{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:03:55Z","timestamp":1782846235459,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>Freezing of Gait (FOG) is a prevalent motor impairment causing sudden gait hesitation and temporary inability to initiate or continue walking, particularly affecting individuals with Parkinson's disease (PD). FOG impairs functional mobility and leads to an increased risk of falls, resulting in serious health complications. In our work, we developed a machine learning (ML) model trained to differentiate freezing episodes during turning from normal walking on data acquired from a wearable 3D lower back sensor. The proposed models were evaluated under both subject-dependent and subject-independent settings. While the Decision Tree achieved a high F1-score and recall of 99% under subject-dependent evaluation, the LightGBM model demonstrated strong generalization performance under subject-independent evaluation with an F1-score of 80.9% and recall of 93.4%. These results are comparable to findings from previous studies, while providing a more realistic evaluation through subject-independent validation. The successful application of machine learning in FOG detection from wearable sensor data holds promise for enhancing the understanding and management of this challenging aspect of Parkinson's disease.\nKeywords: Machine Learning, Accelerometer Data, Wearable Sensor, FOG Detection.<\/jats:p>","DOI":"10.7148\/2026-0779","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:08:43Z","timestamp":1782842923000},"page":"779-785","source":"Crossref","is-referenced-by-count":0,"title":["Wearable-sensor-based detection of freezing of gait in parkinson's disease using machine learning techniques"],"prefix":"10.7148","author":[{"given":"Ayse Kosal","family":"Bulbul","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Irfan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria K.","family":"Jaakkola","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riku","family":"Klen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulhamit","family":"Subasi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:08:48Z","timestamp":1782842928000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0779_sstmsv_ecms2026_0004.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0779","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}