Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment

Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach

BACKGROUND

Stroke therapy is essential to reduce impairments and improve motor movements by engaging autogenous neuroplasticity. Traditionally, stroke rehabilitation occurs in inpatient and outpatient rehabilitation facilities. However, recent literature increasingly explores moving the recovery process into the home and integrating technology-based interventions. This study advances this goal by promoting in-home, autonomous recovery for patients who experienced a stroke through robotics-assisted rehabilitation and classifying stroke residual severity using machine learning methods.

OBJECTIVE

Our main objective is to use kinematics data collected during in-home, self-guided therapy sessions to develop supervised machine learning methods, to address a clinician's autonomous classification of stroke residual severity-labeled data toward improving in-home, robotics-assisted stroke rehabilitation.

METHODS

In total, 33 patients who experienced a stroke participated in in-home therapy sessions using Motus Nova robotics rehabilitation technology to capture upper and lower body motion. During each therapy session, the Motus Hand and Motus Foot devices collected movement data, assistance data, and activity-specific data. We then synthesized, processed, and summarized these data. Next, the therapy session data were paired with clinician-informed, discrete stroke residual severity labels: "no range of motion (ROM)," "low ROM," and "high ROM." Afterward, an 80%:20% split was performed to divide the dataset into a training set and a holdout test set. We used 4 machine learning algorithms to classify stroke residual severity: light gradient boosting (LGB), extra trees classifier, deep feed-forward neural network, and classical logistic regression. We selected models based on 10-fold cross-validation and measured their performance on a holdout test dataset using F1-score to identify which model maximizes stroke residual severity classification accuracy.

RESULTS

We demonstrated that the LGB method provides the most reliable autonomous detection of stroke severity. The trained model is a consensus model that consists of 139 decision trees with up to 115 leaves each. This LGB model boasts a 96.70% F1-score compared to logistic regression (55.82%), extra trees classifier (94.81%), and deep feed-forward neural network (70.11%).

CONCLUSIONS

We showed how objectively measured rehabilitation training paired with machine learning methods can be used to identify the residual stroke severity class, with efforts to enhance in-home self-guided, individualized stroke rehabilitation. The model we trained relies only on session summary statistics, meaning it can potentially be integrated into similar settings for real-time classification, such as outpatient rehabilitation facilities.

Improving patient outcomes in acute and subacute stroke using a wearable device-assisted rehabilitation system: a randomized controlled trial

OBJECTIVE

Multidisciplinary rehabilitation facilitates post-stroke functional recovery, but is associated with resource and accessibility barriers. This study evaluated the combination of a wearable device-assisted system (WEAR) and conventional therapy for post-stroke rehabilitation.

METHODS

This randomized, controlled, parallel group, clinical trial was conducted at two rehabilitation centers. A WEAR system was developed featuring sensors and application program-embedded smartphones. Stroke patients within 12 weeks of onset and modified Rankin Scale (mRS) scores of 2 to 4 were randomized into a wearable group (WG, WEAR + conventional rehabilitation) or control group (CG, conventional rehabilitation) for 90 days. The primary outcome was mRS score changes within 90 days.

RESULTS

Among 127 stroke patients enrolled (76 men [59.8%]; mean age: 57.5 years), 63 and 64 patients were randomized to WG and CG, respectively. Both groups showed significant improvements in mRS scores. Between-group repeated measures analysis adjusted for sex, age and number of rehabilitation sessions showed greater improvement in mRS scores within 90 days in the WG than in the CG (estimate: 0.73).

CONCLUSIONS

This combined WEAR and conventional rehabilitation approach may improve post-stroke functional recovery compared with conventional rehabilitation alone. The WEAR system permits remote monitoring and recording of rehabilitation in various settings.This clinical trial was retrospectively registered at www.clinicaltrials.gov with the Unique Identifier NCT04997408.

Effects of wearable device training on upper limb motor function in patients with stroke: a systematic review and meta-analysis

OBJECTIVE

To evaluate the effect of wearable device training on improving upper limb motor function in patients who experienced strokes.

METHODS

The PubMed, Embase, Cochrane Library, Web of Science, MEDLINE, SCOPUS, China National Knowledge Infrastructure, WanFang, and VIP databases were searched for randomized controlled trials (RCTs) that assessed the effectiveness of wearable device training in improving upper limb motor function in patients with stroke. Two investigators independently screened studies by their titles and abstracts and cross-checked, downloaded, and evaluated the results. Disagreements were resolved by a third highly experienced researcher. Risk of bias was evaluated using the Cochrane risk-of-bias tool. This meta-analysis was registered in PROSPERO (registration No. CRD42023421633).

RESULTS

This study comprised 508 patients from 14 RCTs. The experimental group assessed various wearable devices, including 3D-printed dynamic orthoses, inertial measurement unit (IMU) sensors, electrical stimulation devices, and virtual reality (VR) devices for virtual interactive training. The control group received traditional rehabilitation therapies, including physical and conventional rehabilitation. The experimental group scored better on the Fugl-Meyer Assessment (FMA-UE) scale (standardized mean difference [SMD] 0.26, 95% confidence interval [CI] 0.07, 0.45) and Box and Block Test (BBT) (SMD 0.43, 95% CI 0.17, 0.69) versus controls. No significant intergroup differences were observed in the Action Research Arm Test (SMD 0.20, 95% CI -0.15, 0.55), motor activity log (mean difference [MD] 0.32, 95% CI -0.54, 0.33), and modified Ashworth scale (MD -0.08, 95% CI -0.81, 0.64). The probability rankings of wearable devices that improved FMA-UE scores in patients with stroke were: orthotic devices, with the highest probability ranking of 0.45, followed by sensor devices at 0.23, electrical stimulation devices at 0.21, and VR devices at 0.11.

CONCLUSIONS

Wearable device training was found to significantly improve upper limb motor function in patients with stroke, particularly for large-range movements. Improvements in FMA-UE and BBT scores reflected reduced impairment and enhanced manual dexterity, respectively. However, the training had no significant effect on hand movement frequency, fine motor skills, or spasticity. Among the different wearable devices tested, orthoses produced the most effective results.

Effects of a Wearable-Based Intervention on the Hemiparetic Upper Limb in Persons With Stroke: A Randomized Controlled Trial

INTRODUCTION

Wearables have emerged as a transformative rehabilitation tool to provide self-directed training in the home. Objective. In this study, we examined the efficacy of a novel wearable device, "Smart Reminder" (SR), to provide home-based telerehabilitation for hemiparetic upper limb (UL) training in persons with stroke.

METHODS

Forty stroke survivors from community support groups were randomized (stratified by the period after stroke onset and impairment severity) to either the SR group or the sham device group. Participants received either 20 hours of telerehabilitation using the SR device or training with pictorial handouts and a sham device over 4 weeks. In addition, all participants wore a standard accelerometer for 3 hours each day, 5 times a week, outside the prescribed training. Participants were assessed by a masked assessor at baseline, post-intervention (week 4), and follow-up (week 8). The outcome measures included Fugl-Meyer Assessment for Upper Extremity (FMA-UE), Action Research Arm Test, Motor Activity Log, muscle strength, active range of motion and amount of movement of the UL, and compliance rate of training.

RESULTS

The SR group improved substantially in their FMA-UE scores after treatment (mean difference = 2.05, P = .036) compared to the sham group. Also, adherence to the training using the SR device was significantly higher, 97%, than the sham group, 82.3% (P = .038).

CONCLUSION

The 4-week telerehabilitation program using a "SR" device demonstrated potential efficacy in improving FMA-UE scores of the hemiparetic upper limb. However, it did not significantly enhance the performance of the affected limb in daily activities. The trial was registered on ClinicalTrial.gov (URL: http://www.clinicaltrials.gov) with the identifier NCT05877183.

