Real-Time Detection of Gait Events by Recurrent Neural Networks

• Published in: IEEE access Vol. 9; pp. 134849 - 134857
• Main Authors: Wang, Fu-Cheng, Li, You-Chi, Kuo, Tien-Yun, Chen, Szu-Fu, Lin, Chin-Hsien
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cures; Data models; deep learning; Diseases; Gait; Gait recognition; heel strike; Heels; Heuristic algorithms; Legged locomotion; neural network; Neural networks; Parkinson's disease; Real time; Real-time systems; Recurrent neural networks; Rehabilitation; stroke; Stroke (medical condition); Walking

This paper proposes a gait detection model that can recognize important gait events in real time. Human gaits are periodic, with each gait cycle containing the important gait events of mid-swing, heel-strike (HS), and toe-off. The correct identification of different gait patterns caused by stroke or...