Objective To investigate the clinical validity of finger-worn accelerometers for providing a comprehensive assessment of upper-limb motor performance in individuals post-stroke in real-world environments, compared to wrist-worn accelerometers, and to examine how the clinimetric properties of wearable-based motor performance measures vary with the duration of patient data collection. Design Cross-sectional observational design. Setting Research laboratory and free-living environments. Participants Twenty-seven participants aged 18-80 years with ischemic or hemorrhagic stroke at least six months prior to enrollment and mild-to-moderate upper-limb impairment without severe range-of-motion restrictions were enrolled. Three participants were ineligible and four withdrew, resulting in a final cohort of 20 participants (N = 20). Interventions Not applicable. Main Outcome Measures Wearable-based motor performance measures derived from fine-hand movements, gross-arm movements, and the combination of fine-hand and gross-arm movements captured by finger-worn and wrist-worn accelerometers in naturalistic settings for 6.4 ± 1.8 days. Results Wearable-based motor performance measures from fine-hand movements demonstrated the strongest convergent validity with standardized clinical assessments of motor capacity and performance—including the Fugl-Meyer Assessment for Upper Extremity (FMA-UE), Wolf Motor Function Test (WMFT), and Motor Activity Log (MAL)—as well as the strongest known-group validity in distinguishing impairment levels and the highest test-retest reliability, followed by measures from combined and gross-arm movements. Convergent validity and test-retest reliability improved with longer monitoring durations, with four days being sufficient to obtain accurate and reliable upper-limb measures. Conclusions Wearable-based motor performance measures from finger-worn accelerometers provide a more comprehensive assessment of upper-limb motor performance than those from wrist-worn accelerometers, supporting their use for real-world monitoring in individuals post-stroke. Furthermore, the improvements in clinimetric properties of wearable-based motor performance measures with longer monitoring durations highlight the importance of multi-day monitoring to mitigate day-to-day variability and ensure robust assessment.

Continuous, objective, and precise upper-limb motor assessments are essential for realizing the vision of precision rehabilitation for stroke survivors. Wearable inertial sensors have emerged as a promising solution, enabling the analysis of motor performance in real-world settings. Recent studies have introduced two movement segmentation methods—anatomical segmentation and linear segmentation—for processing wearable inertial data to monitor post-stroke upper-limb motor recovery, each grounded in distinct theories of motor control and behavior. These methods differ in their practical implications for clinical use: linear segmentation requires only a single wearable device on the stroke-affected wrist, while anatomical segmentation necessitates an additional sensor on the sternum. This study seeks to systematically compare the clinimetric performance of these two approaches, taking into account their differences in practicality, to provide insights into their effective integration into clinical practice. 17 stroke survivors were equipped with inertial sensors on the trunk and the stroke-affected wrist while performing activities of daily living in a simulated apartment setting. Acceleration time-series from wrist movements were decomposed into movement segments using each movement segmentation approach. Reliable features were extracted from the movement segments, and supervised regression models were trained to establish concurrent validity against existing clinical measures. Anatomical segmentation demonstrated strong concurrent validity against existing clinical measures but may face challenges for continuous use due to the need for multiple sensors. Linear segmentation, on the other hand, provided slightly reduced but acceptable performance in motor deficit assessment while offering the advantage of requiring only a single wrist-worn sensor.

