An Improved Gait Detection Algorithm Based on Zero-Velocity Detection Method and its Application

• Published in: IEEE sensors journal Vol. 24; no. 2; pp. 2066 - 2078
• Main Authors: Fang, Zedong, Xia, Yuanqing, Zhai, Di-Hua
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Angular velocity; Ankle; Biosensors; Data processing; Diseases; Foot; Gait; Gait detection; Gait recognition; gradient descent method; Inertial navigation; Inertial sensing devices; inertial sensor; Inertial sensors; Kalman filter; Kalman filters; Legged locomotion; Sensors; Wearable technology; zero-velocity detection method
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3336790

Gait detection and recognition have proven to be valuable in various fields. Based on inertial sensors, wearable devices offer a suitable means for extracting gait information. This study focuses on designing a human gait detection algorithm for healthy subjects using inertial sensors. By placing a single sensor at the ankle, the algorithm estimates the body's trajectory and extracts gait information through various data processing methods. A wearable device is designed to implement the proposed algorithm, which is then tested extensively. Experimental results demonstrate that the proposed algorithm achieves an average relative error, compared to a visual system serving as the gold standard, of 3.26% for travel distance, 0.02% for stride frequency, 3.26% for stride length, 3.26% for pace, 1.41% for stride time, 2.72% for stance time, and 2.37% for relevant stance. Furthermore, the algorithm and device prove to be suitable for different testers and various wearing methods (i.e., left or right ankle). When using two sensors, one on each ankle, additional gait information such as step time, single stance time, and symmetry can be extracted.