{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:38:12Z","timestamp":1770835092963,"version":"3.50.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T00:00:00Z","timestamp":1652832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"New South Wales Health Early-Mid Career Fellowship"},{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["APP1134919"],"award-info":[{"award-number":["APP1134919"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Centre of Research Excellence in Digital Health","award":["1170937"],"award-info":[{"award-number":["1170937"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>People are increasingly encouraged to self-manage their chronic conditions; however, many struggle to practise it effectively. Most studies that investigate patient work (ie, tasks involved in self-management and contexts influencing such tasks) rely on self-reports, which are subject to recall and other biases. Few studies use wearable cameras and deep learning to capture and classify patient work activities automatically.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>We propose a deep learning approach to classify activities of patient work collected from wearable cameras, thereby studying self-management routines more effectively. Twenty-six people with type 2 diabetes and comorbidities wore a wearable camera for a day, generating more than 400 h of video across 12 daily activities. To classify these video images, a weighted ensemble network that combines Linear Discriminant Analysis, Deep Convolutional Neural Networks, and Object Detection algorithms is developed. Performance of our model is assessed using Top-1 and Top-5 metrics, compared against manual classification conducted by 2 independent researchers.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Across 12 daily activities, our model achieved on average the best Top-1 and Top-5 scores of 81.9 and 86.8, respectively. Our model also outperformed other non-ensemble techniques in terms of Top-1 and Top-5 scores for most activity classes, demonstrating the superiority of leveraging weighted ensemble techniques.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Deep learning can be used to automatically classify daily activities of patient work collected from wearable cameras with high levels of accuracy. Using wearable cameras and a deep learning approach can offer an alternative approach to investigate patient work, one not subjected to biases commonly associated with self-report methods.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocac071","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T11:27:13Z","timestamp":1651145233000},"page":"1400-1408","source":"Crossref","is-referenced-by-count":5,"title":["Identifying daily activities of patient work for type 2 diabetes and co-morbidities: a deep learning and wearable camera approach"],"prefix":"10.1093","volume":"29","author":[{"given":"Hao","family":"Xiong","sequence":"first","affiliation":[{"name":"Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University , Sydney, New South Wales, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hoai Nam","family":"Phan","sequence":"additional","affiliation":[{"name":"Centre for Health 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