Objectives To investigate the feasibility of employing socially assistive robots (SARs) as a means to enhance compliance with home-based rehabilitation programs among children with neurologic conditions such as cerebral palsy. Design A prototype version of the REHAB-PAL system was created by integrating both an active video game (AVG) interface and an SAR, specifically the Nao robot. Setting Rehabilitation hospital. Participants We recruited 10 children with neurologic conditions (7-14 years old, 4 boys and 6 girls). Interventions Participants engaged in AVG games targeting gait and balance under 3 distinct conditions. These conditions were randomly assigned to each participant and included: (1) utilizing the AVG platform without any augmentation; (2) utilizing the AVG platform enhanced with a physically embodied SAR; and (3) utilizing the AVG platform enhanced with a virtual agent (an avatar displayed on the screen providing feedback similar to the SAR). After the session, both the child and parent(s) who attended the session were presented with questionnaires for feedback. Main Outcome Measures The primary outcome measure used was the system usability scale (SUS), employed to assess the system's usability. Results The results from the SUS indicate notable preferences for the SARs over both the virtual agent and the AVG platform in several key aspects. When comparing SAR to the virtual agent, participants expressed a strong inclination toward using the system frequently (90%) and perceived less complexity in its operation (70%). Additionally, a significant majority believed that most users would quickly grasp the system (90%) and found it less cumbersome to use (80%) compared to the virtual agent. When contrasting SAR with the AVG platform, participants favored SAR in terms of frequency of use (90%) and perceived complexity (90%). Conclusions Our study investigated the feasibility of utilizing SARs to enhance compliance with home-based rehabilitation programs among children with neurologic conditions, such as cerebral palsy. Through the integration of an AVG interface with a physically embodied SAR within the REHAB-PAL system, we aimed to create an engaging and supportive therapeutic environment. The findings, as assessed by the SUS, revealed significant preferences for SARs over both the virtual agent and standalone AVG platforms across various usability metrics. These results highlight the potential of SARs to improve the effectiveness and acceptability of home-based rehabilitation interventions for children with neurologic conditions. This research provides valuable insights for future studies and clinical applications aiming to enhance rehabilitation outcomes through innovative technology integration. Disclosures none.

BACKGROUND:Children with cerebral palsy (CP) often experience gait impairments. Robot-assisted gait training (RGT) has been shown to have beneficial effects in this patient population. However, clinical outcomes of RGT vary substantially from patient to patient. This study explored the hypothesis that clinical outcomes are associated with changes in muscle synergies in response to RGT. METHODS:Thirteen children with CP and Gross Motor Function Classification Scale (GMFCS) levels I-IV were recruited in the study. Children participated in a 6 week-RGT intervention and underwent clinical evaluations and gait studies-with focus on the analysis of electromyographic (EMG) data-pre- and post-training. Lower-limb muscle synergies were derived from the EMG recordings. Pre- vs. post-RGT clinical outcomes and muscle synergies were compared to explore potential relationships. RESULTS:Three and, less often, two muscle synergies were detected in study participants pre-RGT. Linear mixed effect models showed that composition of the muscle synergies and their temporal activation coefficients present deviations from normative data proportional to the severity of functional limitations (i.e., GMFCS levels, p < 0.01). At a group level, changes in muscle synergies pre- vs. post-RGT did not significantly correlate with changes in clinical outcomes (p > 0.05). However, it was observed that participants who displayed prominent changes in muscle synergies also displayed large improvements in clinical scores. CONCLUSIONS:Gait impairments in children with CP were associated with muscle synergies that deviated from normative. Participants who demonstrated the most substantial improvements in clinical scores following RGT exhibited multiple changes in the muscle synergies. However, no statistically significant correlations were identified at the group level. Future studies relying on larger datasets are needed to further investigate this observation and potential underlying mechanisms.

Tai Chi, an Asian martial art, is renowned for its health benefits, particularly in promoting healthy aging among older adults, improving balance, and reducing fall risk. However, methodological challenges hinder the objective measurement of adherence to and proficiency in performing a training protocol, critical for health outcomes. This study introduces a framework using wearable sensors and machine learning to monitor Tai Chi training adherence and proficiency. Data were collected from 32 participants with inertial measurement units (IMUs) while performing six Tai Chi movements evaluated and scored for adherence and proficiency by experts. Our framework comprises a model for identifying the specific Tai Chi movement being performed and a model to assess performance proficiency, both employing Random Forest algorithms and features from IMU signals. The movement identification model achieved a micro F1 score of 90.05%. The proficiency assessment models achieved a mean micro F1 score of 78.64%. This study shows the feasibility of using IMUs and machine learning for detailed Tai Chi movement analysis, offering a scalable method for monitoring practice. This approach has the potential to objectively enhance the evaluation of Tai Chi training protocol adherence, learnability, progression in proficiency, and safety in Tai Chi programs, and thus inform training program parameters that are key to achieving optimal clinical outcomes.

