobalt metal-organic framework-SEBS (JCMS) membrane is reported as a dual-purpose dielectric layer capable of efficiently capturing and blocking charges for high-performance TENGs. The JCMS is produced asymmetrically through gravitational sedimentation, employing spherical CoMOFs within a diluted SEBS solution. Beyond its dual dielectric characteristics, the JCMS showcases exceptional mechanical durability, displaying notable stretchability of up to 475% and remarkable resilience when subjected to diverse mechanical pressures. Consequently, the JCMS-TENG produces a maximum peak-to-peak voltage of 936 V, a current of 42.8 µA, and a power density of 10.89 W m- 2 when exposed to an external force of 10 N at a 5 Hz frequency. This investigation highlights the potential of JCMS-TENGs with unique structures, known for their exceptional energy harvesting capabilities, mechanical strength, and flexibility. Additionally, the promising prospects of easily produced asymmetric structures is emphasized with bifunctionalities for developing efficient and flexible MOFs-based TENGs. These advancements are well-suited for self-powered wearables, rehabilitation devices, and energy harvesters.

Lumbo-Pelvic Rhythm Monitoring Using Wearable Technology with Sensory Biofeedback: A Systematic Review

As an essential lower-back movement pattern, lumbo-pelvic rhythm (LPR) during forward trunk flexion and backward return has been investigated on a large scale. It has been suggested that abnormalities in lumbo-pelvic coordination are related to the risk of developing low back disorders. However, considerable differences in the approaches used to monitor LPR make it challenging to integrate findings from those investigations for future research. Therefore, the aim of this systematic review was to summarize the use of wearable technology for kinematic measurement with sensory biofeedback for LPR monitoring by assessing these technologies' specific capabilities and biofeedback capacities and exploring their practical viability based on sensor outcomes. The review was developed following the PRISMA guidelines, and the risk of bias was analyzed using the PREDro and STROBE scales. PubMed, Web of Science, Scopus, and IEEEXPLORE databases were searched for relevant studies, initially returning a total of 528 articles. Finally, we included eight articles featuring wearable devices with audio or vibration biofeedback. Differences in protocols and limitations were also observed. This novel study presents a review of wearable tracking devices for LPR motion-mediated biofeedback for the purpose of correcting lower back posture. More research is needed to determine the long-term effectiveness of these devices, as well as their most appropriate corresponding methodologies.

Upper limb assessment with inertial measurement units according to the international classification of functioning in stroke: a systematic review and correlation meta-analysis

OBJECTIVE

To investigate the usefulness of inertial measurement units (IMUs) in the assessment of motor function of the upper limb (UL) in accordance with the international classification of functioning (ICF).

DATA SOURCES

PubMed; Scopus; Embase; WoS and PEDro databases were searched from inception to 1 February 2022.

METHODS

The current systematic review follows PRISMA recommendations. Articles including IMU assessment of UL in stroke individuals have been included and divided into four ICF categories (b710, b735, b760, d445). We used correlation meta-analysis to pool the Fisher Z-score of each correlation between kinematics and clinical assessment.

RESULTS

A total of 35 articles, involving 475 patients, met the inclusion criteria. In the included studies, IMUs have been employed to assess the mobility of joint functions (n = 6), muscle tone functions (n = 4), control of voluntary movement functions (n = 15), and hand and arm use (n = 15). A significant correlation was found in overall meta-analysis based on 10 studies, involving 213 subjects: (r = 0.69) (95% CI: 0.69/0.98; p < 0.001) as in the d445 (r = 0.71) and b760 (r = 0.64) ICF domains, with no heterogeneity across the studies.

CONCLUSION

The literature supports the integration of IMUs and conventional clinical assessment in functional evaluation of the UL after a stroke. The use of a limited number of wearable sensors can provide additional kinematic features of UL in all investigated ICF domains, especially in the ADL tasks when a strong correlation with clinical evaluation was found.

Sonification for Personalised Gait Intervention

Mobility challenges threaten physical independence and good quality of life. Often, mobility can be improved through gait rehabilitation and specifically the use of cueing through prescribed auditory, visual, and/or tactile cues. Each has shown use to rectify abnormal gait patterns, improving mobility. Yet, a limitation remains, i.e., long-term engagement with cueing modalities. A paradigm shift towards personalised cueing approaches, considering an individual's unique physiological condition, may bring a contemporary approach to ensure longitudinal and continuous engagement. Sonification could be a useful auditory cueing technique when integrated within personalised approaches to gait rehabilitation systems. Previously, sonification demonstrated encouraging results, notably in reducing freezing-of-gait, mitigating spatial variability, and bolstering gait consistency in people with Parkinson's disease (PD). Specifically, sonification through the manipulation of acoustic features paired with the application of advanced audio processing techniques (e.g., time-stretching) enable auditory cueing interventions to be tailored and enhanced. These methods used in conjunction optimize gait characteristics and subsequently improve mobility, enhancing the effectiveness of the intervention. The aim of this narrative review is to further understand and unlock the potential of sonification as a pivotal tool in auditory cueing for gait rehabilitation, while highlighting that continued clinical research is needed to ensure comfort and desirability of use.

REHABS: An Innovative and User-Friendly Device for Rehabilitation

Rehabilitation is a complex set of interventions involving the assessment, management, and treatment of injuries. It aims to support and facilitate an individual's recovery process by restoring a physiological function, e.g., limb movement, compromised by physical impairments, injuries or diseases to a condition as close to normal as possible. Innovative devices and solutions make the rehabilitation process of patients easier during their daily activities. Devices support physicians and physiotherapists in monitoring and measuring patients' physical improvements during rehabilitation. In this context, we report the design and implementation of a low-cost rehabilitation system, which is a programmable device designed to support tele-rehabilitation of the upper limbs. The proposed system includes a mechanism to acquire and analyze data and signals related to rehabilitation processes.

Applications of wearable sensors in upper extremity MSK conditions: a scoping review

PURPOSE

This scoping review uniquely aims to map the current state of the literature on the applications of wearable sensors in people with or at risk of developing upper extremity musculoskeletal (UE-MSK) conditions, considering that MSK conditions or disorders have the highest rate of prevalence among other types of conditions or disorders that contribute to the need for rehabilitation services.

