Gait phase detection based on LSTM-CRF for stair ambulation

Haochen Wei; Raymond Kai Yu Tong; Michael Yu Wang; Chao Chen

doi:10.1109/LRA.2023.3303787

Gait phase detection based on LSTM-CRF for stair ambulation

Haochen Wei, Raymond Kai Yu Tong, Michael Yu Wang, Chao Chen

Mechanical & Aerospace Engineering

Research output: Contribution to journal › Article › Research › peer-review

2 Citations (Scopus)

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.

Original language	English
Pages (from-to)	6029-6035
Number of pages	7
Journal	IEEE Robotics and Automation Letters
Volume	8
Issue number	9
DOIs	https://doi.org/10.1109/LRA.2023.3303787
Publication status	Published - Sept 2023

Keywords

Deep learning methods
gait phase detection
IMU
prosthetics and exoskeletons
stair ambulation

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10.1109/LRA.2023.3303787

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title = "Gait phase detection based on LSTM-CRF for stair ambulation",

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.",

keywords = "Deep learning methods, gait phase detection, IMU, prosthetics and exoskeletons, stair ambulation",

author = "Haochen Wei and Tong, {Raymond Kai Yu} and Wang, {Michael Yu} and Chao Chen",

note = "Funding Information: This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by Monash University Human Research Ethics Committee under Application No. 17072, and performed in line with the Functional Validation of Knee Brace through Treadmill Testing. Publisher Copyright: {\textcopyright} 2016 IEEE.",

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AU - Tong, Raymond Kai Yu

AU - Wang, Michael Yu

AU - Chen, Chao

N1 - Funding Information: This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by Monash University Human Research Ethics Committee under Application No. 17072, and performed in line with the Functional Validation of Knee Brace through Treadmill Testing. Publisher Copyright: © 2016 IEEE.

PY - 2023/9

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N2 - 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.

AB - 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.

KW - Deep learning methods

KW - gait phase detection

KW - IMU

KW - prosthetics and exoskeletons

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Gait phase detection based on LSTM-CRF for stair ambulation

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Keywords

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