Background:Accurate and objective assessment of motor performance is critical for effective stroke rehabilitation. While wrist-worn accelerometers are widely accepted as a valid tool for evaluating upper-limb motor performance, they primarily capture arm and forearm movements, overlooking hand and finger activity. This limitation reduces their ability to detect changes in distal function, hindering the broader integration of wearable-based motor performance metrics into clinical practice. Objective:To determine whether finger-worn accelerometers, which capture both proximal and distal movements of the upper limbs, offer a more comprehensive assessment of motor performance by comparing their convergent validity with that of wrist-worn accelerometers. Methods:Bilateral accelerometer data were collected from 24 stroke survivors using finger-worn and wrist-worn devices as they performed unscripted daily activities in a simulated home environment. Motor performance metrics from both sensor locations were analyzed for correlations with the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) and sensitivity to differences in motor performance across impairment levels. Results:Finger-worn accelerometer metrics showed stronger correlations with FMA-UE scores than those from wrist-worn sensors, largely due to their ability to capture fine hand movements. Additionally, finger-worn sensors demonstrated greater sensitivity in detecting performance differences between mildly and moderately impaired individuals. Conclusions:By capturing both proximal and distal movements, finger-worn accelerometers demonstrate stronger convergent validity with standardized measures of post-stroke motor impairment compared to wrist-worn accelerometers. These findings highlight their potential for providing a more comprehensive assessment of motor performance in stroke survivors.

Background Frequent and objective monitoring of motor recovery progression holds significant importance in stroke rehabilitation. Despite extensive studies on wearable solutions in this context, the focus has been predominantly on evaluating limb activity. This study aims to address this limitation by delving into a novel measure of wrist kinematics more intricately related to patients' motor capacity.Objective To explore a new wearable-based approach for objectively and reliably assessing upper-limb motor ability in stroke survivors using a single inertial sensor placed on the stroke-affected wrist.Methods Seventeen stroke survivors performed a series of daily activities within a simulated home setting while wearing a six-axis inertial measurement unit on the wrist affected by stroke. Inertial data during point-to-point upper-limb movements were decomposed into movement segments, from which various kinematic variables were derived. A data-driven approach was then employed to identify a kinematic variable demonstrating robust internal reliability, construct validity, and convergent validity.Results We have identified a key kinematic variable, namely the 90th percentile of movement segment distance during point-to-point movements. This variable exhibited robust reliability (intra-class correlation coefficient of .93) and strong correlations with established clinical measures of motor capacity (Pearson's correlation coefficients of .81 with the Fugl-Meyer Assessment for Upper-Extremity; .77 with the Functional Ability component of the Wolf Motor Function Test; and -.68 with the Performance Time component of the Wolf Motor Function Test).Conclusions The findings underscore the potential for continuous, objective, and convenient monitoring of stroke survivors' motor progression throughout rehabilitation.

Theories of embodied cognition hypothesize interdependencies between psychological well-being and physical posture. The purpose of this study was to assess the feasibility of objectively measuring posture, and to explore the relationship between posture and affect and other patient centered outcomes in breast cancer survivors (BCS) with persistent postsurgical pain (PPSP) over a 12-week course of therapeutic Qigong mind-body training. Twenty-one BCS with PPSP attended group Qigong training. Clinical outcomes were pain, fatigue, self-esteem, anxiety, depression, stress and exercise self-efficacy. Posture outcomes were vertical spine and vertical head angles in the sagittal plane, measured with a 3D motion capture system in three conditions: eyes open (EO), eyes open relaxed (EOR) and eyes closed (EC). Assessments were made before and after the Qigong training. The association between categorical variables (angle and mood) was measured by Cramer’s V. In the EO condition, most participants who improved in fatigue and anxiety scales also had better vertical head values. For the EOR condition, a moderate correlation was observed between changes in vertical head angle and changes in fatigue scale. In the EC condition, most of the participants who improved in measures of fatigue also improved vertical head angle. Additionally, pain severity decreased while vertical spine angle improved. These preliminary findings support that emotion and other patient centered outcomes should be considered within an embodied framework, and that Qigong may be a promising intervention for addressing biopsychosocially complex interventions such as PPSP in BCSs.

