Kenneth Koltermann, Woosub Jung, GinaMari Blackwell, Abbott Pinney, Matthew Chen, Leslie Cloud, Ingrid Pretzer-Aboff, Gang Zhou
{"title":"FoG Finder:步态检测和治疗的实时冻结。","authors":"Kenneth Koltermann, Woosub Jung, GinaMari Blackwell, Abbott Pinney, Matthew Chen, Leslie Cloud, Ingrid Pretzer-Aboff, Gang Zhou","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Freezing of gait is a serious symptom of Parkinson's disease that increases the risk of injury through falling, and reduces quality of life. Current clinical freezing of gait treatments fail to adequately address the fall risk posed by freezing of gait symptoms, and current real-time treatment systems have high false positive rates. To address this problem, we designed a closed-loop, non-intrusive, and real-time freezing of gait detection and treatment system, FoG-Finder, that automatically detects and treats freezing of gait. To evaluate FoG-Finder, we first collected 716 freezing of gait events from 11 patients. We then compared FoG-Finder against other real-time systems with our dataset. Our system was able to achieve a 13.4% higher F1 score and a 10.7% higher overall accuracy while achieving a reduction of 85.8% in the false positive treatment rate compared with other validated real-time freezing of gait detection and treatment systems. Additionally, FoG-Finder achieved an average treatment latency of 427ms and 615ms for subject-dependent and leave-one-subject-out settings, respectively, making it a viable system to treat freezing of gait in the real-world.</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":"2023 ","pages":"22-33"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513482/pdf/nihms-1931643.pdf","citationCount":"0","resultStr":"{\"title\":\"FoG-Finder: Real-time Freezing of Gait Detection and Treatment.\",\"authors\":\"Kenneth Koltermann, Woosub Jung, GinaMari Blackwell, Abbott Pinney, Matthew Chen, Leslie Cloud, Ingrid Pretzer-Aboff, Gang Zhou\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Freezing of gait is a serious symptom of Parkinson's disease that increases the risk of injury through falling, and reduces quality of life. Current clinical freezing of gait treatments fail to adequately address the fall risk posed by freezing of gait symptoms, and current real-time treatment systems have high false positive rates. To address this problem, we designed a closed-loop, non-intrusive, and real-time freezing of gait detection and treatment system, FoG-Finder, that automatically detects and treats freezing of gait. To evaluate FoG-Finder, we first collected 716 freezing of gait events from 11 patients. We then compared FoG-Finder against other real-time systems with our dataset. Our system was able to achieve a 13.4% higher F1 score and a 10.7% higher overall accuracy while achieving a reduction of 85.8% in the false positive treatment rate compared with other validated real-time freezing of gait detection and treatment systems. Additionally, FoG-Finder achieved an average treatment latency of 427ms and 615ms for subject-dependent and leave-one-subject-out settings, respectively, making it a viable system to treat freezing of gait in the real-world.</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\":\"2023 \",\"pages\":\"22-33\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513482/pdf/nihms-1931643.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"...IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. 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FoG-Finder: Real-time Freezing of Gait Detection and Treatment.
Freezing of gait is a serious symptom of Parkinson's disease that increases the risk of injury through falling, and reduces quality of life. Current clinical freezing of gait treatments fail to adequately address the fall risk posed by freezing of gait symptoms, and current real-time treatment systems have high false positive rates. To address this problem, we designed a closed-loop, non-intrusive, and real-time freezing of gait detection and treatment system, FoG-Finder, that automatically detects and treats freezing of gait. To evaluate FoG-Finder, we first collected 716 freezing of gait events from 11 patients. We then compared FoG-Finder against other real-time systems with our dataset. Our system was able to achieve a 13.4% higher F1 score and a 10.7% higher overall accuracy while achieving a reduction of 85.8% in the false positive treatment rate compared with other validated real-time freezing of gait detection and treatment systems. Additionally, FoG-Finder achieved an average treatment latency of 427ms and 615ms for subject-dependent and leave-one-subject-out settings, respectively, making it a viable system to treat freezing of gait in the real-world.