A Probability Distribution Model-Based Approach for Foot Placement Prediction in the Early Swing Phase With a Wearable IMU Sensor

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 29; pp. 2595 - 2604
• Main Authors: Chen, Xinxing, Zhang, Kuangen, Liu, Haiyuan, Leng, Yuquan, Fu, Chenglong
• Format: Journal Article
• Language: English
• Published: United States IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayes methods; Bayes Theorem; Bayesian analysis; Bayesian inference; Biomechanical Phenomena; Errors; Estimation; Feature extraction; Feet; Foot; foot placement; Gait; gait prediction; Heels; Human walking; Humans; Inertial platforms; Inertial sensing devices; inertial sensors; Legged locomotion; Mathematical models; Placement; Predictions; Probability distribution; Robots; Statistical analysis; Statistical inference; Trajectory; Velocity; Walking; Wearable Electronic Devices; Wearable technology

Predicting the next foot placement of humans during walking can help improve compliant interactions between humans and walking aid robots. Previous studies have focused on foot placement estimation with wearable inertial sensors after heel-strike, but few have predicted foot placements in advance du...