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• W2912148940 abstract "Upper Extremity (UE) rehabilitation is often needed post-stroke. The main goal of UE treatment in stroke survivors is to increase the use of the affected UE in the home and community. However, the effectiveness of UE treatments are difficult to quantify because no objective evaluation of UE use exists. In practice, a clinician rates the patient's ability to perform specific motor tasks associated with functional use in a clinic or the patient self-reports the amount or quality of arm movement for a standard set of activities. Both methods do not objectively measure the performance of the affected UE in the home or community environment, and there is growing evidence that motor performance in the laboratory is a poor proxy for the actual amount of UE use. Using a single wrist-worn sensor (i.e., accelerometry data) and machine learning, we have reported that it is possible to separate UE functional use from nonfunctional movement after stroke. Specifically, we reported that we correctly classified sensor data with an average of 94.80% in controls and 88.38% in stroke subjects in intra-subject test trials, and 91.53% for controls and 70.18% in stroke subjects in inter-subject test trials. In this paper, we employed feature selection techniques and explored different machine learning methods to improve the classification accuracy. Our enhanced methods are robust and reliable, and work in both intra-subject and inter-subject training and testing. Our result showed better accuracy in stroke patients than previously reported with the same dataset. The enhanced models reached an average of 96% accuracy in control subjects and 94% in stroke subjects for intra-subject trials, and an average of 90% accuracy in control subjects and 83% in stroke subjects for the inter-subject trials. The proposed methods provide an inexpensive and feasible way to quantify the UE functional use in home and community. This information can provide guidance for clinical practice in the rehabilitative care of adults recovering from stroke." @default.
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• W2912148940 date "2018-12-01" @default.
• W2912148940 modified "2023-10-14" @default.
• W2912148940 title "Robust Classification of Functional and Nonfunctional Arm Movement after Stroke Using a Single Wrist-Worn Sensor Device" @default.
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• W2912148940 doi "https://doi.org/10.1109/bigdata.2018.8622620" @default.
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