Remotely Assessing Motor Function and Activity of the Upper Extremity After Stroke: A Systematic Review of Validity and Clinical Utility of Tele-Assessments

OBJECTIVE

The aim of this systematic review is to identify currently available tele-assessments for motor impairments of the upper extremity in adults after a stroke and to assess their psychometric properties and clinical utility.

DATA SOURCES

We searched for studies describing the psychometric properties of tele-assessments for the motor function of the upper extremity. A systematic search was conducted in the Cumulative Index to Nursing and Allied Health Literature, Medline via OVID, Embase, The Cochrane Library, Scopus, Web of Science and Institute of Electrical and Electronics Engineers Xplore from inception until 30 April 2024.

REVIEW METHODS

The quality assessment for the included studies and the rating of the psychometric properties were performed using the COSMIN Risk of Bias Checklist for systematic reviews of patient-reported outcome measures.

RESULTS

A total of 12 studies (N = 3912) describing 11 tele-assessments met the predefined inclusion criteria. The included assessments were heterogeneous in terms of quality and psychometric properties and risk of bias. None of the tele-assessments currently meets the criteria of clinical utility to be recommended for clinical practice without restriction.

CONCLUSION

The quality and clinical utility of tele-assessments varied widely, suggesting a cautious consideration for immediate clinical practice application. There is potential for tele-assessments in clinical practice, but the clinical benefits need to be improved by simplifying the complexity of tele-assessments.

REGISTRATION NUMBER

CRD42022335035.

Automatic rehabilitation exercise task assessment of stroke patients based on wearable sensors with a lightweight multichannel 1D-CNN model

Approximately 75% of stroke survivors have movement dysfunction. Rehabilitation exercises are capable of improving physical coordination. They are mostly conducted in the home environment without guidance from therapists. It is impossible to provide timely feedback on exercises without suitable devices or therapists. Human action quality assessment in the home setting is a challenging topic for current research. In this paper, a low-cost HREA system in which wearable sensors are used to collect upper limb exercise data and a multichannel 1D-CNN framework is used to automatically assess action quality. The proposed 1D-CNN model is first pretrained on the UCI-HAR dataset, and it achieves a performance of 91.96%. Then, five typical actions were selected from the Fugl-Meyer Assessment Scale for the experiment, wearable sensors were used to collect the participants' exercise data, and experienced therapists were employed to assess participants' exercise at the same time. Following the above process, a dataset was built based on the Fugl-Meyer scale. Based on the 1D-CNN model, a multichannel 1D-CNN model was built, and the model using the Naive Bayes fusion had the best performance (precision: 97.26%, recall: 97.22%, F1-score: 97.23%) on the dataset. This shows that the HREA system provides accurate and timely assessment, which can provide real-time feedback for stroke survivors' home rehabilitation.

Reliability of Surface Electromyography From the Lower-limb Muscles During Maximal and Submaximal Voluntary Isometric Contractions in In-bed Healthy Individuals and Patients With Subacute Stroke

This study aims to develop maximal voluntary isometric contraction (MVIC) and submaximal voluntary isometric contraction (subMVIC) methods and to assess the reliability of the developed methods for in-bed healthy individuals and patients with subacute stroke. The electromyography (EMG) activities from the lower-limb muscles including the tensor fascia lata (TFL), rectus femoris (RF), tibialis anterior (TA), and gastrocnemius (GC) on both sides were recorded during MVIC and subMVIC using surface EMG sensors in 20 healthy individuals and 20 subacute stroke patients. In inter-trial reliability, both MVIC and subMVIC methods demonstrated excellent reliability for all the measured muscles at baseline and follow-up evaluations in both healthy individuals and stroke patients. In inter-day reliability, MVIC showed good reliability for the TFL and moderate reliability for the RF, TA, and GC, while subMVIC showed good reliability for the TFL, RF, and GC and poor reliability for the TA in healthy individuals. In conclusion, the MVIC and subMVIC methods of EMG activities were feasible in in-bed healthy individuals and patients with subacute stroke. The results can serve as a basis for the clinical evaluation of muscular activities using quantitative EMG signals on the lower-limb muscles in stroke patients with impaired mobility.

Transferring Sensor-Based Assessments to Clinical Practice: The Case of Muscle Synergies

Sensor-based assessments in medical practice and rehabilitation include the measurement of physiological signals such as EEG, EMG, ECG, heart rate, and NIRS, and the recording of movement kinematics and interaction forces. Such measurements are commonly employed in clinics with the aim of assessing patients' pathologies, but so far some of them have found full exploitation mainly for research purposes. In fact, even though the data they allow to gather may shed light on physiopathology and mechanisms underlying motor recovery in rehabilitation, their practical use in the clinical environment is mainly devoted to research studies, with a very reduced impact on clinical practice. This is especially the case for muscle synergies, a well-known method for the evaluation of motor control in neuroscience based on multichannel EMG recordings. In this paper, considering neuromotor rehabilitation as one of the most important scenarios for exploiting novel methods to assess motor control, the main challenges and future perspectives for the standard clinical adoption of muscle synergy analysis are reported and critically discussed.

A Review of Wearable Optical Fiber Sensors for Rehabilitation Monitoring

As the global aging population increases, the demand for rehabilitation of elderly hand conditions has attracted increased attention in the field of wearable sensors. Owing to their distinctive anti-electromagnetic interference properties, high sensitivity, and excellent biocompatibility, optical fiber sensors exhibit substantial potential for applications in monitoring finger movements, physiological parameters, and tactile responses during rehabilitation. This review provides a brief introduction to the principles and technologies of various fiber sensors, including the Fiber Bragg Grating sensor, self-luminescent stretchable optical fiber sensor, and optic fiber Fabry-Perot sensor. In addition, specific applications are discussed within the rehabilitation field. Furthermore, challenges inherent to current optical fiber sensing technology, such as enhancing the sensitivity and flexibility of the sensors, reducing their cost, and refining system integration, are also addressed. Due to technological developments and greater efforts by researchers, it is likely that wearable optical fiber sensors will become commercially available and extensively utilized for rehabilitation.

Understanding the Sociocultural Challenges and Opportunities for Affordable Wearables to Support Poststroke Upper-Limb Rehabilitation: Qualitative Study

Background

People who survive a stroke in many cases require upper-limb rehabilitation (ULR), which plays a vital role in stroke recovery practices. However, rehabilitation services in the Global South are often not affordable or easily accessible. For example, in Bangladesh, the access to and use of rehabilitation services is limited and influenced by cultural factors and patients' everyday lives. In addition, while wearable devices have been used to enhance ULR exercises to support self-directed home-based rehabilitation, this has primarily been applied in developed regions and is not common in many Global South countries due to potential costs and limited access to technology.

Objective

Our goal was to better understand physiotherapists', patients', and caregivers' experiences of rehabilitation in Bangladesh, existing rehabilitation practices, and how they differ from the rehabilitation approach in the United Kingdom. Understanding these differences and experiences would help to identify opportunities and requirements for developing affordable wearable devices that could support ULR in home settings.

Methods

We conducted an exploratory study with 14 participants representing key stakeholder groups. We interviewed physiotherapists and patients in Bangladesh to understand their approaches, rehabilitation experiences and challenges, and technology use in this context. We also interviewed UK physiotherapists to explore the similarities and differences between the 2 countries and identify specific contextual and design requirements for low-cost wearables for ULR. Overall, we remotely interviewed 8 physiotherapists (4 in the United Kingdom, 4 in Bangladesh), 3 ULR patients in Bangladesh, and 3 caregivers in Bangladesh. Participants were recruited through formal communications and personal contacts. Each interview was conducted via videoconference, except for 2 interviews, and audio was recorded with consent. A total of 10 hours of discussions were transcribed. The results were analyzed using thematic analysis.

