Gait Phase Detection Based on LSTM-CRF for Stair Ambulation

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.

Automated summary

This study focuses on accurately identifying gait phases during stair ambulation using IMU-based phase detection and a LSTM-CRF hybrid model. Data was collected from four IMU sensors attached to the thighs and shanks of ten healthy subjects during stair ascent and descent trials. The network's performance was evaluated using F1-score, recall, and precision, with average scores of 96.3%, 96.6%, and 95.9% respectively.