Gait Phase Detection Based on LSTM-CRF for Stair Ambulation
Haochen Wei, Raymond Kai‐Yu Tong, Michael Yu Wang, Chao Chen
Abstract
It is essential to accurately identify gait phases when active exoskeleton devices assist with the lower limbs. This work focuses on IMU-based phase detection for stair ambulation. In order to enhance the detection sensitivity of phase transition, this work utilises the LSTM-CRF hybrid model. Four IMU sensors attached to the thighs and shanks on both legs were utilised to collect data during trials on ten healthy subjects for stair ascent and descent. The network's performance is evaluated by F1-score, recall (true positive rate), and precision, which are 96.3% on average with a standard deviation (std) of 1.9%, 96.6% on average with an std of 1.6%, and 95.9% on average with an std of 2.7%, respectively.