{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T14:12:05Z","timestamp":1778940725464,"version":"3.51.4"},"reference-count":41,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:00:00Z","timestamp":1778889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"National Science Foundation","award":["CNS-2227002"],"award-info":[{"award-number":["CNS-2227002"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Comput. Healthcare"],"published-print":{"date-parts":[[2026,7,30]]},"abstract":"<jats:p>\n                    Parkinson\u2019s disease (PD) significantly affects patients\u2019 quality of life through debilitating motor symptoms, such as Freezing of Gait (FoG). Continuous, in-home monitoring of FoG is essential for timely clinical intervention but remains challenging due to high power consumption, annotation cost, and the controlled environments required by current wearables. We introduce\n                    <jats:italic toggle=\"yes\">LIFT-PD<\/jats:italic>\n                    (the source code is available at:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/shovito66\/LIFT-PD\">https:\/\/github.com\/shovito66\/LIFT-PD<\/jats:ext-link>\n                    ), a novel self-supervised learning (SSL) framework for real-time, patient-independent FoG detection that uniquely utilizes a single waist-worn accelerometer\u2014an approach traditionally considered less optimal due to weaker gait signatures. LIFT-PD leverages SSL on unlabeled data collected from uncontrolled, real-world settings and employs a novel Differential Hopping Windowing Technique (DHWT) to address gait variability and dataset imbalance. Additionally, an opportunistic inference module selectively activates the deep learning model only during patient movement, significantly reducing power consumption and enabling continuous monitoring (\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(&gt;\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    48\u00a0hours). Experimental results show that LIFT-PD achieves a 7.25% increase in precision and 4.4% improvement in accuracy compared to supervised and semi-supervised baseline models while requiring approximately 40% fewer labeled training samples. Evaluations across diverse patient characteristics-including severity, medication state, age, and gender-confirm the model\u2019s robustness and clinical applicability, positioning LIFT-PD as a practical, energy-efficient, and scalable solution for continuous real-world FoG monitoring in PD.\n                  <\/jats:p>","DOI":"10.1145\/3802589","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:13:43Z","timestamp":1774368823000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson\u2019s Disease"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1949-9795","authenticated-orcid":false,"given":"Shovito Barua","family":"Soumma","sequence":"first","affiliation":[{"name":"College of Health Solutions, Arizona State University, Phoenix, Arizona, USA and Computer Science, Arizona State University Ira A Fulton Schools of Engineering, Tempe, Arizona, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4639-6544","authenticated-orcid":false,"given":"Daniel S.","family":"Peterson","sequence":"additional","affiliation":[{"name":"College of Health Solutions, Arizona State University, Phoenix, Arizona, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3608-4456","authenticated-orcid":false,"given":"Shyamal H.","family":"Mehta","sequence":"additional","affiliation":[{"name":"Mayo Clinic, Scottsdale, Arizona, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1844-1416","authenticated-orcid":false,"given":"Hassan","family":"Ghasemzadeh","sequence":"additional","affiliation":[{"name":"College of Health Solutions, Arizona State University, Phoenix, Arizona, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,16]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/BSN58485.2023.10331481"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","first-page":"537384","DOI":"10.3389\/frobt.2021.537384","article-title":"DeepFoG: An IMU-based detection of freezing of gait episodes in Parkinson\u2019s disease patients via deep learning","volume":"8","author":"Bikias Thomas","year":"2021","unstructured":"Thomas Bikias, Dimitrios Iakovakis, Stelios Hadjidimitriou, Vasileios Charisis, and Leontios J. 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Curran Associates Inc., 1\u201313."},{"issue":"1","key":"e_1_3_1_33_2","first-page":"S319","article-title":"AI-Powered detection of freezing of gait using wearable sensor data in patients with Parkinson\u2019s disease","volume":"39","author":"Soumma Shovito Barua","year":"2024","unstructured":"Shovito Barua Soumma, Daniel Peterson, Hassan. Ghasemzadeh, and Shyamal. Mehta. 2024. AI-Powered detection of freezing of gait using wearable sensor data in patients with Parkinson\u2019s disease. Movement Disorders 39, suppl 1 (2024), S319\u2013S320. Retrieved from https:\/\/mdsabstracts.org\/abstract\/ai-powered-detection-of-freezing-of-gait-using-wearable-sensor-data-in-patients-with-parkinsons-disease\/Abstract","journal-title":"Movement Disorders"},{"key":"e_1_3_1_34_2","volume-title":"IEEE-EMBS International Conference on Body Sensor Networks 2025","author":"Soumma Shovito Barua","year":"2025","unstructured":"Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter, Melanie Hingle, and Hassan Ghasemzadeh. 2025. SenseCF: LLM-prompted counterfactuals for intervention and sensor data augmentation. In IEEE-EMBS International Conference on Body Sensor Networks 2025. Retrieved from https:\/\/openreview.net\/forum?id=8qqMeF9EmT"},{"key":"e_1_3_1_35_2","unstructured":"Shovito Barua Soumma and Hassan Ghasemzadeh. 2026. GlyRAG: Context-aware retrieval-augmented framework for blood glucose forecasting. arXiv:2601.05353. Retrieved from https:\/\/arxiv.org\/abs\/2601.05353"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC58623.2025.11254302"},{"key":"e_1_3_1_37_2","first-page":"1195","volume-title":"31st International Conference on Neural Information Processing Systems (NIPS \u201917)","author":"Tarvainen Antti","year":"2017","unstructured":"Antti Tarvainen and Harri Valpola. 2017. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. 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