Innovative Approaches to Menstruation and Fertility Tracking Using Wearable Reproductive Health Technology: Systematic Review.

BACKGROUND: Emerging digital health technology has moved into the reproductive health market for female individuals. In the past, mobile health apps have been used to monitor the menstrual cycle using manual entry. New technological trends involve the use of wearable devices to track fertility by assessing physiological changes such as temperature, heart rate, and respiratory rate. OBJECTIVE: The primary aims of this study are to review the types of wearables that have been developed and evaluated for menstrual cycle tracking and to examine whether they may detect changes in the menstrual cycle in female individuals. Another aim is to review whether these devices are effective for tracking various stages in the menstrual cycle including ovulation and menstruation. Finally, the secondary aim is to assess whether the studies have validated their findings by reporting accuracy and sensitivity. METHODS: A review of PubMed or MEDLINE was undertaken to evaluate wearable devices for their effectiveness in predicting fertility and differentiating between the different stages of the menstrual cycle. RESULTS: Fertility cycle-tracking wearables include devices that can be worn on the wrists, on the fingers, intravaginally, and inside the ear. Wearable devices hold promise for predicting different stages of the menstrual cycle including the fertile window and may be used by female individuals as part of their reproductive health. Most devices had high accuracy for detecting fertility and were able to differentiate between the luteal phase (early and late), fertile window, and menstruation by assessing changes in heart rate, heart rate variability, temperature, and respiratory rate. CONCLUSIONS: More research is needed to evaluate consumer perspectives on reproductive technology for monitoring fertility, and ethical issues around the privacy of digital data need to be addressed. Additionally, there is also a need for more studies to validate and confirm this research, given its scarcity, especially in relation to changes in respiratory rate as a proxy for reproductive cycle staging.

Cross-Link-Dependent Ionogel-Based Triboelectric Nanogenerators with Slippery and Antireflective Properties.

Given the ability to convert various ambient unused mechanical energies into useful electricity, triboelectric nanogenerators (TENGs) are gaining interest since their inception. Recently, ionogel-based TENGs (I-TENGs) have attracted increasing attention because of their excellent thermal stability and adjustable ionic conductivity. However, previous studies on ionogels mainly pursued the device performance or applications under harsh conditions, whereas few have investigated the structure-property relationships of components to performance. The results indicate that the ionogel formulation-composed of a crosslinking monomer with an ionic liquid-affects the conductivity of the ionogel by modulating the cross-link density. In addition, the ratio of cross-linker to ionic liquid is important to ensure the formation of efficient charge channels, yet increasing ionic liquid content delivers diminishing returns. The ionogels are then used in I-TENGs to harvest water droplet energy and the performance is correlated to the ionogels structure-property relationships. Improvement of the energy harvesting is further explored by the introduction of surface polymer brushes on I-TENGs via a facile and universal method, which enhances droplet sliding by means of ideal surface contact angle hysteresis and improves its anti-reflective properties by employing the I-TENG as a surface covering for solar cells.

Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals.

BACKGROUND: Assistive robotic hand orthoses can support people with sensorimotor hand impairment in many activities of daily living and therefore help to regain independence. However, in order for the users to fully benefit from the functionalities of such devices, a safe and reliable way to detect their movement intention for device control is crucial. Gesture recognition based on force myography measuring volumetric changes in the muscles during contraction has been previously shown to be a viable and easy to implement strategy to control hand prostheses. Whether this approach could be efficiently applied to intuitively control an assistive robotic hand orthosis remains to be investigated. METHODS: In this work, we assessed the feasibility of using force myography measured from the forearm to control a robotic hand orthosis worn on the hand ipsilateral to the measurement site. In ten neurologically-intact participants wearing a robotic hand orthosis, we collected data for four gestures trained in nine arm configurations, i.e., seven static positions and two dynamic movements, corresponding to typical activities of daily living conditions. In an offline analysis, we determined classification accuracies for two binary classifiers (one for opening and one for closing) and further assessed the impact of individual training arm configurations on the overall performance. RESULTS: We achieved an overall classification accuracy of 92.9% (averaged over two binary classifiers, individual accuracies 95.5% and 90.3%, respectively) but found a large variation in performance between participants, ranging from 75.4 up to 100%. Averaged inference times per sample were measured below 0.15 ms. Further, we found that the number of training arm configurations could be reduced from nine to six without notably decreasing classification performance. CONCLUSION: The results of this work support the general feasibility of using force myography as an intuitive intention detection strategy for a robotic hand orthosis. Further, the findings also generated valuable insights into challenges and potential ways to overcome them in view of applying such technologies for assisting people with sensorimotor hand impairment during activities of daily living.

