Kenneth Koltermann, John Clapham, GinaMari Blackwell, Woosub Jung, Evie N Burnet, Ye Gao, Huajie Shao, Leslie Cloud, Ingrid Pretzer-Aboff, Gang Zhou
{"title":"Gait-Guard: Turn-aware Freezing of Gait Detection for Non-intrusive Intervention Systems.","authors":"Kenneth Koltermann, John Clapham, GinaMari Blackwell, Woosub Jung, Evie N Burnet, Ye Gao, Huajie Shao, Leslie Cloud, Ingrid Pretzer-Aboff, Gang Zhou","doi":"10.1109/chase60773.2024.00016","DOIUrl":null,"url":null,"abstract":"<p><p>Freezing of gait significantly reduces the quality of life for Parkinson's disease patients by increasing the risk of injurious falls and reducing mobility. Real-time intervention mechanisms promise relief from these symptoms, but require accurate real-time, portable freezing of gait detection systems to be effective. Current real-time detection systems have unacceptable false positive freezing of gait identification rates to be adopted by the patients for real-world use. To rectify this, we propose Gait-Guard, a closed-loop, real-time, and portable freezing of gait detection and intervention system that treats symptoms in real-time with a low false positive rate. We collected 1591 freezing of gait events across 26 patients to evaluate Gait-Guard. Gait-Guard achieved a 112% reduction in the false positive intervention rate when compared with other validated real-time freezing of gait detection systems, and detected 96.5% of the true positives with an average intervention latency of just 378.5ms in a subject-independent study, making Gait-Guard a practical system for patients to use in their daily lives.</p>","PeriodicalId":93843,"journal":{"name":"...IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies","volume":"2024 ","pages":"61-72"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11384236/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"...IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/chase60773.2024.00016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/5 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Freezing of gait significantly reduces the quality of life for Parkinson's disease patients by increasing the risk of injurious falls and reducing mobility. Real-time intervention mechanisms promise relief from these symptoms, but require accurate real-time, portable freezing of gait detection systems to be effective. Current real-time detection systems have unacceptable false positive freezing of gait identification rates to be adopted by the patients for real-world use. To rectify this, we propose Gait-Guard, a closed-loop, real-time, and portable freezing of gait detection and intervention system that treats symptoms in real-time with a low false positive rate. We collected 1591 freezing of gait events across 26 patients to evaluate Gait-Guard. Gait-Guard achieved a 112% reduction in the false positive intervention rate when compared with other validated real-time freezing of gait detection systems, and detected 96.5% of the true positives with an average intervention latency of just 378.5ms in a subject-independent study, making Gait-Guard a practical system for patients to use in their daily lives.