Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning.
Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning.
- 2017
BACKGROUND AND PURPOSE: Trials of restorative therapies after stroke and clinical rehabilitation require relevant and objective efficacy end points; real-world upper extremity (UE) functional use is an attractive candidate. We present a novel, inexpensive, and feasible method for separating UE functional use from nonfunctional movement after stroke using a single wrist-worn accelerometer. CONCLUSIONS: Our method shows promise for inexpensive and objective quantification of functional UE use in hemiparesis, and for assessing the impact of UE treatments. Training a classifier on raw sensor data is feasible, and determination of whether patients functionally use their UE can thus be done remotely. For the restorative treatment trial setting, an intrasubject test/train approach would be especially accurate. This method presents a potentially precise, cost-effective, and objective measurement of UE use outside the clinical or laboratory environment. Copyright (c) 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved. METHODS: Ten controls and 10 individuals with stroke performed a series of minimally structured activities while simultaneously being videotaped and wearing a sensor on each wrist that captured the linear acceleration and angular velocity of their UEs. Video data provided ground truth to annotate sensor data as functional or nonfunctional limb use. Using the annotated sensor data, we trained a machine learning tool, a Random Forest model. We then assessed the accuracy of that classification. RESULTS: In intrasubject test trials, our method correctly classified sensor data with an average of 94.80% in controls and 88.38% in stroke subjects. In leave-one-out intersubject testing and training, correct classification averaged 91.53% for controls and 70.18% in stroke subjects.
English
1052-3057
*Actigraphy/is [Instrumentation]
*Activities of Daily Living
*Fitness Trackers
*Machine Learning
*Movement
*Signal Processing, Computer-Assisted
*Stroke/di [Diagnosis]
*Upper Extremity/ir [Innervation]
Acceleration
Adult
Aged
Biomechanical Phenomena
Case-Control Studies
Equipment Design
Feasibility Studies
Female
Health Status
Humans
Male
Middle Aged
Predictive Value of Tests
Reproducibility of Results
Stroke/pp [Physiopathology]
Time Factors
Video Recording
MedStar National Rehabilitation Network
Journal Article
BACKGROUND AND PURPOSE: Trials of restorative therapies after stroke and clinical rehabilitation require relevant and objective efficacy end points; real-world upper extremity (UE) functional use is an attractive candidate. We present a novel, inexpensive, and feasible method for separating UE functional use from nonfunctional movement after stroke using a single wrist-worn accelerometer. CONCLUSIONS: Our method shows promise for inexpensive and objective quantification of functional UE use in hemiparesis, and for assessing the impact of UE treatments. Training a classifier on raw sensor data is feasible, and determination of whether patients functionally use their UE can thus be done remotely. For the restorative treatment trial setting, an intrasubject test/train approach would be especially accurate. This method presents a potentially precise, cost-effective, and objective measurement of UE use outside the clinical or laboratory environment. Copyright (c) 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved. METHODS: Ten controls and 10 individuals with stroke performed a series of minimally structured activities while simultaneously being videotaped and wearing a sensor on each wrist that captured the linear acceleration and angular velocity of their UEs. Video data provided ground truth to annotate sensor data as functional or nonfunctional limb use. Using the annotated sensor data, we trained a machine learning tool, a Random Forest model. We then assessed the accuracy of that classification. RESULTS: In intrasubject test trials, our method correctly classified sensor data with an average of 94.80% in controls and 88.38% in stroke subjects. In leave-one-out intersubject testing and training, correct classification averaged 91.53% for controls and 70.18% in stroke subjects.
English
1052-3057
*Actigraphy/is [Instrumentation]
*Activities of Daily Living
*Fitness Trackers
*Machine Learning
*Movement
*Signal Processing, Computer-Assisted
*Stroke/di [Diagnosis]
*Upper Extremity/ir [Innervation]
Acceleration
Adult
Aged
Biomechanical Phenomena
Case-Control Studies
Equipment Design
Feasibility Studies
Female
Health Status
Humans
Male
Middle Aged
Predictive Value of Tests
Reproducibility of Results
Stroke/pp [Physiopathology]
Time Factors
Video Recording
MedStar National Rehabilitation Network
Journal Article