Textile-Based Body Capacitive Sensing for Knee Angle Monitoring.

Monitoring human movement is highly relevant in mobile health applications. Textile-based wearable solutions have the potential for continuous and unobtrusive monitoring. The precise estimation of joint angles is important in applications such as the prevention of osteoarthritis or in the assessment of the progress of physical rehabilitation. We propose a textile-based wearable device for knee angle estimation through capacitive sensors placed in different locations above the knee and in contact with the skin. We exploited this modality to enhance the baseline value of the capacitive sensors, hence facilitating readout. Moreover, the sensors are fabricated with only one layer of conductive fabric, which facilitates the design and realization of the wearable device. We observed the capability of our system to predict knee sagittal angle in comparison to gold-standard optical motion capture during knee flexion from a seated position and squats: the results showed an R2 coefficient between 0.77 and 0.99, root mean squared errors between 4.15 and 12.19 degrees, and mean absolute errors between 3.28 and 10.34 degrees. Squat movements generally yielded more accurate predictions than knee flexion from a seated position. The combination of the data from multiple sensors resulted in R2 coefficient values of 0.88 or higher. This preliminary work demonstrates the feasibility of the presented system. Future work should include more participants to further assess the accuracy and repeatability in the presence of larger interpersonal variability.

Submillimeter Sized 2D Electrothermal Optical Fiber Scanner.

Optical scanners are used frequently in medical imaging units to examine and diagnose cancers, assist with surgeries, and detect lesions and malignancies. The continuous growth in optics along with the use of optical fibers enables fabrication of imaging devices as small as a few millimeters in diameter. Most forward viewing endoscopic scanners contain an optical fiber acting as cantilever which is vibrated at resonance. In many cases, more than one actuating element is used to vibrate the optical fiber in two directions giving a 2D scan. In this paper, it is proposed to excite the cantilever fiber using a single actuator and scan a 2D region from its vibrating tip. An electrothermal actuator is optimized to provide a bidirectional (horizontal and vertical) displacement to the cantilever fiber placed on it. A periodic current, having a frequency equal to the resonant frequency of cantilever fiber, was passed through the actuator. The continuous expansion and contraction of the actuator enabled the free end of fiber to vibrate in a circle like pattern. A small change in the actuation frequency permitted the scanning of the area inside the circle.

The Use of Audio Signals for Detecting COVID-19: A Systematic Review.

A systematic review on the topic of automatic detection of COVID-19 using audio signals was performed. A total of 48 papers were obtained after screening 659 records identified in the PubMed, IEEE Xplore, Embase, and Google Scholar databases. The reviewed studies employ a mixture of open-access and self-collected datasets. Because COVID-19 has only recently been investigated, there is a limited amount of available data. Most of the data are crowdsourced, which motivated a detailed study of the various pre-processing techniques used by the reviewed studies. Although 13 of the 48 identified papers show promising results, several have been performed with small-scale datasets (<200). Among those papers, convolutional neural networks and support vector machine algorithms were the best-performing methods. The analysis of the extracted features showed that Mel-frequency cepstral coefficients and zero-crossing rate continue to be the most popular choices. Less common alternatives, such as non-linear features, have also been proven to be effective. The reported values for sensitivity range from 65.0% to 99.8% and those for accuracy from 59.0% to 99.8%.

A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity Measures.