MATERIALS AND METHODS

The preferred reporting items for systematic reviews and meta-analysis (PRISMA) extension for scoping reviews guideline was followed in this scoping review. Two independent authors conducted a systematic search of four databases, including PubMed, Embase, Scopus, and IEEEXplore. We included studies that have applied wearable sensors on people with or at risk of developing UE-MSK condition published after 2010. We extracted study designs, aims, number of participants, sensor placement locations, sensor types, and number, and outcome(s) of interest from the included studies. The overall findings of our scoping review are presented in tables and diagrams to map an overview of the existing applications.

RESULTS

The final review encompassed 80 studies categorized into clinical population (31 studies), workers' population (31 studies), and general wearable design/performance studies (18 studies). Most were observational, with 2 RCTs in workers' studies. Clinical studies focused on UE-MSK conditions like rotator cuff tear and arthritis. Workers' studies involved industrial workers, surgeons, farmers, and at-risk healthy individuals. Wearable sensors were utilized for objective motion assessment, home-based rehabilitation monitoring, daily activity recording, physical risk characterization, and ergonomic assessments. IMU sensors were prevalent in designs (84%), with a minority including sEMG sensors (16%). Assessment applications dominated (80%), while treatment-focused studies constituted 20%. Home-based applicability was noted in 21% of the studies.

CONCLUSION

Wearable sensor technologies have been increasingly applied to the health care field. These applications include clinical assessments, home-based treatments of MSK disorders, and monitoring of workers' population in non-standardized areas such as work environments. Assessment-focused studies predominate over treatment studies. Additionally, wearable sensor designs predominantly use IMU sensors, with a subset of studies incorporating sEMG and other sensor types in wearable platforms to capture muscle activity and inertial data for the assessment or rehabilitation of MSK conditions.

Exo Supportive Devices: Summary of Technical Aspects

Human societies have been trying to mitigate the suffering of individuals with physical impairments, with a special effort in the last century. In the 1950s, a new concept arose, finding similarities between animal exoskeletons, and with the goal of medically aiding human movement (for rehabilitation applications). There have been several studies on using exosuits with this purpose in mind. So, the current review offers a critical perspective and a detailed analysis of the steps and key decisions involved in the conception of an exoskeleton. Choices such as design aspects, base materials (structure), actuators (force and motion), energy sources (actuation), and control systems will be discussed, pointing out their advantages and disadvantages. Moreover, examples of exosuits (full-body, upper-body, and lower-body devices) will be presented and described, including their use cases and outcomes. The future of exoskeletons as possible assisted movement solutions will be discussed-pointing to the best options for rehabilitation.

The Role and Importance of Using Sensor-Based Devices in Medical Rehabilitation: A Literature Review on the New Therapeutic Approaches

Due to the growth of sensor technology, more affordable integrated circuits, and connectivity technologies, the usage of wearable equipment and sensing devices for monitoring physical activities, whether for wellness, sports monitoring, or medical rehabilitation, has exploded. The current literature review was performed between October 2022 and February 2023 using PubMed, Web of Science, and Scopus in accordance with P.R.I.S.M.A. criteria. The screening phase resulted in the exclusion of 69 articles that did not fit the themes developed in all subchapters of the study, 41 articles that dealt exclusively with rehabilitation and orthopaedics, 28 articles whose abstracts were not visible, and 10 articles that dealt exclusively with other sensor-based devices and not medical ones; the inclusion phase resulted in the inclusion of 111 articles. Patients who utilise sensor-based devices have several advantages due to rehabilitating a missing component, which marks the accomplishment of a fundamental goal within the rehabilitation program. As technology moves faster and faster forward, the field of medical rehabilitation has to adapt to the time we live in by using technology and intelligent devices. This means changing every part of rehabilitation and finding the most valuable and helpful gadgets that can be used to regain lost functions, keep people healthy, or prevent diseases.

Metaverse Wearables for Immersive Digital Healthcare: A Review

The recent exponential growth of metaverse technology has been instrumental in reshaping a myriad of sectors, not least digital healthcare. This comprehensive review critically examines the landscape and future applications of metaverse wearables toward immersive digital healthcare. The key technologies and advancements that have spearheaded the metamorphosis of metaverse wearables are categorized, encapsulating all-encompassed extended reality, such as virtual reality, augmented reality, mixed reality, and other haptic feedback systems. Moreover, the fundamentals of their deployment in assistive healthcare (especially for rehabilitation), medical and nursing education, and remote patient management and treatment are investigated. The potential benefits of integrating metaverse wearables into healthcare paradigms are multifold, encompassing improved patient prognosis, enhanced accessibility to high-quality care, and high standards of practitioner instruction. Nevertheless, these technologies are not without their inherent challenges and untapped opportunities, which span privacy protection, data safeguarding, and innovation in artificial intelligence. In summary, future research trajectories and potential advancements to circumvent these hurdles are also discussed, further augmenting the incorporation of metaverse wearables within healthcare infrastructures in the post-pandemic era.

Spatiotemporal parameters from remote smartphone-based gait analysis are associated with lower extremity functional scale categories

Objective

Self-report tools are recommended in research and clinical practice to capture individual perceptions regarding health status; however, only modest correlations are found with performance-based results. The Lower Extremity Functional Scale (LEFS) is one well-validated measure of impairment affecting physical activities that has been compared with objective tests. More recently, mobile gait assessment software can provide comprehensive motion tracking output from ecologically valid environments, but how this data relates to subjective scales is unknown. Therefore, the association between the LEFS and walking variables remotely collected by a smartphone was explored.

Methods

Proprietary algorithms extracted spatiotemporal parameters detected by a standard integrated inertial measurement unit from 132 subjects enrolled in physical therapy for orthopedic or neurological rehabilitation. Users initiated ambulation recordings and completed questionnaires through the OneStep digital platform. Discrete categories were created based on LEFS score cut-offs and Analysis of Variance was applied to estimate the difference in gait metrics across functional groups (Low-Medium-High).