The study explored the use of machine learning-based algorithms to estimate clinical scores (meant to capture the quality of upper-limb movements) via the analysis of wearable sensor data collected during the performance of functional tasks. Data was recorded using accelerometer units from thirty-seven individuals with upper-limb hemiparesis due to acquired brain injury while they performed eight activities of daily living. Clinicians assessed the study participants' quality of movement using the Functional Ability Scale. A hierarchical Random Forest-based algorithm was developed to derive the Functional Ability Scale scores from the accelerometer data. The results showed a high correlation (R 2 =0.91) between the estimates derived from the accelerometer data and the scores provided by the clinicians. The method herein presented has the potential to provide a means to track the motor recovery process in patients experiencing upper-limb motor impairments following a stroke or a traumatic brain injury hence enabling precision rehabilitation interventions.

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95).

Parkinson's disease (PD) is a neurodegenerative disorder associated with motor and non-motor symptoms. Current treatments primarily focus on managing motor symptom severity such as tremor, bradykinesia, and rigidity. However, as the disease progresses, treatment side-effects can emerge such as on/off periods and dyskinesia. The objective of the Levodopa Response Study was to identify whether wearable sensor data can be used to objectively quantify symptom severity in individuals with PD exhibiting motor fluctuations. Thirty-one subjects with PD were recruited from 2 sites to participate in a 4-day study. Data was collected using 2 wrist-worn accelerometers and a waist-worn smartphone. During Days 1 and 4, a portion of the data was collected in the laboratory while subjects performed a battery of motor tasks as clinicians rated symptom severity. The remaining of the recordings were performed in the home and community settings. To our knowledge, this is the first dataset collected using wearable accelerometers with specific focus on individuals with PD experiencing motor fluctuations that is made available via an open data repository.

Static and dynamic balance are frequently disrupted through brain injuries. The impairment can be complex and for mild traumatic brain injury (mTBI) can be undetectable by standard clinical tests. Therefore, neurologically relevant modeling approaches are needed for detection and inference of mechanisms of injury. The current work presents models of static and dynamic balance that have a high degree of correspondence. Emphasizing structural similarity between the domains facilitates development of both. Furthermore, particular attention is paid to components of sensory feedback and sensory integration to ground mechanisms in neurobiology. Models are adapted to fit experimentally collected data from 10 healthy control volunteers and 11 mild traumatic brain injury volunteers. Through an analysis by synthesis approach whose implementation was made possible by a state-of-the-art high performance computing system, we derived an interpretable, model based feature set that could classify mTBI and controls in a static balance task with an ROC AUC of 0.72.

OBJECTIVE:Rehabilitation specialists have shown considerable interest for the development of models, based on clinical data, to predict the response to rehabilitation interventions in stroke and traumatic brain injury survivors. However, accurate predictions are difficult to obtain due to the variability in patients' response to rehabilitation interventions. This study aimed to investigate the use of wearable technology in combination with clinical data to predict and monitor the recovery process and assess the responsiveness to treatment on an individual basis.METHODS:Gaussian Process Regression-based algorithms were developed to estimate rehabilitation outcomes (i.e., Functional Ability Scale scores) using either clinical or wearable sensor data or a combination of the two.RESULTS:The algorithm based on clinical data predicted rehabilitation outcomes with a Pearson's correlation of 0.79 compared to actual clinical scores provided by clinicians but failed to model the variability in responsiveness to the intervention observed across individuals. In contrast, the algorithm based on wearable sensor data generated rehabilitation outcome estimates with a Pearson's correlation of 0.91 and modeled the individual responses to rehabilitation more accurately. Furthermore, we developed a novel approach to combine estimates derived from the clinical data and the sensor data using a constrained linear model. This approach resulted in a Pearson's correlation of 0.94 between estimated and clinician-provided scores.CONCLUSION:This algorithm could enable the design of patient-specific interventions based on predictions of rehabilitation outcomes relying on clinical and wearable sensor data.SIGNIFICANCE:This is important in the context of developing precision rehabilitation interventions.