Pragmatic, objective, and accurate motor assessment tools could facilitate more frequent appraisal of longitudinal change in motor function and subsequent development of personalized therapeutic strategies. Brain functional connectivity (FC) has shown promise as an objective neurophysiological measure for this purpose. The involvement of different brain networks, along with differences across subjects due to age or existing capabilities, motivates an individualized approach towards the evaluation of FC. We advocate the use of EEG-based resting-state FC (rsFC) measures to address the pragmatic requirements. Pertaining to appraisal of accuracy, we suggest using the acquisition of motor skill by healthy individuals that could be quantified at small incremental change. Computer-based tracing tasks are a good candidate in this regard when using spatial error in tracing as an objective measure of skill. This work investigates the application of an individualized method that utilizes Partial Least Squares analysis to estimate the longitudinal change in tracing error from changes in rsFC. Longitudinal data from participants yielded an average accuracy of 98% (standard deviation of 1.2%) in estimating tracing error. The results show potential for an accurate individualized motor assessment tool that reduces the dependence on the expertise and availability of trained examiners, thereby facilitating more frequent appraisal of function and development of personalized training programs.

Clinicians' perceptions of a potential wearable device for capturing upper limb activity post-stroke: a qualitative focus group study.

BACKGROUND: There is growing interest in the use of wearable devices that track upper limb activity after stroke to help determine and motivate the optimal dose of upper limb practice. The purpose of this study was to explore clinicians' perceptions of a prospective wearable device that captures upper limb activity to assist in the design of devices for use in rehabilitation practice. METHODS: Four focus groups with 18 clinicians (occupational and physical therapists with stroke practice experience from a hospital or private practice setting) were conducted. Data were analyzed thematically. RESULTS: Our analysis revealed three themes: (1) "Quantity and quality is ideal" emphasized how an ideal device would capture both quantity and quality of movement; (2) "Most useful outside therapy sessions" described how therapists foresaw using the device outside of therapy sessions to monitor homework adherence, provide self-monitoring of use, motivate greater use and provide biofeedback on movement quality; (3) "User-friendly please" advocated for the creation of a device that was easy to use and customizable, which reflected the client-centered nature of their treatment. CONCLUSIONS: Findings from this study suggest that clinicians support the development of wearable devices that capture upper limb activity outside of therapy for individuals with some reach to grasp ability. Devices that are easy to use and capture both quality and quantity may result in greater uptake in the clinical setting. Future studies examining acceptability of wearable devices for tracking upper limb activity from the perspective of individuals with stroke are needed.

Force Myography-Based Human Robot Interactions via Deep Domain Adaptation and Generalization.

Estimating applied force using force myography (FMG) technique can be effective in human-robot interactions (HRI) using data-driven models. A model predicts well when adequate training and evaluation are observed in same session, which is sometimes time consuming and impractical. In real scenarios, a pretrained transfer learning model predicting forces quickly once fine-tuned to target distribution would be a favorable choice and hence needs to be examined. Therefore, in this study a unified supervised FMG-based deep transfer learner (SFMG-DTL) model using CNN architecture was pretrained with multiple sessions FMG source data (Ds, Ts) and evaluated in estimating forces in separate target domains (Dt, Tt) via supervised domain adaptation (SDA) and supervised domain generalization (SDG). For SDA, case (i) intra-subject evaluation (Ds ≠ Dt-SDA, Ts ≈ Tt-SDA) was examined, while for SDG, case (ii) cross-subject evaluation (Ds ≠ Dt-SDG, Ts ≠ Tt-SDG) was examined. Fine tuning with few "target training data" calibrated the model effectively towards target adaptation. The proposed SFMG-DTL model performed better with higher estimation accuracies and lower errors (R2 ≥ 88%, NRMSE ≤ 0.6) in both cases. These results reveal that interactive force estimations via transfer learning will improve daily HRI experiences where "target training data" is limited, or faster adaptation is required.

Inductive Textile Sensor Design and Validation for a Wearable Monitoring Device.

Textile sensors have gained attention for wearable devices, in which the most popular are the resistive textile sensor. However, these sensors present high hysteresis and a drift when stretched for long periods of time. Inductive textile sensors have been commonly used as antennas and plethysmographs, and their applications have been extended to measure heartbeat, wireless data transmission, and motion and gesture capturing systems. Inductive textile sensors have shown high reliability, stable readings, low production cost, and an easy manufacturing process. This paper presents the design and validation of an inductive strain textile sensor. The anthropometric dimensions of a healthy participant were used to define the maximum dimensions of the inductive textile sensor. The design of the inductive sensor was studied through theoretical calculations and simulations. Parameters such as height, width, area, perimeter, and number of complete loops were considered to calculate and evaluate the inductance value.

