Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method.

MedStar author(s):
Citation: Neurorehabilitation & Neural Repair. 34(12):1078-1087, 2020 12.PMID: 33150830Institution: MedStar National Rehabilitation NetworkForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: *Accelerometry/st [Standards] | *Machine Learning | *Stroke/di [Diagnosis] | *Stroke/pp [Physiopathology] | *Upper Extremity/pp [Physiopathology] | Accelerometry/mt [Methods] | Adult | Aged | Female | Humans | Male | Middle Aged | Stroke RehabilitationYear: 2020Local holdings: Available online from MWHC library: 2006 - 2009, Available in print through MWHC library: 1999 - March 2006ISSN:
  • 1545-9683
Name of journal: Neurorehabilitation and neural repairAbstract: BACKGROUND: Wrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results.CONCLUSIONS: In our sample, the counts ratio did not accurately reflect functional activity. Machine learning algorithms were more accurate, and future work should focus on the development of a clinical tool.METHODS: Healthy controls and individuals with stroke performed unstructured tasks in a simulated community environment (Test duration = 26 +/- 8 minutes) while accelerometry and video were synchronously recorded. Human annotators scored each frame of the video as being functional or nonfunctional activity, providing ground truth. Several machine learning algorithms were developed to separate functional from nonfunctional activity in the accelerometer data. We also calculated the counts ratio, which uses a thresholding scheme to calculate the duration of activity in the paretic limb normalized by the less-affected limb.OBJECTIVE: Compare machine learning algorithms with standard methods (counts ratio) to improve accuracy in detecting functional activity.RESULTS: The counts ratio was not significantly correlated with ground truth and had large errors (r = 0.48; P = .16; average error = 52.7%) because of high levels of nonfunctional movement in the paretic limb. Counts did not increase with increased functional movement. The best-performing intrasubject machine learning algorithm had an accuracy of 92.6% in the paretic limb of stroke patients, and the correlation with ground truth was r = 0.99 (P < .001; average error = 3.9%). The best intersubject model had an accuracy of 74.2% and a correlation of r =0.81 (P = .005; average error = 5.2%) with ground truth.All authors: Barth J, Bochniewicz EM, Chang LC, Dromerick AW, Lum PS, Shu L, Tran TOriginally published: Neurorehabilitation & Neural Repair. :1545968320962483, 2020 Nov 05Fiscal year: FY2021Digital Object Identifier: ORCID: Date added to catalog: 2020-12-29
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Item type Current library Collection Call number Status Date due Barcode
Journal Article MedStar Authors Catalog Article 33150830 Available 33150830

Available online from MWHC library: 2006 - 2009, Available in print through MWHC library: 1999 - March 2006

BACKGROUND: Wrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results.

CONCLUSIONS: In our sample, the counts ratio did not accurately reflect functional activity. Machine learning algorithms were more accurate, and future work should focus on the development of a clinical tool.

METHODS: Healthy controls and individuals with stroke performed unstructured tasks in a simulated community environment (Test duration = 26 +/- 8 minutes) while accelerometry and video were synchronously recorded. Human annotators scored each frame of the video as being functional or nonfunctional activity, providing ground truth. Several machine learning algorithms were developed to separate functional from nonfunctional activity in the accelerometer data. We also calculated the counts ratio, which uses a thresholding scheme to calculate the duration of activity in the paretic limb normalized by the less-affected limb.

OBJECTIVE: Compare machine learning algorithms with standard methods (counts ratio) to improve accuracy in detecting functional activity.

RESULTS: The counts ratio was not significantly correlated with ground truth and had large errors (r = 0.48; P = .16; average error = 52.7%) because of high levels of nonfunctional movement in the paretic limb. Counts did not increase with increased functional movement. The best-performing intrasubject machine learning algorithm had an accuracy of 92.6% in the paretic limb of stroke patients, and the correlation with ground truth was r = 0.99 (P < .001; average error = 3.9%). The best intersubject model had an accuracy of 74.2% and a correlation of r =0.81 (P = .005; average error = 5.2%) with ground truth.

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