Results

We identified several sociocultural factors that affect ULR and should be taken into account when developing technologies for the home: the important role of family, who may influence the treatment based on social and cultural perceptions; the impact of gender norms and their influence on attitudes toward rehabilitation and physiotherapists; and differences in approach to rehabilitation between the United Kingdom and Bangladesh, with Bangladeshi physiotherapists focusing on individual movements that are necessary to build strength in the affected parts and their British counterparts favoring a more holistic approach. We propose practical considerations and design recommendations for developing ULR devices for low-resource settings.

Conclusions

Our work shows that while it is possible to build a low-cost wearable device, the difficulty lies in addressing sociotechnical challenges. When developing new health technologies, it is imperative to not only understand how well they could fit into patients', caregivers', and physiotherapists' everyday lives, but also how they may influence any potential tensions concerning culture, religion, and the characteristics of the local health care system.

Can We Target Close Therapeutic Goals in the Gait Re-Education Algorithm for Stroke Patients at the Beginning of the Rehabilitation Process?

(1) Background: The study aimed to determine the most important activities of the knee joints related to gait re-education in patients in the subacute period after a stroke. We focused on the tests that a physiotherapist could perform in daily clinical practice. (2) Methods: Twenty-nine stroke patients (SG) and 29 healthy volunteers (CG) were included in the study. The patients underwent the 5-meter walk test (5mWT) and the Timed Up and Go test (TUG). Tests such as step up, step down, squat, step forward, and joint position sense test (JPS) were also performed, and the subjects were assessed using wireless motion sensors. (3) Results: We observed significant differences in the time needed to complete the 5mWT and TUG tests between groups. The results obtained in the JPS show a significant difference between the paretic and the non-paretic limbs compared to the CG group. A significantly smaller range of knee joint flexion (ROM) was observed in the paretic limb compared to the non-paretic and control limbs in the step down test and between the paretic and non-paretic limbs in the step forward test. (4) Conclusions: The described functional tests are useful in assessing a stroke patient's motor skills and can be performed in daily clinical practice.

A pilot exploration of staff and service-user perceptions of a novel digital health technology (Virtual Engagement Rehabilitation Assistant) in complex inpatient rehabilitation

PURPOSE

Digital health technologies have the potential to advance rehabilitation. The Virtual Engagement Rehabilitation Assistant (VERA) is a digital technology, co-designed to increase service-user engagement and promote self-management. This qualitative study explored staff and service-user perceptions of implementing VERA on a UK complex inpatient rehabilitation ward.

METHODS

Purposively sampled service-users were allocated to VERA for up to six weeks. The Non-adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework underpinned service-user post-intervention interviews and staff focus groups, and structured analysis of the data. Seven service-users were interviewed. Nine staff contributed to focus groups.

RESULTS

A framework analysis identified themes (and subthemes) structured by the NASSS framework domains: 1. Nature of Clinical Condition, 2. Technology (Ease of Use, Holding Information/Resources in a single Digital Location, Appointments), 3. Value Proposition (Structuring Time, Feedback, Unexpected Benefits) 4. Adopters (Confidence in using Technology, Usefulness), 5. Wider Organisation. Ease of use and storage of key information in a single location were beneficial. Reliability, and provision of accurate and timely feedback to staff and service-users, were identified as essential.

CONCLUSIONS

A blended approach is required to meet staff and service-user needs. The potential for VERA in a community setting was identified and requires further investigation. Learning from VERA will support development of other digital technologies and their implementation.

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

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

Addressing Data Absenteeism and Technology Chauvinism in the Use of Gamified Wearable Gloves Among Older Adults: Moderated Usability Study

BACKGROUND

Digital health technologies have the potential to improve health outcomes for older adults, especially for those recovering from stroke. However, there are challenges to developing these technologies, such as data absenteeism (where older adults' views are often underrepresented in research and development) and technology chauvinism (the belief that sophisticated technology alone is the panacea to addressing health problems), which hinder their effectiveness.

OBJECTIVE

In this study, we aimed to address these challenges by developing a wearable glove integrated with culturally relevant exergames to motivate older adults to exercise and, for those recovering from stroke, to adhere to rehabilitation.

METHODS

We conducted a moderated usability study with 19 older adults, of which 11 (58%) had a history of stroke. Our participants engaged in a 30-minute gameplay session with the wearable glove integrated with exergames, followed by a quantitative survey and an in-depth interview. We used descriptive analysis to compare responses to the System Usability Scale between those who had a history of stroke and those who did not. In addition, we analyzed the qualitative interviews using a bottom-up thematic analysis to identify key themes related to the motivations and barriers regarding the use of wearable gloves for rehabilitation and exercise.

RESULTS

Our study generated several key insights. First, making the exergames exciting and challenging could improve exercise and rehabilitation motivation, but it could also have a boomerang effect, where participants may become demotivated if the games were very challenging. Second, the comfort and ease of use of the wearable gloves were important for older adults, regardless of their stroke history. Third, for older adults with a history of stroke, the functionality and purpose of the wearable glove were important in helping them with specific exercise movements.

CONCLUSIONS

Our findings highlight the importance of providing contextual support for the effective use of digital technologies, particularly for older adults recovering from stroke. In addition to technology and usability factors, other contextual factors such as gamification and social support (from occupational therapists or caregivers) should be considered to provide a comprehensive approach to addressing health problems. To overcome data absenteeism and technology chauvinism, it is important to develop digital health technologies that are tailored to the needs of underserved communities. Our study provides valuable insights for the development of digital health technologies that can motivate older adults recovering from stroke to exercise and adhere to rehabilitation.

Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements

Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals' physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.

NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Design of a Wearable Real-Time Hand Motion Tracking System Using an Array of Soft Polymer Acoustic Waveguides

Robust hand motion tracking holds promise for improved human-machine interaction in diverse fields, including virtual reality, and automated sign language translation. However, current wearable hand motion tracking approaches are typically limited in detection performance, wearability, and durability. This article presents a hand motion tracking system using multiple soft polymer acoustic waveguides (SPAWs). The innovative use of SPAWs as strain sensors offers several advantages that address the limitations. SPAWs are easily manufactured by casting a soft polymer shaped as a soft acoustic waveguide and containing a commercially available small ceramic piezoelectric transducer. When used as strain sensors, SPAWs demonstrate high stretchability (up to 100%), high linearity (R2 > 0.996 in all quasi-static, dynamic, and durability tensile tests), negligible hysteresis (<0.7410% under strain of up to 100%), excellent repeatability, and outstanding durability (up to 100,000 cycles). SPAWs also show high accuracy for continuous finger angle estimation (average root-mean-square errors [RMSE] <2.00°) at various flexion-extension speeds. Finally, a hand-tracking system is designed based on a SPAW array. An example application is developed to demonstrate the performance of SPAWs in real-time hand motion tracking in a three-dimensional (3D) virtual environment. To our knowledge, the system detailed in this article is the first to use soft acoustic waveguides to capture human motion. This work is part of an ongoing effort to develop soft sensors using both time and frequency domains, with the goal of extracting decoupled signals from simple sensing structures. As such, it represents a novel and promising path toward soft, simple, and wearable multimodal sensors.