Results

The main finding of this cross-sectional retrospective study is that remotely-collected biomechanical walking data are significantly associated with individuals' self-evaluated function as defined by LEFS categorization (n = 132) and many variables differ between groups. Velocity was found to have the strongest effect size.

Discussion

When patients are classified according to subjective mobility level, there are significant differences in quantitative measures of ambulation analyzed with smartphone-based technology. Capturing real-time information about movement is important to obtain accurate impressions of how individuals perform in daily life while understanding the relationship between enacted activity and relevant clinical outcomes.

A Sensor-Based Feedback Device Stimulating Daily Life Upper Extremity Activity in Stroke Patients: A Feasibility Study

This study aims to evaluate the feasibility and explore the efficacy of the Arm Activity Tracker (AAT). The AAT is a device based on wrist-worn accelerometers that provides visual and tactile feedback to stimulate daily life upper extremity (UE) activity in stroke patients.

METHODS

A randomised, crossover within-subject study was conducted in sub-acute stroke patients admitted to a rehabilitation centre. Feasibility encompassed (1) adherence: the dropout rate and the number of participants with insufficient AAT data collection; (2) acceptance: the technology acceptance model (range: 7-112) and (3) usability: the system usability scale (range: 0-100). A two-way ANOVA was used to estimate the difference between the baseline, intervention and control conditions for (1) paretic UE activity and (2) UE activity ratio.

RESULTS

Seventeen stroke patients were included. A 29% dropout rate was observed, and two participants had insufficient data collection. Participants who adhered to the study reported good acceptance (median (IQR): 94 (77-111)) and usability (median (IQR): 77.5 (75-78.5)-). We found small to medium effect sizes favouring the intervention condition for paretic UE activity (η²G = 0.07, p = 0.04) and ratio (η²G = 0.11, p = 0.22).

CONCLUSION

Participants who adhered to the study showed good acceptance and usability of the AAT and increased paretic UE activity. Dropouts should be further evaluated, and a sufficiently powered trial should be performed to analyse efficacy.

Recent progress in flexible micro-pressure sensors for wearable health monitoring

In recent years, flexible micro-pressure sensors have been used widely in wearable health monitoring applications due to their excellent flexibility, stretchability, non-invasiveness, comfort wearing and real-time detection. According to the working mechanism of the flexible micro-pressure sensor, it can be classified as piezoresistive, piezoelectric, capacitive and triboelectric types. Herein, an overview of flexible micro-pressure sensors for wearable health monitoring is presented. The physiological signaling and body motions contain a lot of health status information. Thus, this review focuses on the applications of flexible micro-pressure sensors in these fields. Additionally, the contents of sensing mechanism, sensing materials and performance of flexible micro-pressure sensors are introduced in detail. Finally, we predict the future research directions of the flexible micro-pressure sensors, and discuss the challenges in practical applications.

Ultralow-Latency Textile Sensors for Wearable Interfaces with a Human-in-Loop Sensing Approach

The evolution of wearable technologies has led to the development of novel types of sensors customized for a wide range of applications. Wearable sensors need to possess a low form factor and be ergonomic, causing minimal impediment of the user's natural movement. Various principles have been explored to meet these requirements, ranging from optical, magnetic, resistive flex sensing to 3D printed sensors and liquid metals such as those using eutectic gallium-indium. However, manufacturing techniques for most current wearable sensors tend to be complex and difficult to scale. Challenges also exist in achieving high sensitivity with noise resistance and robustness to false detections, especially in capacitive sensors. In this research, a novel ultralow-latency soft tactile and pressure sensor developed using off-the-shelf e-textiles is proposed, which overcomes some of these limitations. The sensor does not use any specialized equipment or materials for manufacture. A human-in-loop (HIL) sensing technique is demonstrated, which provides high sensitivity, high sensing bandwidth, as well as ultralow latency, which makes it ideal as a wearable input device. In addition, the HIL method provides other advantages such as high noise rejection and resistance to accidental triggers that could be caused by other humans or environmental factors owing to its high signal to noise ratio. Finally, two applications-a wearable keyboard and gaming input device-were demonstrated using these sensors.

Immediate improvements in post-stroke gait biomechanics are induced with both real-time limb position and propulsive force biofeedback

BACKGROUND

Paretic propulsion [measured as anteriorly-directed ground reaction forces (AGRF)] and trailing limb angle (TLA) show robust inter-relationships, and represent two key modifiable post-stroke gait variables that have biomechanical and clinical relevance. Our recent work demonstrated that real-time biofeedback is a feasible paradigm for modulating AGRF and TLA in able-bodied participants. However, the effects of TLA biofeedback on gait biomechanics of post-stroke individuals are poorly understood. Thus, our objective was to investigate the effects of unilateral, real-time, audiovisual TLA versus AGRF biofeedback on gait biomechanics in post-stroke individuals.

METHODS

Nine post-stroke individuals (6 males, age 63 ± 9.8 years, 44.9 months post-stroke) participated in a single session of gait analysis comprised of three types of walking trials: no biofeedback, AGRF biofeedback, and TLA biofeedback. Biofeedback unilaterally targeted deficits on the paretic limb. Dependent variables included peak AGRF, TLA, and ankle plantarflexor moment. One-way repeated measures ANOVA with Bonferroni-corrected post-hoc comparisons were conducted to detect the effect of biofeedback on gait biomechanics variables.

RESULTS

Compared to no-biofeedback, both AGRF and TLA biofeedback induced unilateral increases in paretic AGRF. TLA biofeedback induced significantly larger increases in paretic TLA than AGRF biofeedback. AGRF biofeedback increased ankle moment, and both feedback conditions increased non-paretic step length. Both types of biofeedback specifically targeted the paretic limb without inducing changes in the non-paretic limb.

CONCLUSIONS

By showing comparable increases in paretic limb gait biomechanics in response to both TLA and AGRF biofeedback, our novel findings provide the rationale and feasibility of paretic TLA as a gait biofeedback target for post-stroke individuals. Additionally, our results provide preliminary insights into divergent biomechanical mechanisms underlying improvements in post-stroke gait induced by these two biofeedback targets. We lay the groundwork for future investigations incorporating greater dosages and longer-term therapeutic effects of TLA biofeedback as a stroke gait rehabilitation strategy. Trial registration NCT03466372.