Low Back Pain-Behavior Correction by Providing Haptic Feedbacks: A Preliminary Investigation.

The activities performed by nurses in their daily activities involve frequent forward bending and awkward back postures. These movements contribute to the prevalence and development of low back pain (LBP). In previous studies, it has been shown that modifying their posture by education and training in proper lifting techniques decreases the prevalence of LBP. However, this education and training needs to be implemented daily. Hence, implementing the use of a wearable device to monitor the back posture with haptic feedback would be of importance to prevent LBP. This paper proposes a wearable device to monitor the back posture of the user and provide feedback when the participant is performing a possible hurtful movement. In this study, a group of participants was asked to wear the device while performing three of the most common activities performed by nurses. The study was divided into three sessions: In the first session, the participants performed the activities without feedback (baseline). During the second session, the participants received feedback from the wearable device (training) while performing the three tasks. Finally, for the third session, the participants performed the three tasks again, but the haptic feedback was turned off (validation). We found an improvement in the posture of more than 40% for the pitch (lateral bending) and roll (forward/backward bending) axes and 7% for the yaw (twisting) axis when comparing to the results from session 1 and session 2. The comparison between session 1 and session 3 showed an overall improvement of more than 50% for the pitch (lateral bending) and roll (forward/backward bending) axes and more than 20% for the yaw axis. These results hinted at the impact of the haptic feedback on the participants to correct their posture.

Scanning and Actuation Techniques for Cantilever-Based Fiber Optic Endoscopic Scanners-A Review.

Endoscopes are used routinely in modern medicine for in-vivo imaging of luminal organs. Technical advances in the micro-electro-mechanical system (MEMS) and optical fields have enabled the further miniaturization of endoscopes, resulting in the ability to image previously inaccessible small-caliber luminal organs, enabling the early detection of lesions and other abnormalities in these tissues. The development of scanning fiber endoscopes supports the fabrication of small cantilever-based imaging devices without compromising the image resolution. The size of an endoscope is highly dependent on the actuation and scanning method used to illuminate the target image area. Different actuation methods used in the design of small-sized cantilever-based endoscopes are reviewed in this paper along with their working principles, advantages and disadvantages, generated scanning patterns, and applications.

Wearable Technology to Increase Self-Awareness of Low Back Pain: A Survey of Technology Needs among Health Care Workers.

Low back pain (LBP) is a leading contributor to musculoskeletal injury worldwide and carries a high economic cost. The healthcare industry is the most burdened, with nurses, in particular, being highly prone to LBP. Wearable technologies have the potential to address the challenges of monitoring postures that contribute to LBP and increase self-awareness of workplace postures and movements. We aimed to gain insight into workers' perceptions of LBP and whether they would consider using wearable monitoring technologies to reduce injury risks. We conducted a cross-sectional survey to gather information from a selected population of nurses. Sixty-four participants completed the survey, and data were analyzed with the support of Machine Learning techniques. Findings from this study indicate that the surveyed population (64 nurses) is interested in these new approaches to monitor movement and posture in the workplace. This technology can potentially change the way ergonomic guidelines are implemented in this population.

A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance.

ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing pipeline employing a manifold learning method to produce a robust signal representation to boost hand gesture classifiers' performance. We tested this approach on an FMG dataset collected from nine participants in 3 different data collection sessions with short delays between each. For each participant's data, the proposed pipeline was applied, and then different classification algorithms were used to evaluate the effect of the pipeline compared to raw FMG signals in hand gesture classification. The results show that incorporating the proposed pipeline reduced variance within the same gesture data and notably maximized variance between different gestures, allowing improved robustness of hand gestures classification performance and consistency across time. On top of that, the pipeline improved the classification accuracy consistently regardless of different classifiers, gaining an average of 5% accuracy improvement.

PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals.