Data Augmentation Techniques for Accurate Action Classification in Stroke Patients with Hemiparesis

Stroke survivors with hemiparesis require extensive home-based rehabilitation. Deep learning-based classifiers can detect actions and provide feedback based on patient data; however, this is difficult owing to data sparsity and heterogeneity. In this study, we investigate data augmentation and model training strategies to address this problem. Three transformations are tested with varying data volumes to analyze the changes in the classification performance of individual data. Moreover, the impact of transfer learning relative to a pre-trained one-dimensional convolutional neural network (Conv1D) and training with an advanced InceptionTime model are estimated with data augmentation. In Conv1D, the joint training data of non-disabled (ND) participants and double rotationally augmented data of stroke patients is observed to outperform the baseline in terms of F1-score (60.9% vs. 47.3%). Transfer learning pre-trained with ND data exhibits 60.3% accuracy, whereas joint training with InceptionTime exhibits 67.2% accuracy under the same conditions. Our results indicate that rotational augmentation is more effective for individual data with initially lower performance and subset data with smaller numbers of participants than other techniques, suggesting that joint training on rotationally augmented ND and stroke data enhances classification performance, particularly in cases with sparse data and lower initial performance.

The efficacy of contralaterally controlled functional electrical stimulation compared to conventional neuromuscular electrical stimulation for recovery of limb function following a stroke: a systematic review and meta-analysis

Introduction

Limb paresis following a stroke is a common sequela that can impact patients' quality of life. Many rehabilitation strategies targeting the restoration of motor function exist. This systematic review and meta-analysis aim to evaluate the effects of contralaterally controlled functional electrical stimulation (CCFES) as a modality for limb rehabilitation. Unlike conventional neuromuscular electrical simulation (NMES), the contra-laterality in CCFES is achieved by two methods a bend angle sensor or an electromyographic bridge (EMGB) method, both of which targets signals from the unaffected limb.

Method

This review study was performed following the preferred reporting item for systematic review and meta-analysis (PRISMA) guidelines. Records that met the inclusion criteria were extracted from the following databases: Medline, Embase, and Cochrane Register of Controlled Trials (CENTRAL). Additional articles were also retrieved from clinicaltrials.gov and China/Asia on Demand (CAOD). Only randomized controlled studies (RCTs) were included.

Results

Sixteen RCTs met the inclusion criteria, and 14 of which were included in the quantitative analysis (meta-analysis). The results of the analysis show that when compared to conventional NMES, CCFES displayed a better improvement in the upper extremity Fugl-Meyer assessment (UEFMA) (SMD = 0.41, 95% CI: 0.21, 0.62, p-value <0.0001, I2 = 15%, GRADE: moderate), box and blocks test (BBT) (SMD = 0.48, 95% CI: 0.10, 0.86, p-value = 0.01, I2 = 0%, GRADE: very low), modified Barthel index (mBI) (SMD = 0.44, 95% CI: 0.16, 0.71, p-value = 0.002, I2 = 0%, GRADE: moderate), active range of motion (AROM) (SMD = 0.61, 95% CI: 0.29, 0.94, p-value = 0.0002, I2 = 23%, GRADE: moderate), and surface electromyography (sEMG) scores (SMD = 0.52, 95% CI: 0.14, 0.90, p-value = 0.008, I2 = 0%, GRADE: low). The results of the subgroup analysis for the type of sensor used in CCFES shows that an EMGB (SMD = 0.58, 95% CI: 0.33, 0.84, p-value <0.00001, I2 = 7%) is more effective than a bend angle sensor (SMD = 0.17, 95% CI: -0.12, 0.45, p-value = 0.25, I2 = 0%).

Conclusion

The results of this study provide strong evidence that shows CCFES being a better electrical stimulation modality compared to conventional NMES. This could be explained by the fact that CCFES is bilateral in nature which offers a platform for better neuroplasticity following a stroke. There is still a need for high-quality studies with a standardized approach comparing CCFES to other treatment modalities.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=342670, identifier CRD42022342670.

Detection and Assessment of Point-to-Point Movements During Functional Activities Using Deep Learning and Kinematic Analyses of the Stroke-Affected Wrist

Stoke is a leading cause of long-term disability, including upper-limb hemiparesis. Frequent, unobtrusive assessment of naturalistic motor performance could enable clinicians to better assess rehabilitation effectiveness and monitor patients' recovery trajectories. We therefore propose and validate a two-phase data analytic pipeline to estimate upper-limb impairment based on the naturalistic performance of activities of daily living (ADLs). Eighteen stroke survivors were equipped with an inertial sensor on the stroke-affected wrist and performed up to four ADLs in a naturalistic manner. Continuous inertial time series were segmented into sliding windows, and a machine-learned model identified windows containing instances of point-to-point (P2P) movements. Using kinematic features extracted from the detected windows, a subsequent model was used to estimate upper-limb motor impairment, as measured by the Fugl-Meyer Assessment (FMA). Both models were evaluated using leave-one-subject-out cross-validation. The P2P movement detection model had an area under the precision-recall curve of 0.72. FMA estimates had a normalized root mean square error of 18.8% with R2=0.72. These promising results support the potential to develop seamless, ecologically valid measures of real-world motor performance.

Ten meter walk test with mobile devices: A dataset with accelerometer, magnetometer, and gyroscope

This paper presents a dataset related to the performance of the Ten Meter Walking Test, a test to allow locomotor capacity in different research and clinical settings. One of the most important parameters to measure is the gait speed during a path of ten meters. The data available in this dataset consists of accelerometer, magnetometer, and gyroscope data acquired with a mobile device in a waistband. The experiments were performed two times by 109 individuals (30 males and 79 females) in different senior residences in the Fundão municipality (Portugal). The dataset includes 208 samples because the sensors reported some failures. The acquisition of the sensors data allows the creation of a technological method for the automatic measurement of features related to the Ten Meter Walk Test, promoting patient independence in measuring their physical health status.

'Smart reminder': A feasibility pilot study on the effects of a wearable device treatment on the hemiplegic upper limb in persons with stroke

INTRODUCTION

Emerging literature suggests that wearable devices offer a promising option for self-directed home-based upper limb training for persons with stroke. However, little research is available to explore integrating smartphone applications with wearable devices to provide upper limb telerehabilitation to stroke survivors at home. This study examined the feasibility and potential therapeutic effects of a wearable device integrated with a smartphone-based telerehabilitation system to provide upper limb rehabilitation to stroke survivors at home.

METHODS

Twelve stroke survivors from community support groups participated in a treatment consisting of 4-week telerehabilitation using a wearable device and 4-week conventional therapy successively in a single-blind, randomised crossover study. A 3-week washout period was administered between the two 4-week treatments. The primary outcome measures were the Fugl Meyer Assessment, the Action Research Arm Test, and the active range of motion (ROM) of the upper limb. Secondary outcome measures included the Motor Activity Log and exercise adherence.

RESULTS

Results showed that the active ROM of participants' hemiplegic shoulder improved more significantly after 4 weeks of telerehabilitation with the wearable device than with conventional therapy. No significant differences were found in other outcome measures.

CONCLUSIONS

A 4-week telerehabilitation programme using a wearable device improves the hemiplegic upper limb in community-dwelling stroke survivors and may be feasible as an effective intervention for self-directed upper limb rehabilitation at home.

A review of combined functional neuroimaging and motion capture for motor rehabilitation

BACKGROUND

Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate the diagnosis and rehabilitation of motor deficits. These advancements allow for the synchronous acquisition and analysis of complex signal streams of neurophysiological data (e.g., EEG, fNIRS) and behavioral data (e.g., motion capture). The fusion of those data streams has the potential to provide new insights into cortical mechanisms during movement, guide the development of rehabilitation practices, and become a tool for assessment and therapy in neurorehabilitation.

RESEARCH OBJECTIVE

This paper aims to review the existing literature on the combined use of motion capture and functional neuroimaging in motor rehabilitation. The objective is to understand the diversity and maturity of technological solutions employed and explore the clinical advantages of this multimodal approach.

