{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:49:50Z","timestamp":1771955390970,"version":"3.50.1"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439490","type":"print"},{"value":"9783031439506","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":264,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Measuring the range of motion (ROM) is one of the important tasks in medical or healthcare sectors. However, person-to-person measurement is time-consuming and requires resources. In this paper, we propose an approach to estimate ROM using machine learning algorithm equipped with computer vision technology based on data-driven experiments. We describe the setup to gather experimental dataset to learn the angle of human joints in 2D space. From the extensive experiments and multi-linear regression learning approach, our method can reduce estimation error by 11.1% in average. We draw conclusions that machine learning-based, data-driven approaches could predict ROM better than using vision camera only.<\/jats:p>","DOI":"10.1007\/978-3-031-43950-6_25","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T03:25:20Z","timestamp":1695266720000},"page":"282-291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing the Measurement of the Range of Motion Using Multi-camera Learning Approaches"],"prefix":"10.1007","author":[{"given":"Giseop","family":"Noh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juyoung","family":"Ahn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bogja","family":"Jeoung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"25_CR1","unstructured":"PhysioTalk. ROM evaluation \u2013 Arm. https:\/\/youtu.be\/jKkuq3DIUkE"},{"key":"25_CR2","unstructured":"ML Kit. Pose Detection. https:\/\/developers.google.com\/ml-kit\/vision\/pose-detection"},{"key":"25_CR3","unstructured":"Lugaresi, C., et al.: MediaPipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172 (2019)"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Cronin, N.J., et al.: Markerless 2D kinematic analysis of underwater running: a deep learning approach.\u00a0J. Biomech. 87, 75\u201382 (2019)","DOI":"10.1016\/j.jbiomech.2019.02.021"},{"key":"25_CR5","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s40798-018-0139-y","volume":"4","author":"SL Colyer","year":"2018","unstructured":"Colyer, S.L., Evans, M., Cosker, D.P., et al.: A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system. Sports Med. Open 4, 24 (2018)","journal-title":"Sports Med. Open"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Qiu, Y., et al.: Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training. Biomed. Sig. Process. Control 72, 103323 (2022)","DOI":"10.1016\/j.bspc.2021.103323"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Haberkamp, L.D., Garcia, M.C., Bazett-Jones, D.M.: Validity of an artificial intelligence, human pose estimation model for measuring single-leg squat kinematics. J. Biomech. 144, 111333 (2022)","DOI":"10.1016\/j.jbiomech.2022.111333"},{"key":"25_CR8","unstructured":"HealthCare. PhysioGait. https:\/\/hcifitness.com\/collections\/products\/"},{"key":"25_CR9","unstructured":"AC Mobility. MOTOmed. https:\/\/acmobility.com.au\/motomed-rehabilitation-exercise-equipment\/"},{"key":"25_CR10","unstructured":"BCIT. Introducing the AAPLEwalk. https:\/\/www.bcit.ca\/applied-research\/makeplus-product-development\/rehabilitation-engineering-design-lab\/aaplewalk\/"},{"key":"25_CR11","unstructured":"Tonal. A home gym built to work as hard as you. https:\/\/www.tonal.com\/equipment\/"},{"key":"25_CR12","unstructured":"Google. Mediapipe. https:\/\/github.com\/google\/mediapipe"}],"container-title":["Lecture Notes in Computer Science","Digital Health Transformation, Smart Ageing, and Managing Disability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43950-6_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T03:27:28Z","timestamp":1695266848000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43950-6_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439490","9783031439506"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43950-6_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICOST","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Smart Homes and Health Telematics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wonju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icost2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icost-society.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}