Assessment of Foot Trajectory for Human Gait Phase Detection Using Wireless Ultrasonic Sensor Network

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 24; no. 1; pp. 88 - 97
• Main Authors: Qi, Yongbin, Soh, Cheong Boon, Gunawan, Erry, Low, Kay-Soon, Thomas, Rijil
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Acoustics; Actigraphy - instrumentation; Adult; Algorithms; Equipment Design; Equipment Failure Analysis; Errors; Female; Foot; Foot - physiology; Gait; Gait - physiology; Gait analysis; Humans; Injuries; Legged locomotion; Male; Mathematical model; Monitoring, Ambulatory - instrumentation; Phase detection; Rehabilitation; Reproducibility of Results; Sensitivity and Specificity; Sensors; Tracking; Transducers; Ultrasonic technology; Ultrasonic testing; Ultrasonography - instrumentation; ultrasound; Walking; Walking - physiology; walking assessment; wearable sensor; wireless sensor network; Wireless Technology - instrumentation
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2015.2409123

This paper presents a new highly accurate gait phase detection system using wearable wireless ultrasonic sensors, which can be used in gait analysis or rehabilitation applications. The gait phase detection system uses the foot displacement information during walking to extract the following gait phases: heel-strike, heel-off, toe-off, and mid-swing. The displacement of foot-mounted ultrasonic sensor is obtained from several passive anchors placed at known locations by employing local spherical positioning technique, which is further enhanced by the combination of recursive Newton-Gauss method and Kalman Filter. The algorithm performance is examined by comparing with a commercial optical motion tracking system with ten healthy subjects and two foot injured subjects. Accurate estimates of gait cycle (with an error of -0.02 ±0.01 s), stance phase(with an error of 0.04±0.03 s), and swing phase (with an error of -0.05±0.03 s) compared to the reference system are obtained. We have also investigated the influence of walking velocities on the performance of the proposed gait phase detection algorithm. Statistical analysis shows that there is no significant difference between both systems during different walking speeds. Moreover, we have tested and discussed the possibility of the proposed system for clinical applications by analyzing the experimental results for both healthy and injured subjects. The experiments show that the estimated gait phases have the potential to become indicators for sports and rehabilitation engineering.