METHODS

This paper reviews literature related to the combined use of functional neuroimaging and motion capture for motor rehabilitation following the PRISMA guidelines. Besides study and participant characteristics, technological aspects of the used systems, signal processing methods, and the nature of multimodal feature synchronization and fusion were extracted.

RESULTS

Out of 908 publications, 19 were included in the final review. Basic or translation studies were mainly represented and based predominantly on healthy participants or stroke patients. EEG and mechanical motion capture technologies were most used for biomechanical data acquisition, and their subsequent processing is based mainly on traditional methods. The system synchronization techniques at large were underreported. The fusion of multimodal features mainly supported the identification of movement-related cortical activity, and statistical methods were occasionally employed to examine cortico-kinematic relationships.

CONCLUSION

The fusion of motion capture and functional neuroimaging might offer advantages for motor rehabilitation in the future. Besides facilitating the assessment of cognitive processes in real-world settings, it could also improve rehabilitative devices' usability in clinical environments. Further, by better understanding cortico-peripheral coupling, new neuro-rehabilitation methods can be developed, such as personalized proprioceptive training. However, further research is needed to advance our knowledge of cortical-peripheral coupling, evaluate the validity and reliability of multimodal parameters, and enhance user-friendly technologies for clinical adaptation.

Healthcare professionals' perspectives on development of assistive technology using the comprehensive assistive technology model

The implementation of technology in healthcare shows promising results and provides new opportunities in rehabilitation. However, the adoption of technology into daily care is largely dependent on the acceptance rate of end-users. This study aims to gather information from healthcare professionals on the development of new assistive technology that match users' needs using the Comprehensive Assistive Technology model. In total 27 healthcare professionals (12 occupational therapists, 8 physiotherapists, 3 nurses, 2 allied health directors, a physician and a speech therapist) attended one of four online focus group discussions. These focus group discussions were structured using a question guide based on three predefined scenarios. Recordings were transcribed and data was analyzed using a thematic analysis (NVivo). Major themes identified in this study were safety, price and usability. Healthcare professionals focused on both functional capabilities of the user, as well as behavioral aspects of usability and attitude toward technology. Furthermore, the need for assistive technology that were catered toward the limitations in activity and user experience, was highlighted extensively. Based on information gathered from healthcare professionals a user-centered approach in development of safe, low-cost devices that maximize both functional outcomes and user acceptance, could potentially increase the adoption of new technology in rehabilitation.

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.

Monitoring walking asymmetries and endpoint control in persons living with chronic stroke: Implications for remote diagnosis and telerehabilitation

Objective

The objective of this study was to assess the feasibility of monitoring and diagnosing compromised walking motion in the frontal plane, particularly in persons living with the chronic effects of stroke (PwCS). The study aimed to determine whether active control of walking in the frontal plane could be monitored and provide diagnostic insights into compensations made by PwCS during community living.

Methods

The study recruited PwCS with noticeable walking asymmetries and employed a monitoring method to assess frontal plane motion. Monitoring was conducted both within a single assessment and between assessments. The study aimed to uncover baseline data and diagnostic information about active control in chronic stroke survivors. Data were collected using sensors during 6 minutes of walking and compared between the paretic and non-paretic legs.

Results

The study demonstrated the feasibility of monitoring frontal plane motion and diagnosing disturbed endpoint control (p < 0.0125) in chronic stroke survivors when comparing the paretic leg to the non-paretic leg. A greater variability was observed in the paretic leg (p < 0.0125), and sensors were able to diagnose a stronger coupling of the body with its endpoint on the paretic side (p < 0.0125). Similar results were obtained when monitoring was conducted over a six-minute walking period, and no significant diagnostic differences were found between the two monitoring assessments. Monitoring did not reveal performance fatigue or debilitation over time.

Conclusions

This study's findings indicate that monitoring frontal plane motion is a feasible approach for diagnosing compromised walking motion. The results suggest that individuals with walking asymmetries, exhibit differences in endpoint control and variability between their paretic and non-paretic legs. These insights could contribute to more effective rehabilitation strategies and highlight the potential for monitoring compensations during various activities of daily living.

SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies

Background/objective

Recent technological advances have allowed for the development of smart wearable devices (SmartWear) which can be used to monitor various aspects of patient healthcare. These devices provide clinicians with continuous biometric data collection for patients in both inpatient and outpatient settings. Although these devices have been widely used in fields such as cardiology and orthopedics, their use in the field of neurosurgery and neurology remains in its infancy.

Methods

A comprehensive literature search for the current and future applications of SmartWear devices in the above conditions was conducted, focusing on outpatient monitoring.

Findings

Through the integration of sensors which measure parameters such as physical activity, hemodynamic variables, and electrical conductivity - these devices have been applied to patient populations such as those at risk for stroke, suffering from epilepsy, with neurodegenerative disease, with spinal cord injury and/or recovering from neurosurgical procedures. Further, these devices are being tested in various clinical trials and there is a demonstrated interest in the development of new technologies.

Conclusion

This review provides an in-depth evaluation of the use of SmartWear in selected neurological diseases and neurosurgical applications. It is clear that these devices have demonstrated efficacy in a variety of neurological and neurosurgical applications, however challenges such as data privacy and management must be addressed.

Investigating Activity Recognition for Hemiparetic Stroke Patients Using Wearable Sensors: A Deep Learning Approach with Data Augmentation

Measuring the daily use of an affected limb after hospital discharge is crucial for hemiparetic stroke rehabilitation. Classifying movements using non-intrusive wearable sensors provides context for arm use and is essential for the development of a home rehabilitation system. However, the movement classification of stroke patients poses unique challenges, including variability and sparsity. To address these challenges, we collected movement data from 15 hemiparetic stroke patients (Stroke group) and 29 non-disabled individuals (ND group). The participants performed two different tasks, the range of motion (14 movements) task and the activities of daily living (56 movements) task, wearing five inertial measurement units in a home setting. We trained a 1D convolutional neural network and evaluated its performance for different training groups: ND-only, Stroke-only, and ND and Stroke jointly. We further compared the model performance with data augmentation from axis rotation and investigated how the performance varied based on the asymmetry of movements. The joint training of ND + Stroke yielded an increased F1-score by a margin of 31.6% and 10.6% compared to ND-only training and Stroke-only training, respectively. Data augmentation further enhanced F1-scores across all conditions by an average of 11.3%. Finally, asymmetric movements decreased the F1-score by 25.9% compared to symmetric movements in the Stroke group, indicating the importance of asymmetry in movement classification.

A Machine Learning App for Monitoring Physical Therapy at Home

Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient's travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient's body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research.

Adaptive neuro-fuzzy inference system model driven by the non-negative matrix factorization-extracted muscle synergy patterns to estimate lower limb joint movements

BACKGROUND AND OBJECTIVE

For patients with movement disorders, the main clinical focus is on exercise rehabilitation to help recover lost motor function, which is achieved by relevant assisted equipment. The basis for seamless control of the assisted equipment is to achieve accurate inference of the user's movement intentions in the human-machine interface. This study proposed a novel movement intention detection technology for estimating lower limb joint continuous kinematic variables following muscle synergy patterns, to develop applications for more efficient assisted rehabilitation training.

METHODS

This study recruited 16 healthy males and 16 male patients with symptomatic patellar tendinopathy (VISA-P: 59.1 ± 8.7). The surface electromyography of 12 muscles and lower limb joint kinematic and kinetic data from healthy subjects and patients during step-off landings from 30 cm-high stair steps were collected. We subsequently solved the preprocessed data based on the established recursive model of second-order differential equation to obtain the muscle activation matrix, and then imported it into the non-negative matrix factorization model to obtain the muscle synergy matrix. Finally, the lower limb neuromuscular synergy pattern was then imported into the developed adaptive neuro-fuzzy inference system non-linear regression model to estimate the human movement intention during this movement pattern.

