Predicting Transitioning Walking Gaits: Hip and Knee Joint Trajectories From the Motion of Walking Canes

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 27; no. 9; pp. 1791 - 1800
• Main Authors: Mounir Boudali, A., Sinclair, Peter J., Manchester, Ian R.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2019
• Subjects: Adult; Algorithms; Biomechanical Phenomena; Canes; data analysis; Exoskeletons; Female; Gait - physiology; gait trajectory prediction; Hip; Hip - physiology; Humans; Knee Joint - physiology; Least-Squares Analysis; Legged locomotion; Lower Extremity - physiology; lower limb rehabilitation; Male; Neural Networks, Computer; Posture - physiology; Robot sensing systems; system identification; Task analysis; Trajectory; Walking - physiology; Young Adult
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2019.2933896

In recent years, wearable exoskeletons and powered prosthetics have been considered key elements to remedy mobility loss. One of the main challenges pertaining to this field is the prediction of the wearer's desired motion. In this paper, we perform a human locomotion analysis, and we investigate the accuracy of predicting the angular position of the lower limb joints from the motion of walking canes. Nine healthy subjects took part of this study and performed a locomotor task that comprised straight walking on flat ground, stair ascent, and upright resting posture. Recurrent Neural Networks and polynomial fitting using Least Squares were used to model dynamic and static non-linear mappings, respectively, between the motion of a cane and its contra-lateral leg joints. A successful prediction of both the hip and knee joints was achieved using information from the cane only, and significant improvement of the prediction error was realized through the addition of data from the arm joints. Overall, Recurrent Neural Networks outperformed Least Squares for both joints' angular position prediction. When using the cane only, the static maps were able to predict steady behaviour but failed in predicting transitioning, as opposed to RNN, which was able to capture both steady behaviour and transitions.