Application of MM-Wave Radar and Machine Learning for Post-Stroke Upper Extremity Motor Assessment

Abstract Background: Prior studies suggest that participation in rehabilitation exercises improves motor function post-stroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. Objective: In this exploratory study, we assessed the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigated which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. Methods: MC10 BioStampRC® sensors were used to measure accelerometry and gyroscopy data from arms of healthy controls (n=13) and patients with upper extremity (UE) weakness due to recent stroke (n=13) while the subjects performed three pre-selected UE exercises. Sensor data was then labeled by exercise type, and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine-learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets. Results: We achieved a repetition counting accuracy of 95.6 ± 2.4 % overall, and 95.0 ± 2.3 % in patients with UE weakness due to stroke. Accuracy was decreased when using fewer sensors or using accelerometry data alone. Conclusions: Our exploratory study suggests that body-worn sensor systems are technically feasible, well-tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in post-stroke patients during clinical rehabilitation or clinical trials.

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Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

10.1101/2020.04.17.20043869 ◽

2020 ◽

Author(s):

Noah Balestra ◽

Gaurav Sharma ◽

Linda M. Riek ◽

Ania Busza

Keyword(s):

Machine Learning ◽

Upper Extremity ◽

Exploratory Study ◽

Sensor Data ◽

Data Set ◽

Machine Learning Classification ◽

Post Stroke ◽

Exercise Type ◽

Exercise Dose ◽

Rehabilitation Exercises

AbstractStroke is a major cause of adult-onset disability worldwide, and approximately 40% of stroke survivors have residual impairment in upper-extremity function. Prior studies suggest that increased participation in rehabilitation exercises improves motor function post-stroke,1 however further clarification of optimal exercise dose and timing has been limited by the technical challenge of quantifying exercise activities over multiple days. In this exploratory study, we assessed the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting, and investigated which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. MC10 BioStampRC® sensors were used to measure accelerometry and gyroscopy from arms of healthy controls (n=11) and patients with arm weakness due to recent stroke (n=13) while the subjects performed three pre-selected upper extremity exercises. Sensor data was then labeled by exercise type, and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine-learning algorithm and a peak-finding algorithm was used to count exercise repetitions in non-labeled data sets. We achieved a repetition counting accuracy of 95.6 ± 2.4 % overall, and 95.0 ± 2.3 % in patients with arm weakness due to stroke. Accuracy was decreased when using fewer sensors or using accelerometry alone. Our exploratory study suggests that body-worn sensor systems are technically feasible, well-tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in post-stroke patients during clinical rehabilitation or clinical trials.

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Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

Digital Biomarkers ◽

10.1159/000516619 ◽

2021 ◽

pp. 158-166

Author(s):

Noah Balestra ◽

Gaurav Sharma ◽

Linda M. Riek ◽

Ania Busza

Keyword(s):

Machine Learning ◽

Upper Extremity ◽

Sensor Data ◽

Inpatient Setting ◽

Accelerometer Data ◽

Data Set ◽

Machine Learning Classification ◽

Exercise Type ◽

Exercise Dose ◽

Rehabilitation Exercises

Background: Prior studies suggest that participation in rehabilitation exercises improves motor function poststroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. Objectives: The objectives of this study were to assess the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigate which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. Methods: MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (n = 13) and individuals with upper extremity weakness due to recent stroke (n = 13) while the subjects performed 3 preselected arm exercises. Sensor data were then labeled by exercise type and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets. Results: We achieved a repetition counting accuracy of 95.6% overall, and 95.0% in patients with upper extremity weakness due to stroke when using both accelerometer and gyroscope data. Accuracy was decreased when using fewer sensors or using accelerometer data alone. Conclusions: Our exploratory study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in poststroke patients during clinical rehabilitation or clinical trials.

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Efficacy of Constraint-Induced Movement Therapy for Post-Stroke Upper Extremity Hemiparesis Patients Attending a Day-Care Center

Rigakuryoho Kagaku ◽

10.1589/rika.24.929 ◽

2009 ◽

Vol 24 (6) ◽

pp. 929-933

Author(s):

Taichi KURAYAMA ◽

Anna WATANABE ◽

Minami TAKAMOTO ◽

Nami SHIGETA ◽

Yuki HASEGAWA ◽

...

Keyword(s):

Upper Extremity ◽

Day Care ◽

Care Center ◽

Movement Therapy ◽

Day Care Center ◽

Post Stroke ◽

Constraint Induced Movement Therapy

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The Comparison and Interpretation of Machine-Learning Models in Post-Stroke Functional Outcome Prediction

Diagnostics ◽

10.3390/diagnostics11101784 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1784

Author(s):

Shih-Chieh Chang ◽

Chan-Lin Chu ◽

Chih-Kuang Chen ◽

Hsiang-Ning Chang ◽

Alice M. K. Wong ◽

...

