Leg Joint Angle Estimation From a Single Inertial Sensor During Variety of Walking Motions: A Deep Learning Approach

Abstract

This study evaluated the capability of a single inertial sensor based on both legs’ hip and knee joint angles estimation during four different walking patterns in an outdoor setting. The sensor was placed on the upper part of the tibia, a location chosen due to its large range of motion and minimal foot-ground impact influence. A Bi-LSTM (bidirectional long short-term memory) data-driven approach was used for joint angle estimation. The results showed smaller errors in intra-subject angle estimation compared to inter-subject, with an average MAE (mean absolute error) of 2.11° to 3.65°. The study suggests that deep learning approaches can effectively process single IMU (inertial measurement unit) data for accurate human motion monitoring, reducing the need for multiple sensors. Despite using only one sensor and four different walking patterns (zigzag, sideways, backward, and ramp walking), our method achieved similar results to previous studies that used single-motion activities. This study, conducted outdoors without instructing participants, is a step closer to real-world application, potentially providing insights into lower body biomechanics in physiotherapy, mobility improvement progress after surgery, and aiding in the development of personalized exoskeletons robots.