Elaine M Bochniewicz1, Geoff Emmer2, Adam McLeod2, Jessica Barth3, Alexander W Dromerick4, Peter Lum5. 1. The MITRE Corporation, McLean, Virginia; Department of Biomedical Engineering, Catholic University of America, Washington, District of Columbia. Electronic address: emb@mitre.org. 2. The MITRE Corporation, McLean, Virginia. 3. Medstar National Rehabilitation Network, Washington, District of Columbia. 4. Medstar National Rehabilitation Network, Washington, District of Columbia; Washington DC Veterans Affairs Medical Center, Washington, District of Columbia; Center for Brain Plasticity and Recovery, Georgetown University, Washington, District of Columbia; Department of Rehabilitation Medicine, Georgetown University, Washington, District of Columbia; Department of Neurology, Georgetown University, Washington, District of Columbia. 5. Department of Biomedical Engineering, Catholic University of America, Washington, District of Columbia; Medstar National Rehabilitation Network, Washington, District of Columbia; Washington DC Veterans Affairs Medical Center, Washington, District of Columbia; Center for Brain Plasticity and Recovery, Georgetown University, Washington, District of Columbia.
Abstract
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. 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. 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.
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. 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. 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.
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Authors: Peter S Lum; Liqi Shu; Elaine M Bochniewicz; Tan Tran; Lin-Ching Chang; Jessica Barth; Alexander W Dromerick Journal: Neurorehabil Neural Repair Date: 2020-11-05 Impact factor: 3.919