RESULTS

Six muscle synergies were determined to construct the muscle synergy pattern driven ANFIS model. Three fuzzy rules were determined in most estimation cases. Combining the results of the four error indicators across the estimated variables indicates that the current model has excellent estimated performance in estimating lower limb joint movement. The estimation errors between the healthy (Angle: R2=0.98±0.03; Torque: R2=0.96±0.04) and patient (Angle: R2=0.98±0.02; Torque: R2=0.96±0.03) groups are consistent.

CONCLUSION

The proposed model of this study can accurately and reliably estimate lower limb joint movements, and the effectiveness will also be radiated to the patient group. This revealed that our models also have certain advantages in the recognition of motor intentions in patients with relevant movement disorders. Future work from this study can be focused on sports rehabilitation in the clinical field by achieving more flexible and precise movement control of the lower limb assisted equipment to help the rehabilitation of patients.

Post-Stroke Brain Health Monitoring and Optimization: A Narrative Review

Significant advancements have been made in recent years in the acute treatment and secondary prevention of stroke. However, a large proportion of stroke survivors will go on to have enduring physical, cognitive, and psychological disabilities from suboptimal post-stroke brain health. Impaired brain health following stroke thus warrants increased attention from clinicians and researchers alike. In this narrative review based on an open timeframe search of the PubMed, Scopus, and Web of Science databases, we define post-stroke brain health and appraise the body of research focused on modifiable vascular, lifestyle, and psychosocial factors for optimizing post-stroke brain health. In addition, we make clinical recommendations for the monitoring and management of post-stroke brain health at major post-stroke transition points centered on four key intertwined domains: cognition, psychosocial health, physical functioning, and global vascular health. Finally, we discuss potential future work in the field of post-stroke brain health, including the use of remote monitoring and interventions, neuromodulation, multi-morbidity interventions, enriched environments, and the need to address inequities in post-stroke brain health. As post-stroke brain health is a relatively new, rapidly evolving, and broad clinical and research field, this narrative review aims to identify and summarize the evidence base to help clinicians and researchers tailor their own approach to integrating post-stroke brain health into their practices.

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.

Inertial measurement data from loose clothing worn on the lower body during everyday activities

Embedding sensors into clothing is promising as a way for people to wear multiple sensors easily, for applications such as long-term activity monitoring. To our knowledge, this is the first published dataset collected from sensors in loose clothing. 6 Inertial Measurement Units (IMUs) were configured as a 'sensor string' and attached to casual trousers such that there were three sensors on each leg near the waist, thigh, and ankle/lower-shank. Participants also wore an Actigraph accelerometer on their dominant wrist. The dataset consists of 15 participant-days worth of data collected from 5 healthy adults (age range: 28-48 years, 3 males and 2 females). Each participant wore the clothes with sensors for between 1 and 4 days for 5-8 hours per day. Each day, data were collected while participants completed a fixed circuit of activities (with a video ground truth) as well as during free day-to-day activities (with a diary). This dataset can be used to analyse human movements, transitional movements, and postural changes based on a range of features.

Clinical Research Progress of the Post-Stroke Upper Limb Motor Function Improvement via Transcutaneous Auricular Vagus Nerve Stimulation

Stroke is a disease with high morbidity and disability, and motor impairment is a common sequela of stroke. Transcutaneous auricular vagus nerve stimulation (taVNS) is a type of non-invasive stimulation, which can effectively improve post-stroke motor dysfunction. This review discusses stimulation parameters, intervention timing, and the development of innovative devices for taVNS. We further summarize the application of taVNS in improving post-stroke upper limb motor function to further promote the clinical research and application of taVNS in the rehabilitation of post-stroke upper limb motor dysfunction.

A bidirectional fabric-based soft robotic glove for hand function assistance in patients with chronic stroke

BACKGROUND

Chronic stroke patients usually experience reduced hand functions, impeding their ability to perform activities of daily living (ADLs) independently. Additionally, improvements in hand functions by physical therapy beyond six months after the initial onset of stroke are much slower than in the earlier months. As such, chronic stroke patients could benefit from an assistive device to enhance their hand functions, allowing them to perform ADLs independently daily. In recent years, soft robotics has provided a novel approach to assistive devices for motor impaired individuals, offering more compliant and lightweight alternatives to traditional robotic devices. The scope of this study is to demonstrate the viability of a fabric-based soft robotic (SR) glove with bidirectional actuators in assisting chronic stroke study participants with hand impairments in performing ADLs.

METHODS

Force and torque measurement tests were conducted to characterize the SR Glove, and hand functional tasks were given to eight chronic stroke patients to assess the efficacy of the SR Glove as an assistive device. The tasks involved object manipulation tasks that simulate ADLs, and the series of tasks was done by the participants once without assistance for baseline data, and once while using the SR Glove. A usability questionnaire was also given to each participant after the tasks were done to gain insight into how the SR Glove impacts their confidence and reliance on support while performing ADLs.

RESULTS

The SR Glove improved the participants' manipulation of objects in ADL tasks. The difference in mean scores between the unassisted and assisted conditions was significant across all participants. Additionally, the usability questionnaire showed the participants felt more confident and less reliant on support while using the SR Glove to perform ADLs than without the SR Glove.

CONCLUSIONS

The results from this study demonstrated that the SR Glove is a viable option to assist hand function in chronic stroke patients who suffer from hand motor impairments.

Quantitative Comparison of Hand Kinematics Measured with a Markerless Commercial Head-Mounted Display and a Marker-Based Motion Capture System in Stroke Survivors

Upper-limb paresis is common after stroke. An important tool to assess motor recovery is to use marker-based motion capture systems to measure the kinematic characteristics of patients' movements in ecological scenarios. These systems are, however, very expensive and not readily available for many rehabilitation units. Here, we explored whether the markerless hand motion capabilities of the cost-effective Oculus Quest head-mounted display could be used to provide clinically meaningful measures. A total of 14 stroke patients executed ecologically relevant upper-limb tasks in an immersive virtual environment. During task execution, we recorded their hand movements simultaneously by means of the Oculus Quest's and a marker-based motion capture system. Our results showed that the markerless estimates of the hand position and peak velocity provided by the Oculus Quest were in very close agreement with those provided by a marker-based commercial system with their regression line having a slope close to 1 (maximum distance: mean slope = 0.94 ± 0.1; peak velocity: mean slope = 1.06 ± 0.12). Furthermore, the Oculus Quest had virtually the same sensitivity as that of a commercial system in distinguishing healthy from pathological kinematic measures. The Oculus Quest was as accurate as a commercial marker-based system in measuring clinically meaningful upper-limb kinematic parameters in stroke patients.

Objective wearable measures correlate with self-reported chronic pain levels in people with spinal cord stimulation systems

Spinal Cord Stimulation (SCS) is a well-established therapy for treating chronic pain. However, perceived treatment response to SCS therapy may vary among people with chronic pain due to diverse needs and backgrounds. Patient Reported Outcomes (PROs) from standard survey questions do not provide the full picture of what has happened to a patient since their last visit, and digital PROs require patients to visit an app or otherwise regularly engage with software. This study aims to assess the feasibility of using digital biomarkers collected from wearables during SCS treatment to predict pain and PRO outcomes. Twenty participants with chronic pain were recruited and implanted with SCS. During the six months of the study, activity and physiological metrics were collected and data from 15 participants was used to develop a machine learning pipeline to objectively predict pain levels and categories of PRO measures. The model reached an accuracy of 0.768 ± 0.012 in predicting the pain intensity of mild, moderate, and severe. Feature importance analysis showed that digital biomarkers from the smartwatch such as heart rate, heart rate variability, step count, and stand time can contribute to modeling different aspects of pain. The results of the study suggest that wearable biomarkers can be used to predict therapy outcomes in people with chronic pain, enabling continuous, real-time monitoring of patients during the use of implanted therapies.