A Proof of Concept Combined Using Mixed Reality for Personalized Neurorehabilitation of Cerebellar Ataxic Patients

BACKGROUND

Guidelines for degenerative cerebellar ataxia neurorehabilitation suggest intensive coordinative training based on physiotherapeutic exercises. Scientific studies demonstrate virtual exergaming therapeutic value. However, patient-based personalization, post processing analyses and specific audio-visual feedbacks are not provided. This paper presents a wearable motion tracking system with recording and playback features. This system has been specifically designed for ataxic patients, for upper limbs coordination studies with the aim to retrain movement in a neurorehabilitation setting. Suggestions from neurologists and ataxia patients were considered to overcome the shortcomings of virtual systems and implement exergaming.

METHODS

The system consists of the mixed-reality headset Hololens2 and a proprietary exergaming implemented in Unity. Hololens2 can track and save upper limb parameters, head position and gaze direction in runtime.

RESULTS

Data collected from a healthy subject are reported to demonstrate features and outputs of the system.

CONCLUSIONS

Although further improvements and validations are needed, the system meets the needs of a dynamic patient-based exergaming for patients with cerebellar ataxia. Compared with existing solutions, the mixed-reality system is designed to provide an effective and safe therapeutic exergaming that supports both primary and secondary goals of an exergaming: what a patient should do and how patient actions should be performed.

Sensor based assessment of turning during instrumented Timed Up and Go Test for quantifying mobility in chronic stroke patients

BACKGROUND

Turning may be particularly challenging for stroke patients leading to decreased mobility and increased functional restriction. Timed up and go instrumentation using a simple technology in the clinical context could allow for the collection of both traditional and potentially more discriminatory variables in turning ability.

AIM

Determine whether the speed turning metrics obtained by a single inertial sensor are suitable for differentiating between stroke patients with varying levels of mobility and disability.

DESIGN

Cross-sectional study.

SETTING

Outpatients setting.

POPULATION

Chronic stroke patients.

METHODS

A total of 48 chronic stroke patients and 23 healthy controls were included. Stroke patients were divided in two groups based on the total iTUG score: an impaired mobility (> 20 seconds) and an available mobility (<20 seconds) group. All subjects performed an instrumented Timed Up and Go (iTUG) wearing a single IMU sensor on the lower back. Time of subcomponents of the timed up and go test and kinematic parameters of turning were quantified. Other clinical outcomes were: 10 meters walk test, Functional Ambulation Categories Scale (FAC), the Rivermead Mobility Index (RMI), Modified Rankin Scale and the Saltin-Grimby Physical Activity Level Scale (SGPALS).

RESULTS

There were significant differences (P<0.01) in iTUG phases and turning speeds among groups. Low to strong significant correlations were found between measures derived from the turning speeds and clinical measures. The area under the curve (AUC) of Receiver Operating Characteristic (ROC) turning speeds was demonstrated to be able to discriminate (AUC: 0.742-0.912) from available to impaired stroke patients.

CONCLUSIONS

This study provides evidence that turning speeds during timed up and go test are accurate measures of mobility and capable of discriminating stroke patients with impaired mobility from those with normal mobility.

CLINICAL REHABILITATION IMPACT

The turning metrics are related to impairment and mobility in chronic stroke patients; hence they are important to include during clinical evaluation and may assist in creating a customized strategy, assess potential treatments, and effectively organize recovery.

A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.

Comparing a Sensor for Movement Assessment with Traditional Physiotherapeutic Assessment Methods in Patients after Knee Surgery-A Method Comparison and Reproducibility Study

Wearable sensors offer the opportunity for patients to perform a self-assessment of their function with respect to a variety of movement exercises. Corresponding commercial products have the potential to change the communication between patients and physiotherapists during the recovery process. Even if they turn out to be user-friendly, there remains the question to what degree the numerical results are reliable and comparable with those obtained by assessment methods traditionally used. To address this question for one specific recently developed and commercially available sensor, a method comparison study was performed. The sensor-based assessment of eight movement parameters was compared with an assessment of the same parameters based on test procedures traditionally used. Thirty-three patients recovering after arthroscopic knee surgery participated in the study. The whole assessment procedure was repeated. Reproducibility and agreement were quantified by the intra class correlation coefficient. The height of a one-leg vertical jump and the number of side hops showed high agreement between the two modalities and high reproducibility (ICC > 0.85). Due to differences in the set-up of the assessment, agreement could not be achieved for three mobility parameters, but even the correlation was only fair (r < 0.5). Knee stability showed poor agreement. Consequently, the use of the sensor can currently only be recommended for selected parameters. The variation in degree of agreement and reproducibility across different parameters clearly indicate the need for developing corresponding guidance for each new sensor put onto the market.

Telerehabilitation in Older Thai Community-Dwelling Adults

To investigate the impact on physical performance and walking abilities associated with fall risk and disability in the senior population, we created a telerehabilitation system. This is a multi-site, community setting, pre−post experimental study. We recruited participants from four rural areas in Thailand. All participants received eight weeks of tele-exercise, three sessions per week, via the telerehabilitation system. After the intervention, all participants underwent the Short Physical Performance Battery (SPPB), Timed Up and Go (TUG) test, and the six-minute walk test (6MWT) using a wearable sensor system. A total of 123 participants participated in the study and 2 participants dropped out while conducting the study, thus 121 participants were included in the analysis. In comparison to the baseline, we discovered a considerable improvement in the SPPB score (0.65 ± 0.22, p < 0.001), TUG (−1.70 ± 0.86, p < 0.001), and 6MWT (10.23 ± 7.33, p = 0.007). Our study demonstrates the benefits of telerehabilitation on SPPB, TUG, and 6MWT related to disabilities and fall risk. This telerehabilitation technology demonstrated its viability in the community environment and demonstrated its capacity to offer fundamental components of remote rehabilitation services within the healthcare system.