An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched together to generate a long, annotated PPG. Furthermore, this toolbox can simulate real-life PPG signals by introducing different noise levels and PPG waveforms. The toolbox can implement two stitching methods: one based on the systolic peak and the other on the onset. Additionally, cubic spline interpolation is used to smooth the waveform around the stitching point, and a skewness index is used as a signal quality index to select the final signal output based on the stitching method used. The developed toolbox is free and open-source software, and a graphical user interface is provided. The method of synthesizing by stitching introduced in this paper is a data augmentation strategy that can help researchers significantly increase the size and diversity of annotated PPG signals available for training and testing different feature extraction algorithms.

Cortical Effects of Noisy Galvanic Vestibular Stimulation Using Functional Near-Infrared Spectroscopy.

Noisy galvanic vestibular stimulation (nGVS) can improve different motor, sensory, and cognitive behaviors. However, it is unclear how this stimulation affects brain activity to facilitate these improvements. Functional near-infrared spectroscopy (fNIRS) is inexpensive, portable, and less prone to motion artifacts than other neuroimaging technology. Thus, fNIRS has the potential to provide insight into how nGVS affects cortical activity during a variety of natural behaviors. Here we sought to: (1) determine if fNIRS can detect cortical changes in oxygenated (HbO) and deoxygenated (HbR) hemoglobin with application of subthreshold nGVS, and (2) determine how subthreshold nGVS affects this fNIRS-derived hemodynamic response. A total of twelve healthy participants received nGVS and sham stimulation during a seated, resting-state paradigm. To determine whether nGVS altered activity in select cortical regions of interest (BA40, BA39), we compared differences between nGVS and sham HbO and HbR concentrations. We found a greater HbR response during nGVS compared to sham stimulation in left BA40, a region previously associated with vestibular processing, and with all left hemisphere channels combined (p < 0.05). We did not detect differences in HbO responses for any region during nGVS (p > 0.05). Our results suggest that fNIRS may be suitable for understanding the cortical effects of nGVS.

The Validity and Reliability of Two Commercially Available Load Sensors for Clinical Strength Assessment.

OBJECTIVE: Handheld dynamometers are common tools for assessing/monitoring muscular strength and endurance. Health/fitness Bluetooth load sensors may provide a cost-effective alternative; however, research is needed to evaluate the validity and reliability of such devices. This study assessed the validity and reliability of two commercially available Bluetooth load sensors (Activ5 by Activbody and Progressor by Tindeq). METHODS: Four tests were conducted on each device: stepped loading, stress relaxation, simulated exercise, and hysteresis. Each test type was repeated three times using the Instron ElectroPuls mechanical testing device (a gold-standard system). Test-retest reliability was assessed through intraclass correlations. Agreement with the gold standard was assessed with Pearson's correlation, interclass correlation, and Lin's concordance correlation. RESULTS: The Activ5 and Progressor had excellent test-retest reliability across all four tests (ICC(3,1) ≥ 0.999, all p ≤ 0.001). Agreement with the gold standard was excellent for both the Activ5 (ρ ≥ 0.998, ICC(3,1) ≥ 0.971, ρc ≥ 0.971, all p's ≤ 0.001) and Progressor (ρ ≥ 0.999, ICC(3,1) ≥ 0.999, ρc ≥ 0.999, all p's ≤ 0.001). Measurement error increased for both devices as applied load increased. CONCLUSION: Excellent test-retest reliability was found, suggesting that both devices can be used in a clinical setting to measure patient progress over time; however, the Activ5 consistently had poorer agreement with the gold standard (particularly at higher loads).

Wrist-worn wearables based on force myography: on the significance of user anthropometry.