Keyword(s):

Machine Learning ◽

Area Under The Curve ◽

Superior Performance ◽

Support Vector ◽

Balance Test ◽

Post Stroke ◽

Feature Importance ◽

Value Range ◽

Importance Analysis ◽

Partial Dependence

Prediction of post-stroke functional outcomes is crucial for allocating medical resources. In this study, a total of 577 patients were enrolled in the Post-Acute Care-Cerebrovascular Disease (PAC-CVD) program, and 77 predictors were collected at admission. The outcome was whether a patient could achieve a Barthel Index (BI) score of >60 upon discharge. Eight machine-learning (ML) methods were applied, and their results were integrated by stacking method. The area under the curve (AUC) of the eight ML models ranged from 0.83 to 0.887, with random forest, stacking, logistic regression, and support vector machine demonstrating superior performance. The feature importance analysis indicated that the initial Berg Balance Test (BBS-I), initial BI (BI-I), and initial Concise Chinese Aphasia Test (CCAT-I) were the top three predictors of BI scores at discharge. The partial dependence plot (PDP) and individual conditional expectation (ICE) plot indicated that the predictors’ ability to predict outcomes was the most pronounced within a specific value range (e.g., BBS-I < 40 and BI-I < 60). BI at discharge could be predicted by information collected at admission with the aid of various ML models, and the PDP and ICE plots indicated that the predictors could predict outcomes at a certain value range.

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Abstract 13: Corticospinal Tract Injury Estimated From Acute Stroke Imaging Predicts Upper Extremity Motor Recovery After Stroke

Stroke ◽

10.1161/str.51.suppl_1.13 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

David J Lin ◽

Alison M Cloutier ◽

Kimberly S Erler ◽

Jessica M Cassidy ◽

Samuel B Snider ◽

...

Keyword(s):

Acute Stroke ◽

Upper Extremity ◽

Corticospinal Tract ◽

Standard Of Care ◽

Major Effect ◽

Motor Recovery ◽

Post Stroke ◽

Stroke Imaging ◽

Recovery Potential ◽

Structural Injury

Introduction: Injury to the corticospinal tract (CST) has been shown to have a major effect on upper extremity motor recovery after stroke. This study aimed to examine how well CST injury, measured from neuroimaging acquired during the acute stroke workup, predicts upper extremity motor recovery. Methods: Patients (N = 48) with upper extremity weakness after ischemic stroke were assessed using the upper extremity Fugl-Meyer (FM) during the acute stroke hospitalization and again at 3-month follow-up. CST injury was quantified and compared, using four different methods, from images obtained as part of the stroke standard-of-care workup. Logistic and linear regression were performed using CST injury to predict delta FM. Injury to primary motor and premotor cortices were included as potential modifiers of the effect of CST injury on recovery. Results: 48 patients were enrolled 4.2 ± 2.7 days post-stroke and completed this study. CST injury distinguished patients who reached their recovery potential (as predicted from initial impairment) from those who did not, with AUC values ranging from 0.75 to 0.8. In addition, CST injury explained ~20% of the variance in the magnitude of upper extremity recovery, even after controlling for the severity of initial impairment. Results were consistent when comparing four different methods of measuring CST injury. Extent of injury to primary motor and premotor cortices did not significantly influence the predictive value that CST injury had for recovery. Conclusions: Structural injury to the CST, as estimated from standard-of-care imaging available during the acute stroke hospitalization, is a robust way to distinguish patients who achieve their predicted recovery potential and explains a significant amount of the variance in post-stroke upper extremity motor recovery.

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Robotic Table and Serious Games for Integrative Rehabilitation Early Post-Stroke: Two Case Reports (Preprint)

10.2196/preprints.26990 ◽

2021 ◽

Author(s):

Grigore Burdea ◽

Nam H. Kim ◽

Kevin Polistico ◽

Ashwin Kadaru ◽

Namrata Grampurohit ◽

...

Keyword(s):

Upper Extremity ◽

Serious Games ◽

Case Reports ◽

Upper Extremities ◽

Pinch Strength ◽

Robotic Rehabilitation ◽

Standardized Measures ◽

Post Stroke ◽

Active Flexion ◽

Rehabilitation System

BACKGROUND BrightArm Compact is a new rehabilitation system for upper extremities. It provides bimanual training with gradated gravity loading and mediates interactions with serious games. OBJECTIVE To design and test a robotic rehabilitation table-based virtual rehabilitation system for training upper extremities early post-stroke. METHODS A new robotic rehabilitation table, controllers and adaptive games were developed. Participants underwent 12 experimental sessions in addition to the standard of care. Standardized measures of upper extremity motor impairment and function, depression severity, and cognitive function were administered pre- and post-intervention. Non-standardized measures included game variables and subjective evaluations. RESULTS Two case study participants attained high total arm repetitions per session (504 and 957, respectively), and achieved high grasp and finger extension counts. Training intensity contributed to marked improvements in affected arm shoulder strength (225% and 100%, respectively), grasp strength (27% and 16% increase), 3-finger pinch strength (31% and 15% increase). Shoulder active flexion range increased 17% and 18%, respectively, and elbow active supination was larger by 75% and 58%, respectively. Improvements in motor function were at/above Minimal Clinically Important Difference for Fugl-Meyer Assessment (11 and 10 points), Chedoke Inventory (11 and 14 points) and Upper Extremity Functional Index (19 and 23 points). Cognitive/emotive outcomes were mixed. CONCLUSIONS The design of the robotic rehabilitation table was successfully tested on two participants early post-stroke. Results are encouraging. CLINICALTRIAL ClinicalTrials.gov NCT04252170

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