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Background: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support from physical therapists or technicians. Methods: This paper describes the early findings of the NeuroExo brain–machine interface (BMI) with an upper-limb robotic exoskeleton for stroke neurorehabilitation. This early feasibility study consisted of a six-week protocol, with an initial training and BMI calibration phase at the clinic followed by 60 sessions of neuromotor therapy at the homes of the participants. Pre- and post-assessments were used to assess users’ compliance and system performance. Results: Participants achieved a compliance rate between 21% and 100%, with an average of 69%, while maintaining adequate signal quality and a positive perceived BMI performance during home usage with an average Likert scale score of four out of five. Moreover, adequate signal quality was maintained for four out of five participants throughout the protocol. These findings provide valuable insights into essential components for comprehensive rehabilitation therapy for stroke survivors. Furthermore, linear mixed-effects statistical models showed a significant reduction in trial duration (p-value < 0.02) and concomitant changes in brain patterns (p-value < 0.02). Conclusions: the analysis of these findings suggests that a low-cost, safe, simple-to-use BMI system for at-home stroke rehabilitation is feasible.
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Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach
BackgroundStroke 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. ObjectiveOur 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. MethodsIn 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. ResultsWe 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%). ConclusionsWe 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.
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- Award ID(s):
- 1953135
- PAR ID:
- 10615070
- Publisher / Repository:
- JMIR Publications
- Date Published:
- Journal Name:
- JMIR Biomedical Engineering
- Volume:
- 9
- ISSN:
- 2561-3278
- Page Range / eLocation ID:
- e56980
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Purpose of ReviewThe current review aims to address critical gaps in the field of stroke rehabilitation related to sensory impairment. Here, we examine the role and importance of sensation throughout recovery of neural injury, potential clinical and experimental approaches for improving sensory function, and mechanism-based theories that may facilitate the design of sensory-based approaches for the rehabilitation of somatosensation. Recent FindingsRecently, the field of neurorehabilitation has shifted to using more quantitative and sensitive measures to more accurately capture sensory function in stroke and other neurological populations. These approaches have laid the groundwork for understanding how sensory impairments impact overall function after stroke. However, there is less consensus on which interventions are effective for remediating sensory function, with approaches that vary from clinical re-training, robotics, and sensory stimulation interventions. SummaryCurrent evidence has found that sensory and motor systems are interdependent, but commonly have independent recovery trajectories after stroke. Therefore, it is imperative to assess somatosensory function in order to guide rehabilitation outcomes and trajectory. Overall, considerable work in the field still remains, as there is limited evidence for purported mechanisms of sensory recovery, promising early-stage work that focuses on sensory training, and a considerable evidence-practice gap related to clinical sensory rehabilitation.more » « less
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Abstract ObjectiveNeuropsychological testing is essential for both clinical and basic stroke research; however, the in-person nature of this testing is a limitation. Virtual testing overcomes the hurdles of geographic location, mobility issues and permits social distancing, yet its validity has received relatively little investigation, particularly in comparison with in-person testing. MethodWe expand on our prior findings of virtual testing feasibility by assessing virtual versus in-person administration of language and communication tasks with 48 left-hemisphere stroke patients (21 F, 27 M; mean age = 63.4 ± 12; mean years of education = 15.3 ± 3.5) in a quasi-test–retest paradigm. Each participant completed two testing sessions: one in their home and one in the research lab. Participants were assigned to one of the eight groups, with the testing condition (fully in-person, partially virtual), order of home session (first, second) and technology (iPad, Windows tablet) varied across groups. ResultsAcross six speech-language tasks that utilized varying response modalities and interfaces, we found no significant difference in performance between virtual and in-person testing. However, our results reveal key considerations for successful virtual administration of neuropsychological tests, including technology complications and disparities in internet access. ConclusionsVirtual administration of neuropsychological assessments demonstrates comparable reliability with in-person data collection involving stroke survivors, though technology issues must be taken into account.more » « less
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We are developing a system for long term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor's movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high level constraints relating to activity structure (i.e., type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high level priors to data driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data driven techniques. We use a transformer based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complimentary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce a robust segmentation and task assessment results on noisy, variable and limited data, which is characteristic of low cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e., lower extremity training for neurological accidents).more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2196/56980
