Wisman, Hayley2025-03-222025-03-222025-03-21vt_gsexam:42584https://hdl.handle.net/10919/125064In the field of assistive robotics and exoskeletons, foot trajectory prediction has the potential to play a pivotal role in improving the functionality and user experience of worn devices. Rather than operating as a reactive system which only responds to user movement, a de vice which predicts future foot position can anticipate an action before it occurs, reducing latency and moving with the wearer for a more natural, uninhibited motion. While previous studies have focused on predicting continuous motion, they often overlook critical transitions between walking and standing, which are essential for natural locomotion. We propose in this study a foot trajectory prediction approach which leverages a recurrent deep learning architecture to make predictions based on sequential walking data. The first of the two ma chine learning models predicts the gait phase as a value between 0 and 1, while the second model leverages the gait phase prediction output to predict foot position in three dimensions. The models were trained and evaluated on IMU sensor data collected from three subjects instructed to walk on a treadmill at speeds varying from 0.5 mph to 1.5 mph. The result ing mean absolute error on gait phase percentage across subjects and velocity was 1.92%. For foot trajectory prediction, the cross-subject trained model achieved mean distance er ror of 2.85±2.89 cm, 3.29±2.82 cm, 4.15±4.12 cm, 5.33±5.46 cm, and 6.92±6.56 cm with prediction horizons of 0.1s, 0.25s, 0.5s, 1s, and 2s, respectively.ETDenIn CopyrightDeep learningexoskeletonsassistive roboticsgated recurrent unitgait phasefoot trajectoryThesis