A Narrative Review on Multi-Domain Instrumental Approaches to Evaluate Neuromotor Function in Rehabilitation

In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients' state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients' state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.

The NuroSleeve, a user-centered 3D printed hybrid orthosis for individuals with upper extremity impairment

BACKGROUND

Active upper extremity (UE) assistive devices have the potential to restore independent functional movement in individuals with UE impairment due to neuromuscular diseases or injury-induced chronic weakness. Academically fabricated UE assistive devices are not usually optimized for activities of daily living (ADLs), whereas commercially available alternatives tend to lack flexibility in control and activation methods. Both options are typically difficult to don and doff and may be uncomfortable for extensive daily use due to their lack of personalization. To overcome these limitations, we have designed, developed, and clinically evaluated the NuroSleeve, an innovative user-centered UE hybrid orthosis.

METHODS

This study introduces the design, implementation, and clinical evaluation of the NuroSleeve, a user-centered hybrid device that incorporates a lightweight, easy to don and doff 3D-printed motorized UE orthosis and a functional electrical stimulation (FES) component. Our primary goals are to develop a customized hybrid device that individuals with UE neuromuscular impairment can use to perform ADLs and to evaluate the benefits of incorporating the device into occupational therapy sessions. The trial is designed as a prospective, open-label, single-cohort feasibility study of eight-week sessions combined with at-home use of the device and implements an iterative device design process where feedback from participants and therapists informs design improvement cycles.

RESULTS

All participants learned how to independently don, doff, and use the NuroSleeve in ADLs, both in clinical therapy and in their home environments. All participants showed improvements in their Canadian Occupational Performance Measure (COPM), which was the primary clinical trial outcome measure. Furthermore, participants and therapists provided valuable feedback to guide further development.

CONCLUSIONS

Our results from non-clinical testing and clinical evaluation demonstrate that the NuroSleeve has met feasibility and safety goals and effectively improved independent voluntary function during ADLs. The study's encouraging preliminary findings indicate that the NuroSleeve has met its technical and clinical objectives while improving upon the limitations of the existing UE orthoses owing to its personalized and flexible approach to hardware and firmware design.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT04798378, https://clinicaltrials.gov/ct2/show/NCT04798378 , date of registration: March 15, 2021.

Bridging the Gap between Basic Research and Clinical Practice: The Growing Role of Translational Neurorehabilitation

Translational neuroscience is intended as a holistic approach in the field of brain disorders, starting from the basic research of cerebral morphology and with the function of implementing it into clinical practice. This concept can be applied to the rehabilitation field to promote promising results that positively influence the patient's quality of life. The last decades have seen great scientific and technological improvements in the field of neurorehabilitation. In this paper, we discuss the main issues related to translational neurorehabilitation, from basic research to current clinical practice, and we also suggest possible future scenarios.

Empirical Myoelectric Feature Extraction and Pattern Recognition in Hemiplegic Distal Movement Decoding

In myoelectrical pattern recognition (PR), the feature extraction methods for stroke-oriented applications are challenging and remain discordant due to a lack of hemiplegic data and limited knowledge of skeletomuscular function. Additionally, technical and clinical barriers create the need for robust, subject-independent feature generation while using supervised learning (SL). To the best of our knowledge, we are the first study to investigate the brute-force analysis of individual and combinational feature vectors for acute stroke gesture recognition using surface electromyography (EMG) of 19 patients. Moreover, post-brute-force singular vectors were concatenated via a Fibonacci-like spiral net ranking as a novel, broadly applicable concept for feature selection. This semi-brute-force navigated amalgamation in linkage (SNAiL) of EMG features revealed an explicit classification rate performance advantage of 10-17% compared to canonical feature sets, which can drastically extend PR capabilities in biosignal processing.

Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain

BACKGROUND

Neurostimulation is an effective therapy for treating and management of refractory chronic pain. However, the complex nature of pain and infrequent in-clinic visits, determining subject's long-term response to the therapy remains difficult. Frequent measurement of pain in this population can help with early diagnosis, disease progression monitoring, and evaluating long-term therapeutic efficacy. This paper compares the utilization of the common subjective patient-reported outcomes with objective measures captured through a wearable device for predicting the response to neurostimulation therapy.

METHOD

Data is from the ongoing international prospective post-market REALITY clinical study, which collects long-term patient-reported outcomes from 557 subjects implanted by Spinal Cord Stimulator (SCS) or Dorsal Root Ganglia (DRG) neurostimulators. The REALITY sub-study was designed for collecting additional wearables data on a subset of 20 participants implanted with SCS devices for up to six months post implantation. We first implemented a combination of dimensionality reduction algorithms and correlation analyses to explore the mathematical relationships between objective wearable data and subjective patient-reported outcomes. We then developed machine learning models to predict therapy outcome based on the subject's response to the numerical rating scale (NRS) or patient global impression of change (PGIC).

RESULTS

Principal component analysis showed that psychological aspects of pain were associated with heart rate variability, while movement-related measures were strongly associated with patient-reported outcomes related to physical function and social role participation. Our machine learning models using objective wearable data predicted PGIC and NRS outcomes with high accuracy without subjective data. The prediction accuracy was higher for PGIC compared with the NRS using subjective-only measures primarily driven by the patient satisfaction feature. Similarly, the PGIC questions reflect an overall change since the study onset and could be a better predictor of long-term neurostimulation therapy outcome.

CONCLUSIONS

The significance of this study is to introduce a novel use of wearable data collected from a subset of patients to capture multi-dimensional aspects of pain and compare the prediction power with the subjective data from a larger data set. The discovery of pain digital biomarkers could result in a better understanding of the patient's response to therapy and their general well-being.

Perspectives of users for a future interactive wearable system for upper extremity rehabilitation following stroke: a qualitative study

BACKGROUND

Wearable sensor technology can facilitate diagnostics and monitoring of people with upper extremity (UE) paresis after stroke. The purpose of this study is to investigate the perspectives of clinicians, people living with stroke, and their caregivers on an interactive wearable system that detects UE movements and provides feedback.

METHODS

This qualitative study used semi-structured interviews relating to the perspectives of a future interactive wearable system including a wearable sensor to capture UE movement and a user interface to provide feedback as the means of data collection. Ten rehabilitation therapists, 9 people with stroke, and 2 caregivers participated in this study.

RESULTS

Four themes were identified (1) "Everyone is different" highlighted the need for addressing individual user's rehabilitation goal and personal preference; (2) "The wearable system should identify UE and trunk movements" emphasized that in addition to arm, hand, and finger movements, detecting compensatory trunk movements during UE movements is also of interest; (3) "Both quality and amount of movements are necessary to measure" described the parameters related to how well and how much the user is using their affected UE that participants envisioned the system to monitor; (4) "Functional activities should be practiced by the users" outlined UE movements and activities that are of priority in designing the system.

CONCLUSIONS

Narratives from clinicians, people with stroke, and their caregivers offer insight into the design of interactive wearable systems. Future studies examining the experience and acceptability of existing wearable systems from end-users are warranted to guide the adoption of this technology.

