{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T23:46:10Z","timestamp":1777851970547,"version":"3.51.4"},"reference-count":14,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:p>Background: Maintaining physical activity (PA) and functioning (mobility, balance) is essential for older adults\u2019 well-being and quality of life. However, current methods (functional tests, self-reports) and available techniques (accelerometers, sensors, advanced movement analysis systems) for assessing physical activity and functioning have shown to be less reliable, time- and resource-consuming with limited routine usage in clinical practice. There is a need to simplify the assessment of physical activity and functioning among older adults both in health care and clinical studies. This work presents a study on using Skeleton Avatar Technology (SAT) for this assessment. SAT analyzes human movement videos using artificial intelligence (AI). The study compares handy SAT based on 2D camera technology (2D SAT) with previously studied 3D SAT for assessing physical activity and functioning in older adults. Objective: To explore whether 2D SAT yields accurate results in physical activity and functioning assessment in healthy older adults, statistically compared to the accuracy of 3D SAT. Method: The mobile pose estimation model provided by Tensorflow was used to extract 2D skeletons from the video recordings of functional test movements. Deep neural networks were used to predict the outcomes of functional tests (FT), expert-based movement quality assessment (EA), accelerometer-based assessments (AC), and self-assessments of PA (SA). To compare the accuracy with 3D SAT models, statistical analysis was used to test whether the difference in the predictions between 2D and 3D models is significant or not. Results: Overall, the accuracy of 2D SAT is lower than 3D SAT in predicting FTs and EA. 2D SAT was able to predict AC with 7% Mean Absolute Error (MAE), and self-assessed PA (SA) with 16% MAE. On average MAE was 4% higher for 2D than for 3D SAT. There was no significant difference found between the 2D and the 3D model for AC and for two FTs (30 seconds chair stand test, 30sCST and Timed up and go, TUG). A significant difference was found for the 2D- and 3D-model of another FT (4-stage balance test, 4SBT). Conclusion: Altogether, the results show that handy 2D SAT might be used for assessing physical activity in older adults without a significant loss of accuracy compared to time-consuming standard tests and to bulky 3D SAT-based assessments. However, the accuracy of 2D SAT in assessing physical functioning should be improved. Taken together, this study shows promising results to use 2D SAT for assessing physical activity in healthy older adults in future clinical studies and clinical practice.<\/jats:p>","DOI":"10.1177\/14604582231214589","type":"journal-article","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T02:39:20Z","timestamp":1699065560000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["A comparative study of the 2D- and 3D-based skeleton avatar technology for assessing physical activity and functioning among healthy older adults"],"prefix":"10.1177","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9062-1609","authenticated-orcid":false,"given":"Alisa","family":"Lincke","sequence":"first","affiliation":[{"name":"Faculty of Technology, Department of Computer Science and Media Technology, Linnaeus University, V\u00e4xj\u00f6, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cecilia","family":"Fagerstr\u00f6m","sequence":"additional","affiliation":[{"name":"Faculty of Health and Life Sciences, Department of Health and Caring Sciences, Linnaeus University, V\u00e4xj\u00f6, Sweden"},{"name":"The Research Section, Kalmar, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mirjam","family":"Ekstedt","sequence":"additional","affiliation":[{"name":"Faculty of Health and Life Sciences, Department of Health and Caring Sciences, Linnaeus University, V\u00e4xj\u00f6, Sweden;"},{"name":"Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Welf","family":"L\u00f6we","sequence":"additional","affiliation":[{"name":"Faculty of Technology, Department of Computer Science and Media Technology, Linnaeus University, V\u00e4xj\u00f6, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sofia","family":"Back\u00e5berg","sequence":"additional","affiliation":[{"name":"Faculty of Health and Life Sciences, Department of Health and Caring Sciences, Linnaeus University, V\u00e4xj\u00f6, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2023,11,3]]},"reference":[{"key":"bibr1-14604582231214589","doi-asserted-by":"publisher","DOI":"10.3389\/fcomp.2020.601271"},{"key":"bibr2-14604582231214589","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-8303-8_29"},{"key":"bibr3-14604582231214589","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-8311-3_22"},{"key":"bibr4-14604582231214589","doi-asserted-by":"publisher","DOI":"10.33965\/eh2019_201910L005"},{"key":"bibr5-14604582231214589","unstructured":"Hagelb\u00e4ck J, Lincke A, L\u00f6we W, et al. On the agreement of commodity 3D cameras. In: Arabnia HR, Deligiannidis L, Tinetti FG, et al. (eds). Proceedings of the 2019 International Conference on Image Processing, Computer Vision, and Pattern Recognition. Long Beach, NV, 2019b, pp. 36\u201342."},{"key":"bibr6-14604582231214589","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100609"},{"key":"bibr7-14604582231214589","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3073720"},{"key":"bibr8-14604582231214589","volume-title":"IRAF, an Instrument for movement analysis of person transfer and mobility in daily living","author":"Back\u00e5berg S","year":"2020"},{"key":"bibr9-14604582231214589","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"bibr10-14604582231214589","volume-title":"Deep learning","author":"Goodfellow I","year":"2016"},{"key":"bibr11-14604582231214589","volume-title":"Tensorflow: large-scale machine learning on heterogeneous systems","author":"Abadi M","year":"2015"},{"key":"bibr12-14604582231214589","doi-asserted-by":"publisher","DOI":"10.1023\/A:1024068626366"},{"key":"bibr13-14604582231214589","unstructured":"Vanwinckelen G, Blockeel H. On estimating model accuracy with repeated cross-validation. In: BeneLearn 2012: Proceedings of the 21st Belgian-Dutch conference on machine learning, 2012, pp. 39\u201344."},{"key":"bibr14-14604582231214589","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017197"}],"container-title":["Health Informatics Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14604582231214589","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14604582231214589","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14604582231214589","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:28:19Z","timestamp":1777501699000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14604582231214589"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":14,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["10.1177\/14604582231214589"],"URL":"https:\/\/doi.org\/10.1177\/14604582231214589","relation":{},"ISSN":["1460-4582","1741-2811"],"issn-type":[{"value":"1460-4582","type":"print"},{"value":"1741-2811","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10]]},"article-number":"14604582231214589"}}