Current clinical tests lack the sensitivity needed for detecting subtle balance impairments associated with mild traumatic brain injury (mTBI). Patient-reported symptoms can be significant and have a huge impact on daily life, but impairments may remain undetected or poorly quantified using clinical measures. Our central hypothesis was that provocative sensorimotor perturbations, delivered in a highly instrumented, immersive virtual environment, would challenge sensory subsystems recruited for balance through conflicting multi-sensory evidence, and therefore reveal that not all subsystems are performing optimally. The results show that, as compared to standard clinical tests, the provocative perturbations illuminate balance impairments in subjects who have had mild traumatic brain injuries. Perturbations delivered while subjects were walking provided greater discriminability (average accuracy ≈ 0.90) than those delivered during standing (average accuracy ≈ 0.65) between mTBI subjects and healthy controls. Of the categories of features extracted to characterize balance, the lower limb accelerometry-based metrics proved to be most informative. Further, in response to perturbations, subjects with an mTBI utilized hip strategies more than ankle strategies to prevent loss of balance and also showed less variability in gait patterns. We have shown that sensorimotor conflicts illuminate otherwise-hidden balance impairments, which can be used to increase the sensitivity of current clinical procedures. This augmentation is vital in order to robustly detect the presence of balance impairments after mTBI and potentially define a phenotype of balance dysfunction that enhances risk of injury.

Purpose: To assess the feasibility, safety, and preliminary effectiveness of a 12-week multimodal Qigong Mind-Body Exercise (QMBE) program for breast cancer survivors with persistent post-surgical pain (PPSP). Methods: This was a single-arm mixed-methods pilot study. Primary outcome measures were feasibility (recruitment, adherence) and safety. Validated self-report questionnaires were used to evaluate a constellation of interdependent symptoms, including pain, fatigue, mood, exercise, interoceptive awareness, and health-related quality of life at baseline and 12 weeks. A subset of the instruments was administered 6 months postintervention. Shoulder range of motion and grip strength were objectively assessed at baseline and 12 weeks. Qualitative interviews were conducted at baseline and 12 weeks. Results: Twenty-one participants were enrolled; 18 and 17 participants, respectively, completed the 12-week and 6-month outcome assessment. No serious adverse events were reported. Statistically significant improvements were observed at 12 weeks in pain severity and interference, fatigue, anxiety, depression, perceived stress, self-esteem, pain catastrophizing, and several subdomains of quality of life, interoceptive awareness, and shoulder range of motion. Changes in pain, fatigue, pain catastrophizing, anxiety, depression, and quality of life were clinically meaningful. Postintervention effects were sustained at 6 months. Conclusions: QMBE is a safe and gentle multimodal intervention that shows promise in conferring a broad range of psychosocial and physical benefits for breast cancer survivors with PPSP. Results support the value of future studies evaluating the impact of QMBE on multiple outcomes relevant to breast cancer survivors with PPSP.