Past, Present and Future of Research on Wearable Technologies for Healthcare: A Bibliometric Analysis Using Scopus

Currently, wearable technology is present in different fields that aim to satisfy our needs in daily life, including the improvement of our health in general, the monitoring of patient health, ensuring the safety of people in the workplace or supporting athlete training. The objective of this bibliometric analysis is to examine and map the scientific advances in wearable technologies in healthcare, as well as to identify future challenges within this field and put forward some proposals to address them. In order to achieve this objective, a search of the most recent related literature was carried out in the Scopus database. Our results show that the research can be divided into two periods: before 2013, it focused on design and development of sensors and wearable systems from an engineering perspective and, since 2013, it has focused on the application of this technology to monitoring health and well-being in general, and in alignment with the Sustainable Development Goals wherever feasible. Our results reveal that the United States has been the country with the highest publication rates, with 208 articles (34.7%). The University of California, Los Angeles, is the institution with the most studies on this topic, 19 (3.1%). Sensors journal (Switzerland) is the platform with the most studies on the subject, 51 (8.5%), and has one of the highest citation rates, 1461. We put forward an analysis of keywords and, more specifically, a pennant chart to illustrate the trends in this field of research, prioritizing the area of data collection through wearable sensors, smart clothing and other forms of discrete collection of physiological data.

Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions

The progressive loss of functional capacity due to aging is a serious problem that can compromise human locomotion capacity, requiring the help of an assistant and reducing independence. The NanoStim project aims to develop a system capable of performing treatment with electrostimulation at the patient's home, reducing the number of consultations. The knee angle is one of the essential attributes in this context, helping understand the patient's movement during the treatment session. This article presents a wearable system that recognizes the knee angle through IMU sensors. The hardware chosen for the wearables are low cost, including an ESP32 microcontroller and an MPU-6050 sensor. However, this hardware impairs signal accuracy in the multitasking environment expected in rehabilitation treatment. Three optimization filters with algorithmic complexity O(1) were tested to improve the signal's noise. The complementary filter obtained the best result, presenting an average error of 0.6 degrees and an improvement of 77% in MSE. Furthermore, an interface in the mobile app was developed to respond immediately to the recognized movement. The systems were tested with volunteers in a real environment and could successfully measure the movement performed. In the future, it is planned to use the recognized angle with the electromyography sensor.

Pedometers and Accelerometers in Multiple Sclerosis: Current and New Applications

Pedometers and accelerometers have become commonplace for the assessment of physical behaviors (e.g., physical activity and sedentary behavior) in multiple sclerosis (MS) research. Current common applications include the measurement of steps taken and the classification of physical activity intensity, as well as sedentary behavior, using cut-points methods. The existing knowledge and applications, coupled with technological advances, have spawned new opportunities for using those motion sensors in persons with MS, and these include the utilization of the data as biomarkers of disease severity and progression, perhaps in clinical practice. Herein, we discuss the current state of knowledge on the validity and applications of pedometers and accelerometers in MS, as well as new opportunities and strategies for the improved assessment of physical behaviors and disease progression, and consequently, personalized care.

Innovation: It's in Our DNA

Colleen M. Kigin, PT, DPT, MS, MPA, FAPTA, the 52nd Mary McMillan Lecturer, is a consultant focused on innovation. She is a visiting clinical professor at the University of Colorado physical therapy program, University of Colorado School of Medicine, and an adjunct associate professor at the MGH Institute of Health Professions (MGH IHP). From 1998-2014, she held the positions of chief of staff and program manager for the Center of Integration of Medicine and Innovative Technology, a 12-institution consortium based in Boston, Massachusetts, developing innovative solutions to health care problems. She subsequently has served as a consultant to such efforts as the University of Manchester, Manchester Academic Health Science Centre, United Kingdom, to develop an innovation culture. In 1994, she joined the newly formed Partners HealthCare System in Boston, coordinating the system's cost reduction efforts through 1998. Kigin previously served as director of physical therapy services at Massachusetts General Hospital (MGH) (1977-1984) and as assistant professor at MGH IHP (1980-1994). While at MGH, she was responsible for the merger of 2 separate physical therapy departments, the establishment of the first nonphysician specialist position, and practice without referral for the physical therapy services. Kigin has held numerous positions within the American Physical Therapy Association (APTA), serving on the Board of Directors from 1988-1997, including as vice president; co-chair of The Physical Therapy Summit in 2007; and co-chair of FiRST, the Frontiers in Rehabilitation, Science and Technology Council. She also served as prior chair of the APTA Committee on Clinical Residencies and served on the American Board of Physical Therapy Specialties. Kigin earned a bachelor of science degree in physical therapy at the University of Colorado, a master of science degree at Boston University, a master's degree in public administration from the Harvard Kennedy School of Government, and a doctor in physical therapy degree from the MGH IHP.

Recent trends in telerehabilitation of stroke patients: A narrative review

BACKGROUND: Stroke is the main reason for disabilities worldwide leading to motor dysfunction, spatial neglect and cognitive problems, aphasia, and other speech-language pathologies, reducing the life quality. To overcome disabilities, telerehabilitation (TR) has been recently introduced. OBJECTIVE: The aim of this review was to analyze current TR approaches for stroke patients’ recovery. METHODS: We searched 6 online databases from January 2018 to October 2021, and included 70 research and review papers in the review. We analyzed TR of 995 individuals, which was delivered synchronously and asynchronously. RESULTS: Findings show TR is feasible improving motor function, cognition, speech, and language communication among stroke patients. However, the dose of TR sessions varied significantly. We identified the following limitations: lack of equipment, software, and space for home-based exercises, insufficient internet capacity and speed, unavailability to provide hands on guidance, low digital proficiency and education, high cognitive demand, small samples, data heterogeneity, and no economic evaluation. CONCLUSIONS: The review shows TR is superior or similar to conventional rehabilitation in clinical outcomes and is used as complementary therapy or as alternative treatments. More importantly, TR provides access to rehabilitation services of a large number of patients with immobility, living in remote areas, and during COVID-19 pandemic or similar events.

