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Gait-Guard: Turn-aware Freezing of Gait Detection for Non-intrusive Intervention Systems

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.

References
1.
Mazilu S, Calatroni A, Gazit E, Mirelman A, Hausdorff J, Troster G . Prediction of Freezing of Gait in Parkinson's From Physiological Wearables: An Exploratory Study. IEEE J Biomed Health Inform. 2015; 19(6):1843-54. DOI: 10.1109/JBHI.2015.2465134. View

2.
Sun R, Hu K, Ehgoetz Martens K, Hagenbuchner M, Tsoi A, Bennamoun M . Higher Order Polynomial Transformer for Fine-Grained Freezing of Gait Detection. IEEE Trans Neural Netw Learn Syst. 2023; 35(9):12746-12759. DOI: 10.1109/TNNLS.2023.3264647. View

3.
Hu K, Wang Z, Mei S, Ehgoetz Martens K, Yao T, Lewis S . Vision-Based Freezing of Gait Detection With Anatomic Directed Graph Representation. IEEE J Biomed Health Inform. 2019; 24(4):1215-1225. DOI: 10.1109/JBHI.2019.2923209. View

4.
Bikias T, Iakovakis D, Hadjidimitriou S, Charisis V, Hadjileontiadis L . DeepFoG: An IMU-Based Detection of Freezing of Gait Episodes in Parkinson's Disease Patients via Deep Learning. Front Robot AI. 2021; 8:537384. PMC: 8185568. DOI: 10.3389/frobt.2021.537384. View

5.
Ahlrichs C, Sama A, Lawo M, Cabestany J, Rodriguez-Martin D, Perez-Lopez C . Detecting freezing of gait with a tri-axial accelerometer in Parkinson's disease patients. Med Biol Eng Comput. 2015; 54(1):223-33. DOI: 10.1007/s11517-015-1395-3. View