Sensor-Based Rehabilitation in Neurological Diseases: A Bibliometric Analysis of Research Trends

BACKGROUND

As the field of sensor-based rehabilitation continues to expand, it is important to gain a comprehensive understanding of its current research landscape. This study aimed to conduct a bibliometric analysis to identify the most influential authors, institutions, journals, and research areas in this field.

METHODS

A search of the Web of Science Core Collection was performed using keywords related to sensor-based rehabilitation in neurological diseases. The search results were analyzed with CiteSpace software using bibliometric techniques, including co-authorship analysis, citation analysis, and keyword co-occurrence analysis.

RESULTS

Between 2002 and 2022, 1103 papers were published on the topic, with slow growth from 2002 to 2017, followed by a rapid increase from 2018 to 2022. The United States was the most active country, while the Swiss Federal Institute of Technology had the highest number of publications among institutions. Sensors published the most papers. The top keywords included rehabilitation, stroke, and recovery. The clusters of keywords comprised machine learning, specific neurological conditions, and sensor-based rehabilitation technologies.

CONCLUSIONS

This study provides a comprehensive overview of the current state of sensor-based rehabilitation research in neurological diseases, highlighting the most influential authors, journals, and research themes. The findings can help researchers and practitioners to identify emerging trends and opportunities for collaboration and can inform the development of future research directions in this field.

Predicting poststroke dyskinesia with resting-state functional connectivity in the motor network

SIGNIFICANCE

Motor function evaluation is essential for poststroke dyskinesia rehabilitation. Neuroimaging techniques combined with machine learning help decode a patient's functional status. However, more research is needed to investigate how individual brain function information predicts the dyskinesia degree of stroke patients.

AIM

We investigated stroke patients' motor network reorganization and proposed a machine learning-based method to predict the patients' motor dysfunction.

APPROACH

Near-infrared spectroscopy (NIRS) was used to measure hemodynamic signals of the motor cortex in the resting state (RS) from 11 healthy subjects and 31 stroke patients, 15 with mild dyskinesia (Mild), and 16 with moderate-to-severe dyskinesia (MtS). The graph theory was used to analyze the motor network characteristics.

RESULTS

The small-world properties of the motor network were significantly different between groups: (1) clustering coefficient, local efficiency, and transitivity: MtS > Mild > Healthy and (2) global efficiency: MtS < Mild < Healthy. These four properties linearly correlated with patients' Fugl-Meyer Assessment scores. Using the small-world properties as features, we constructed support vector machine (SVM) models that classified the three groups of subjects with an accuracy of 85.7%.

CONCLUSIONS

Our results show that NIRS, RS functional connectivity, and SVM together constitute an effective method for assessing the poststroke dyskinesia degree at the individual level.

Internet of Things (IoT) Enables Robot-Assisted Therapy as a Home Program for Training Upper Limb Functions in Chronic Stroke: A Randomized Control Crossover Study

OBJECTIVE

To compare the effects of using an Internet of things (IoT)-assisted tenodesis-induced-grip exoskeleton robot (TIGER) and task-specific motor training (TSMT) as home programs for the upper-limb (UL) functions of patients with chronic stroke to overturn conventional treatment modes for stroke rehabilitation.

DESIGN

A randomized 2-period crossover study.

SETTING

A university hospital.

PARTICIPANTS

Eighteen chronic stroke patients were recruited and randomized to receive either the IoT-assisted TIGER first or TSMT first at the beginning of the experiment (N=18).

INTERVENTION

In addition to the standard hospital-based therapy, participants were allocated to receive a 30-minute home-based, self-administered program of either IoT-assisted TIGER first or TSMT first twice daily for 4 weeks, with the order of both treatments reversed after a 12-week washout period. The exercise mode of the TIGER training included continuous passive motion and the functional mode of gripping pegs. The TSMT involved various movement components of the wrist and hand.

MAIN OUTCOME MEASURES

The outcome measures included the box and block test (BBT), the Fugl-Meyer assessment for upper extremity (FMA-UE), the motor activity log, the Semmes-Weinstein Monofilament test, the range of motion (ROM) of the wrist joint, and the modified Ashworth scale.

RESULTS

Significant treatment-by-time interaction effects emerged in the results for the BBT (F(1.31)=5.212 and P=.022), the FMA-UE (F(1.31)=6.807 and P=.042), and the ROM of the wrist extension (F(1.31)=8.618 and P=.009). The participants who trained at home with the IoT-assisted TIGER showed more improvement of their UL functions.

CONCLUSIONS

The IoT-assisted TIGER training has the potential for restoring the UL functions of stroke patients.

Usability of a wearable device for home-based upper limb telerehabilitation in persons with stroke: A mixed-methods study

Background

The use of wearable technology offers a promising home-based self-directed option for upper limb training. Although product usability is a crucial aspect of users' acceptance of a wearable device, usability studies in wearable devices are rare, with most studies focusing primarily on clinical validity.

Objective

This study aimed to explore the usability of a wristwatch device called "Smart reminder" for home-based upper limb telerehabilitation for persons with stroke.

Methods

Eleven stroke participants used the proposed wristwatch for at least two weeks and underwent a home-based telerehabilitation program. A mixed-methods design was used to explore the usability of the wristwatch. Quantitative data were collected through the System Usability Scale (SUS) questionnaire, and the participants' rate of therapy compliance (gathered from the device) was reported descriptively. In addition, qualitative data were collected through semi-structured interviews with the participants and were analyzed using thematic analysis.

Results

The results demonstrated that the usability of the proposed wristwatch and telerehabilitation system was rated highly by the participants, with a high SUS mean score of 84.3 (12.3) and high therapy compliance rate (mean  =  91%). Qualitatively, all participants reported positive experiences with the wristwatch and indicated keenness to use it again. Participants reported physical improvements and felt motivated to exercise after using the wristwatch. They found the device easy and convenient and appreciated the remote monitoring function. Meanwhile, they highlighted critical considerations for the design of the device and program, including technical support, a wearable design of the device, graded exercise content according to ability, and flexibility in exercise schedules. Finally, they suggested that an interim review with the therapist on their progress might help them continue using the wristwatch.

Conclusions

This study's results supported the proposed wearable device's usability and showed strong acceptance by the participants for using it as a home-based upper limb telerehabilitation intervention.

Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview

A quantitative evaluation of kinetic parameters, the joint's range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the device's positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a user's vital signs directly from the body in an accurate and non-invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coach's subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during post-operative rehabilitation and athletes' training, and to present evidence that supports the efficacy of this technology for healthcare applications. First, a classification of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the user's health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for defining the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties.

A scoping review on recent trends in wearable sensors to analyze gait in people with stroke: From sensor placement to validation against gold-standard equipment

The purpose of the review is to evaluate wearable sensor placement, their impact and validation of wearable sensors on analyzing gait, primarily the postural instability in people with stroke. Databases, namely PubMed, Cochrane, SpringerLink, and IEEE Xplore were searched to identify related articles published since January 2005. The authors have selected the articles by considering patient characteristics, intervention details, and outcome measurements by following the priorly set inclusion and exclusion criteria. From a total of 1077 articles, 142 were included in this study and classified into functional fields, namely postural stability (PS) assessments, physical activity monitoring (PA), gait pattern classification (GPC), and foot drop correction (FDC). The review covers the types of wearable sensors, their placement, and their performance in terms of reliability and validity. When employing a single wearable sensor, the pelvis and foot were the most used locations for detecting gait asymmetry and kinetic parameters, respectively. Multiple Inertial Measurement Units placed at different body parts were effectively used to estimate postural stability and gait pattern. This review article has compared results of placement of sensors at different locations helping researchers and clinicians to identify the best possible placement for sensors to measure specific kinematic and kinetic parameters in persons with stroke.