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Article: Viewpoint-invariant exercise repetition counting

TitleViewpoint-invariant exercise repetition counting
Authors
KeywordsCamera
Exercise
Repetition counting
Issue Date2024
Citation
Health Information Science and Systems, 2024, v. 12, n. 1, article no. 1 How to Cite?
AbstractCounting the repetition of human exercise and physical rehabilitation is common in rehabilitation and exercise training. The existing vision-based repetition counting methods less emphasize the concurrent motions in the same video, and counting skeleton in different view angles. This work analyzed the spectrogram of the pose estimation cosine similarity to count the repetition. Besides the public datasets. This work also collected exercise videos from 11 adults to verify that the proposed method can handle concurrent motion and different view angles. The presented method was validated on the University of Idaho Physical Rehabilitation Movements Data Set (UI-PRMD) and MM-fit dataset. The overall mean absolute error (MAE) for MM-fit was 0.06 with off-by-one Accuracy (OBOA) of 0.94. As for the UI-PRMD dataset, MAE was 0.06 with OBOA 0.95. We have also tested the performance in various camera locations and concurrent motions with 57 skeleton time-series videos with an overall MAE of 0.07 and OBOA of 0.91. The proposed method provides a view-angle and motion agnostic concurrent motion counting. This method can potentially use in large-scale remote rehabilitation and exercise training with only one camera.
Persistent Identifierhttp://hdl.handle.net/10722/336405
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHsu, Yu Cheng-
dc.contributor.authorEfstratios, Tsougenis-
dc.contributor.authorTsui, Kwok leung-
dc.date.accessioned2024-01-15T08:26:36Z-
dc.date.available2024-01-15T08:26:36Z-
dc.date.issued2024-
dc.identifier.citationHealth Information Science and Systems, 2024, v. 12, n. 1, article no. 1-
dc.identifier.urihttp://hdl.handle.net/10722/336405-
dc.description.abstractCounting the repetition of human exercise and physical rehabilitation is common in rehabilitation and exercise training. The existing vision-based repetition counting methods less emphasize the concurrent motions in the same video, and counting skeleton in different view angles. This work analyzed the spectrogram of the pose estimation cosine similarity to count the repetition. Besides the public datasets. This work also collected exercise videos from 11 adults to verify that the proposed method can handle concurrent motion and different view angles. The presented method was validated on the University of Idaho Physical Rehabilitation Movements Data Set (UI-PRMD) and MM-fit dataset. The overall mean absolute error (MAE) for MM-fit was 0.06 with off-by-one Accuracy (OBOA) of 0.94. As for the UI-PRMD dataset, MAE was 0.06 with OBOA 0.95. We have also tested the performance in various camera locations and concurrent motions with 57 skeleton time-series videos with an overall MAE of 0.07 and OBOA of 0.91. The proposed method provides a view-angle and motion agnostic concurrent motion counting. This method can potentially use in large-scale remote rehabilitation and exercise training with only one camera.-
dc.languageeng-
dc.relation.ispartofHealth Information Science and Systems-
dc.subjectCamera-
dc.subjectExercise-
dc.subjectRepetition counting-
dc.titleViewpoint-invariant exercise repetition counting-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s13755-023-00258-3-
dc.identifier.scopuseid_2-s2.0-85178212494-
dc.identifier.volume12-
dc.identifier.issue1-
dc.identifier.spagearticle no. 1-
dc.identifier.epagearticle no. 1-
dc.identifier.eissn2047-2501-
dc.identifier.isiWOS:001118316700003-

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