Effect of Wearable Sensor-Based Exercise on Musculoskeletal Disorders in Individuals With Neurodegenerative Diseases: A Systematic Review and Meta-Analysis

Background

The application of wearable sensor technology in an exercise intervention provides a new method for the standardization and accuracy of intervention. Considering that the deterioration of musculoskeletal conditions is of serious concern in patients with neurodegenerative diseases, it is worthwhile to clarify the effect of wearable sensor-based exercise on musculoskeletal disorders in such patients compared with traditional exercise.

Methods

Five health science-related databases, including PubMed, Cochrane Library, Embase, Web of Science, and Ebsco Cumulative Index to Nursing and Allied Health, were systematically searched. The protocol number of the study is PROSPERO CRD42022319763. Randomized controlled trials (RCTs) that were published up to March 2022 and written in English were included. Balance was the primary outcome measure, comprising questionnaires on postural stability and computerized dynamic posturography. The secondary outcome measures are motor symptoms, mobility ability, functional gait abilities, fall-associated self-efficacy, and adverse events. Stata version 16.0 was used for statistical analysis, and the weighted mean difference (WMD) was selected as the effect size with a 95% confidence interval (CI).

Results

Fifteen RCTs involving 488 participants with mean ages ranging from 58.6 to 81.6 years were included in this review, with 14 of them being pooled in a quantitative meta-analysis. Only five included studies showed a low risk of bias. The Berg balance scale (BBS) was used in nine studies, and the pooled data showed a significant improvement in the wearable sensor-based exercise group compared with the traditional exercise group after 3–12-week intervention (WMD = 1.43; 95% CI, 0.50 to 2.36, P = 0.003). A significant change in visual score was found both post-assessment and at 1-month follow-up assessment (WMD = 4.38; 95% CI, 1.69 to 7.07, P = 0.001; I2 = 0.0%). However, no significant differences were found between the two groups in the secondary outcome measures (all p > 0.05). No major adverse events were reported.

Conclusion

The wearable sensor-based exercise had advantages in improving balance in patients with neurodegenerative diseases, while there was a lack of evidence in motor symptoms, mobility, and functional gait ability enhancement. Future studies are recommended to construct a comprehensive rehabilitation treatment system for the improvement in both postural control and quality of life.

Systematic Review Registration

http://www.crd.york.ac.uk/prospero/, identifier CRD42022319763.

Personalized Activity Recognition with Deep Triplet Embeddings

A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data generated by individual users, resulting in very poor performance for some subjects. We present an approach to personalized activity recognition based on deep feature representation derived from a convolutional neural network (CNN). We experiment with both categorical cross-entropy loss and triplet loss for training, and describe a novel loss function based on subject triplets. We evaluate these methods on three publicly available inertial human activity recognition datasets (MHEALTH, WISDM, and SPAR) comparing classification accuracy, out-of-distribution activity detection, and generalization to new activity classes. The proposed triplet algorithm achieved an average 96.7% classification accuracy across tested datasets versus the 87.5% achieved by the baseline CNN algorithm. We demonstrate that personalized algorithms, and, in particular, the proposed novel triplet loss algorithms, are more robust to inter-subject variability and thus exhibit better performance on classification and out-of-distribution detection tasks.

Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches

Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today-with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care-requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.

Physical Activity Behavior of Patients at a Skilled Nursing Facility: Longitudinal Cohort Study

BACKGROUND

On-body wearable sensors have been used to predict adverse outcomes such as hospitalizations or fall, thereby enabling clinicians to develop better intervention guidelines and personalized models of care to prevent harmful outcomes. In our previous work, we introduced a generic remote patient monitoring framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and the extraction of indoor localization using Bluetooth low energy beacons, in concert. Using the same framework, this paper addresses the longitudinal analyses of a group of patients in a skilled nursing facility. We try to investigate if the metrics derived from a remote patient monitoring system comprised of physical activity and indoor localization sensors, as well as their association with therapist assessments, provide additional insight into the recovery process of patients receiving rehabilitation.

OBJECTIVE

The aim of this paper is twofold: (1) to observe longitudinal changes of sensor-based physical activity and indoor localization features of patients receiving rehabilitation at a skilled nursing facility and (2) to investigate if the sensor-based longitudinal changes can complement patients' changes captured by therapist assessments over the course of rehabilitation in the skilled nursing facility.

METHODS

From June 2016 to November 2017, patients were recruited after admission to a subacute rehabilitation center in Los Angeles, CA. Longitudinal cohort study of patients at a skilled nursing facility was followed over the course of 21 days. At the time of discharge from the skilled nursing facility, the patients were either readmitted to the hospital for continued care or discharged to a community setting. A longitudinal study of the physical therapy, occupational therapy, and sensor-based data assessments was performed. A generalized linear mixed model was used to find associations between functional measures with sensor-based features. Occupational therapy and physical therapy assessments were performed at the time of admission and once a week during the skilled nursing facility admission.

RESULTS

Of the 110 individuals in the analytic sample with mean age of 79.4 (SD 5.9) years, 79 (72%) were female and 31 (28%) were male participants. The energy intensity of an individual while in the therapy area was positively associated with transfer activities (β=.22; SE 0.08; P=.02). Sitting energy intensity showed positive association with transfer activities (β=.16; SE 0.07; P=.02). Lying down energy intensity was negatively associated with hygiene activities (β=-.27; SE 0.14; P=.04). The interaction of sitting energy intensity with time (β=-.13; SE 0.06; P=.04) was associated with toileting activities.

CONCLUSIONS

This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features, a subset of which can provide crucial information to the story line of daily and longitudinal activity patterns of patients receiving rehabilitation at a skilled nursing facility. The findings suggest that detecting physical activity changes within locations may offer some insight into better characterizing patients' progress or decline.