BACKGROUND: Force myography (FMG) is a non-invasive technology used to track functional movements and hand gestures by sensing volumetric changes in the limbs caused by muscle contraction. Force transmission through tissue implies that differences in tissue mechanics and/or architecture might impact FMG signal acquisition and the accuracy of gesture classifier models. The aim of this study is to identify if and how user anthropometry affects the quality of FMG signal acquisition and the performance of machine learning models trained to classify different hand and wrist gestures based on that data. METHODS: Wrist and forearm anthropometric measures were collected from a total of 21 volunteers aged between 22 and 82 years old. Participants performed a set of tasks while wearing a custom-designed FMG band. Primary outcome measure was the Spearman's correlation coefficient (R) between the anthropometric measures and FMG signal quality/ML model performance. RESULTS: Results demonstrated moderate (0.3 ≤|R| < 0.67) and strong (0.67 ≤ |R|) relationships for ratio of skinfold thickness to forearm circumference, grip strength and ratio of wrist to forearm circumference. These anthropometric features contributed to 23-30% of the variability in FMG signal acquisition and as much as 50% of the variability in classification accuracy for single gestures. CONCLUSIONS: Increased grip strength, larger forearm girth, and smaller skinfold-to-forearm circumference ratio improve signal quality and gesture classification accuracy.

Robotics-assisted visual-motor training influences arm position sense in three-dimensional space.

BACKGROUND: Performing activities of daily living depends, among other factors, on awareness of the position and movements of limbs. Neural injuries, such as stroke, might negatively affect such an awareness and, consequently, lead to degrading the quality of life and lengthening the motor recovery process. With the goal of improving the sense of hand position in three-dimensional (3D) space, we investigate the effects of integrating a pertinent training component within a robotic reaching task. METHODS: In the proof-of-concept study presented in this paper, 12 healthy participants, during a single session, used their dominant hand to attempt reaching without vision to two targets in 3D space, which were placed at locations that resembled the functional task of self-feeding. After each attempt, participants received visual and haptic feedback about their hand's position to accurately locate the target. Performance was evaluated at the beginning and end of each session during an assessment in which participants reached without visual nor haptic feedback to three targets: the same two targets employed during the training phase and an additional one to evaluate the generalization of training. RESULTS: Collected data showed a statistically significant [39.81% (p=0.001)] reduction of end-position reaching error when results of reaching to all targets were combined. End-position error to the generalization target, although not statistically significant, was reduced by 15.47%. CONCLUSIONS: These results provide support for the effectiveness of combining an arm position sense training component with functional motor tasks, which could be implemented in the design of future robot-assisted rehabilitation paradigms to potentially expedite the recovery process of individuals with neurological injuries.

Perspectives on the prospective development of stroke-specific lower extremity wearable monitoring technology: a qualitative focus group study with physical therapists and individuals with stroke.

BACKGROUND: Wearable activity monitors that track step count can increase the wearer's physical activity and motivation but are infrequently designed for the slower gait speed and compensatory patterns after stroke. New and available technology may allow for the design of stroke-specific wearable monitoring devices, capable of detecting more than just step counts, which may enhance how rehabilitation is delivered. The objective of this study was to identify important considerations in the development of stroke-specific lower extremity wearable monitoring technology for rehabilitation, from the perspective of physical therapists and individuals with stroke. METHODS: A qualitative research design with focus groups was used to collect data. Five focus groups were conducted, audio recorded, and transcribed verbatim. Data were analyzed using content analysis to generate overarching categories representing the stakeholder considerations for the development of stroke-specific wearable monitor technology for the lower extremity. RESULTS: A total of 17 physical therapists took part in four focus group discussions and three individuals with stroke participated in the fifth focus group. Our analysis identified four main categories for consideration: 1) 'Variability' described the heterogeneity of patient presentation, therapy approaches, and therapeutic goals that are taken into account for stroke rehabilitation; 2) 'Context of use' described the different settings and purposes for which stakeholders could foresee employing stroke-specific wearable technology; 3) 'Crucial design features' identified the measures, functions, and device characteristics that should be considered for incorporation into prospective technology to enhance uptake; and 4) 'Barriers to adopting technology' highlighted challenges, including personal attitudes and design flaws, that may limit the integration of current and future wearable monitoring technology into clinical practice. CONCLUSIONS: The findings from this qualitative study suggest that the development of stroke-specific lower extremity wearable monitoring technology is viewed positively by physical therapists and individuals with stroke. While a single, specific device or function may not accommodate all the variable needs of therapists and their clients, it was agreed that wearable monitoring technology could enhance how physical therapists assess and treat their clients. Future wearable devices should be developed in consideration of the highlighted design features and potential barriers for uptake.