Accurately monitoring motor and non-motor symptoms as well as complications in people with Parkinson’s disease (PD) is a major challenge, both during clinical management and when conducting clinical trials investigating new treatments. A variety of strategies have been relied upon including questionnaires, motor diaries, and the serial administration of structured clinical exams like part III of the MDS-UPDRS. To evaluate the potential use of mobile and wearable technologies in clinical trials of new pharmacotherapies targeting PD symptoms, we carried out a project (project BlueSky) encompassing four clinical studies, in which 60 healthy volunteers (aged 23–69; 33 females) and 95 people with PD (aged 42–80; 37 females; years since diagnosis 1–24 years; Hoehn and Yahr 1–3) participated and were monitored in either a laboratory environment, a simulated apartment, or at home and in the community. In this paper, we investigated (i) the utility and reliability of self-reports for describing motor fluctuations; (ii) the agreement between participants and clinical raters on the presence of motor complications; (iii) the ability of video raters to accurately assess motor symptoms, and (iv) the dynamics of tremor, dyskinesia, and bradykinesia as they evolve over the medication cycle. Future papers will explore methods for estimating symptom severity based on sensor data. We found that 38% of participants who were asked to complete an electronic motor diary at home missed ~25% of total possible entries and otherwise made entries with an average delay of >4 h. During clinical evaluations by PD specialists, self-reports of dyskinesia were marked by ~35% false negatives and 15% false positives. Compared with live evaluation, the video evaluation of part III of the MDS-UPDRS significantly underestimated the subtle features of tremor and extremity bradykinesia, suggesting that these aspects of the disease may be underappreciated during remote assessments. On the other hand, live and video raters agreed on aspects of postural instability and gait. Our results highlight the significant opportunity for objective, high-resolution, continuous monitoring afforded by wearable technology to improve upon the monitoring of PD symptoms.

Between 15% to 40% of mild traumatic brain injury (mTBI) patients experience incomplete recoveries or provide subjective reports of decreased motor abilities, despite a clinically-determined complete recovery. This demonstrates a need for objective measures capable of detecting subclinical residual mTBI, particularly in return-to-duty decisions for warfighters and return-to-play decisions for athletes. In this paper, we utilize features from recordings of directed speech and gait tasks completed by ten healthy controls and eleven subjects with lingering subclinical impairments from an mTBI. We hypothesize that decreased coordination and precision during fine motor movements governing speech production (articulation, phonation, and respiration), as well as during gross motor movements governing gait, can be effective indicators of subclinical mTBI. Decreases in coordination are measured from correlations of vocal acoustic feature time series and torso acceleration time series. We apply eigenspectra derived from these correlations to machine learning models to discriminate between the two subject groups. The fusion of correlation features derived from acoustic and gait time series achieve an AUC of 0.98. This highlights the potential of using the combination of vocal acoustic features from speech tasks and torso acceleration during a simple gait task as a rapid screening tool for subclinical mTBI.1

Goal: The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise in electronic Patient-Reported Outcomes (ePRO), wearable sensors, and digital contact tracing technologies. Its members collected and discussed available information and summarized it in a series of reports. Results: The Task Force identified technologies that could be deployed in response to the COVID-19 pandemic and would likely be suitable for future pandemics. Criteria for their evaluation were agreed upon and applied to these systems. Conclusions: mHealth technologies are viable options to monitor COVID-19 patients and be used to predict symptom escalation for earlier intervention. These technologies could also be utilized to monitor individuals who are presumed non-infected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing.

Objectives: Breast cancer treatment leaves breast cancer survivors (BCS) with an array of lasting side effects, including persistent postsurgical pain (PPSP). In this study, we explored the perceptions of BCS with PPSP as they learned Qigong mind-body exercise (QMBE), a multimodal practice rooted in Traditional Chinese Medicine. Methods: Participants included 18 female BCS treated for stage 0-III breast cancer and experiencing PPSP. Participants were taught QMBE over 12 weeks. Semi-structured interviews were conducted before and after the intervention. Results: BCS disclosed a disconnect between mind and body that emerged during treatment. They perceived QMBE as moving meditation, which enabled them to reconnect mind and body, lessen their pain, and make peace with their bodies. Conclusion: These women's experiences both inform the promise of integrating QMBE and related mind-body exercise into PPSP clinical practice guidelines and suggest new areas of research regarding the role of multimodal interventions for holistic healing in BCS.