Reliability Study of Inertial Sensors LIS2DH12 Compared to ActiGraph GT9X: Based on Free Code

The study’s purpose was to assess the reliability of the LIS2DH12 in two different positions, using the commercial sensor Actigraph GT9X as a reference instrument. Five participants completed two gait tests on a treadmill. Firstly, both sensors were worn on the wrist and around the thigh. Each test consisted of a 1 min walk for participants to become accustomed to the treadmill, followed by a 2 min trial at ten pre-set speeds. Data from both sensors were collected in real-time. Intraclass correlation coefficient (ICC) was used to evaluate the equality of characteristics obtained by both sensors: maximum peaks, minimum peaks, and the mean of the complete signal (sequence of acceleration values along the time) by each axis and speed were extracted to evaluate the equality of characteristics obtained with LIS2DH12 compared to Actigraph. Intraclass correlation coefficient (ICC) was extracted, and a standard deviation of the mean was obtained from the data. Our results show that LIS2DH12 measurements present more reliability than Actigraph GT9X, ICC > 0.8 at three axes. This study concludes that LIS2DH12 is as reliable and accurate as Actigraph GT9X Link and, therefore, would be a suitable tool for future kinematic studies.

Turmell-Meter: A Device for Estimating the Subtalar and Talocrural Axes of the Human Ankle Joint by Applying the Product of Exponentials Formula

The human ankle is a complex joint, most commonly represented as the talocrural and subtalar axes. It is troublesome to take in vivo measurements of the ankle joint. There are no instruments for patients lying on flat surfaces; employed in outdoor or remote sites. We have developed a "Turmell-meter" to address these issues. It started with the study of ankle anatomy and anthropometry. We also use the product of exponentials' formula to visualize the movements. We built a prototype using human proportions and statistics. For pose estimation, we used a trilateration method by applying tetrahedral geometry. We computed the axis direction by fitting circles in 3D, plotting the manifold and chart as an ankle joint model. We presented the results of simulations, a prototype comprising 45 parts, specifically designed draw-wire sensors, and electronics. Finally, we tested the device by capturing positions and fitting them into the bi-axial ankle model as a Riemannian manifold. The Turmell-meter is a hardware platform for human ankle joint axes estimation. The measurement accuracy and precision depend on the sensor quality; we address this issue by designing an electronics capture circuit, measuring the real measurement with a Vernier caliper. Then, we adjust the analog voltages and filter the 10-bit digital value. The Technology Readiness Level is 2. The proposed ankle joint model has the properties of a chart in a geometric manifold, and we provided the details.

Inertial Sensor-to-Segment Calibration for Accurate 3D Joint Angle Calculation for Use in OpenSim

Inertial capture (InCap) systems combined with musculoskeletal (MSK) models are an attractive option for monitoring 3D joint kinematics in an ecological context. However, the primary limiting factor is the sensor-to-segment calibration, which is crucial to estimate the body segment orientations. Walking, running, and stair ascent and descent trials were measured in eleven healthy subjects with the Xsens InCap system and the Vicon 3D motion capture (MoCap) system at a self-selected speed. A novel integrated method that combines previous sensor-to-segment calibration approaches was developed for use in a MSK model with three degree of freedom (DOF) hip and knee joints. The following were compared: RMSE, range of motion (ROM), peaks, and R² between InCap kinematics estimated with different calibration methods and gold standard MoCap kinematics. The integrated method reduced the RSME for both the hip and the knee joints below 5°, and no statistically significant differences were found between MoCap and InCap kinematics. This was consistent across all the different analyzed movements. The developed method was integrated on an MSK model workflow, and it increased the sensor-to-segment calibration accuracy for an accurate estimate of 3D joint kinematics compared to MoCap, guaranteeing a clinical easy-to-use approach.

The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review

Wearable technologies are often indicated as tools that can enable the in-field collection of quantitative biomechanical data, unobtrusively, for extended periods of time, and with few spatial limitations. Despite many claims about their potential for impact in the area of injury prevention and management, there seems to be little attention to grounding this potential in biomechanical research linking quantities from wearables to musculoskeletal injuries, and to assessing the readiness of these biomechanical approaches for being implemented in real practice. We performed a systematic scoping review to characterise and critically analyse the state of the art of research using wearable technologies to study musculoskeletal injuries in sport from a biomechanical perspective. A total of 4952 articles were retrieved from the Web of Science, Scopus, and PubMed databases; 165 were included. Multiple study features-such as research design, scope, experimental settings, and applied context-were summarised and assessed. We also proposed an injury-research readiness classification tool to gauge the maturity of biomechanical approaches using wearables. Five main conclusions emerged from this review, which we used as a springboard to propose guidelines and good practices for future research and dissemination in the field.

Validity and Reliability of Smartphone App for Evaluating Postural Adjustments during Step Initiation

The evaluation of anticipatory postural adjustments (APAs) requires high-cost and complex handling systems, only available at research laboratories. New alternative methods are being developed in this field, on the other hand, to solve this issue and allow applicability in clinic, sport and hospital environments. The objective of this study was to validate an app for mobile devices to measure the APAs during gait initiation by comparing the signals obtained from cell phones using the Momentum app with measurements made by a kinematic system. The center-of-mass accelerations of a total of 20 healthy subjects were measured by the above app, which read the inertial sensors of the smartphones, and by kinematics, with a reflective marker positioned on their lumbar spine. The subjects took a step forward after hearing a command from an experimenter. The variables of the anticipatory phase, prior to the heel-off and the step phase, were measured. In the anticipatory phase, the linear correlation of all variables measured by the two measurement techniques was significant and indicated a high correlation between the devices (APAonset: r = 0.95, p < 0.0001; APAamp: r = 0.71, p = 0.003, and PEAKtime: r = 0.95, p < 0.0001). The linear correlation between the two measurement techniques for the step phase variables measured by ques was also significant (STEPinterval: r = 0.56, p = 0.008; STEPpeak1: r = 0.79, p < 0.0001; and STEPpeak2: r = 0.64, p < 0.0001). The Bland−Altman graphs indicated agreement between instruments with similar behavior as well as subjects within confidence limits and low dispersion. Thus, using the Momentum cell phone application is valid for the assessment of APAs during gait initiation compared to the gold standard instrument (kinematics), proving to be a useful, less complex, and less costly alternative for the assessment of healthy individuals.

Human activity recognition of children with wearable devices using LightGBM machine learning

Human activity recognition (HAR) using machine learning (ML) methods has been a continuously developed method for collecting and analyzing large amounts of human behavioral data using special wearable sensors in the past decade. Our main goal was to find a reliable method that could automatically detect various playful and daily routine activities in children. We defined 40 activities for ML recognition, and we collected activity motion data by means of wearable smartwatches with a special SensKid software. We analyzed the data of 34 children (19 girls, 15 boys; age range: 6.59–8.38; median age = 7.47). All children were typically developing first graders from three elementary schools. The activity recognition was a binary classification task which was evaluated with a Light Gradient Boosted Machine (LGBM) learning algorithm, a decision tree based method with a threefold cross validation. We used the sliding window technique during the signal processing, and we aimed at finding the best window size for the analysis of each behavior element to achieve the most effective settings. Seventeen activities out of 40 were successfully recognized with AUC values above 0.8. The window size had no significant effect. In summary, the LGBM is a very promising solution for HAR. In line with previous findings, our results provide a firm basis for a more precise and effective recognition system that can make human behavioral analysis faster and more objective.

Smart clothes-assisted home-nursing care program for family caregivers of older persons with dementia and hip fracture: a mixed-methods study

Background

The purpose of this preliminary study was to explore whether a smart clothes-assisted home-nursing care program could benefit family caregivers and their care recipients.

Methods

Family caregivers in charge of a care recipient’s living situation participated in this convergent parallel, mixed methods study. We recruited older persons with dementia (n = 7) and those discharged following hip-fracture surgery (n = 6) from neurological clinics and surgical wards of a medical center, respectively, along with their family caregivers: three spouses, eight sons, one daughter, and one daughter-in-law. Care recipients were asked to wear a smart vest at least 4 days/week for 6 months, which contained a coin-size monitor hidden in an inner pocket. Sensors installed in bedrooms and living areas received signals from the smart clothing, which were transmitted to a mobile phone app of homecare nurses, who provided caregivers with transmitted information regarding activities, emergency situations and suggestions for caregiving activities. Outcomes included changes from baseline in caregivers’ preparedness and depressive symptoms collected at 1- and 3-months, which were analyzed with Friedman’s non-parametric test of repeated measures with post-hoc analysis. Transcripts of face-to-face semi-structured interview data about caregivers’ experiences were analyzed to identify descriptive, interpretative, and pattern codes.

Results

Preparedness did not change from baseline at either 1- or 3-months for family caregivers of persons with dementia. However, depressive symptoms decreased significantly at 1-month and 3-months compared with baseline, but not between 1-months and 3-months. Analysis of the interview data revealed the smart clothes program increased family caregivers’ knowledge of the care recipient’s situation and condition, informed healthcare providers of the care recipient’s physical health and cognitive status, helped homecare nurses provide timely interventions, balanced the care recipient’s exercise and safety, motivated recipients to exercise, helped family caregivers balance work and caregiving, and provided guidance for caregiving activities.

Conclusions

Experiences with the smart clothes-assisted home-nursing care program directly benefited family caregivers, which provided indirect benefits to the care recipients due to the timely interventions and caregiving guidance from homecare nurses. These benefits suggest a smart-clothes-assisted program might be beneficial for all family caregivers.

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.

Commentary: Remote assessments of gait and balance - Implications for research during and beyond Covid-19

The COVID-19 pandemic has disrupted non-essential in-person research activities that require contact with human subjects. While guidelines are being developed for ramping up human subjects research, one component of research that can be performed remotely is participant screening for lower limb function and gait impairments. In this commentary, we summarize evidence-supported clinical assessments that have potential to be conducted remotely in a safe manner, to make an initial determination of the functional mobility status of persons with neurological disorders. We present assessments that do not require complex or costly equipment, specialized software, or trained personnel to administer. We provide recommendations to implement remote functional assessments for participant recruitment and continuation of lower limb neurorehabilitation research as a rapid response to the COVID-19 pandemic and for utilization beyond the current pandemic. We also highlight critical research gaps related to feasibility and measurement characteristics of remote lower limb assessments, providing opportunities for future research to advance tele-assessment and tele-rehabilitation.

A Systematic Review of Diagnostic Accuracy and Clinical Applications of Wearable Movement Sensors for Knee Joint Rehabilitation

In clinical practice, only a few reliable measurement instruments are available for monitoring knee joint rehabilitation. Advances to replace motion capturing with sensor data measurement have been made in the last years. Thus, a systematic review of the literature was performed, focusing on the implementation, diagnostic accuracy, and facilitators and barriers of integrating wearable sensor technology in clinical practices based on a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. For critical appraisal, the COSMIN Risk of Bias tool for reliability and measurement of error was used. PUBMED, Prospero, Cochrane database, and EMBASE were searched for eligible studies. Six studies reporting reliability aspects in using wearable sensor technology at any point after knee surgery in humans were included. All studies reported excellent results with high reliability coefficients, high limits of agreement, or a few detectable errors. They used different or partly inappropriate methods for estimating reliability or missed reporting essential information. Therefore, a moderate risk of bias must be considered. Further quality criterion studies in clinical settings are needed to synthesize the evidence for providing transparent recommendations for the clinical use of wearable movement sensors in knee joint rehabilitation.

Configurable Offline Sensor Placement Identification for a Medical Device Monitoring Parkinson's Disease

Sensor placement identification in body sensor networks is an important feature, which could render such a system more robust, transparent to the user, and easy to wear for long term data collection. It can be considered an active measure to avoid the misuse of a sensing system, specifically as these platforms become more ubiquitous and, apart from their research orientation, start to enter industries, such as fitness and health. In this work we discuss the offline, fixed class, sensor placement identification method implemented in PDMonitor^®, a medical device for long-term Parkinson’s disease monitoring at home. We analyze the stepwise procedure used to accurately identify the wearables depending on how many are used, from two to five, given five predefined body positions. Finally, we present the results of evaluating the method in 88 subjects, 61 Parkinson’s disease patients and 27 healthy subjects, when the overall average accuracy reached 99.1%.

Artificial Intelligence Based Body Sensor Network Framework-Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare

With the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality 'big data' in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.