A Novel CNN-BiLSTM Ensemble Model With Attention Mechanism for Sit-to-Stand Phase Identification Using Wearable Inertial Sensors

A Novel CNN-BiLSTM Ensemble Model With Attention Mechanism for Sit-to-Stand Phase Identification Using Wearable Inertial Sensors

Sit-to-stand transition phase identification is vital in the control of a wearable exoskeleton robot for assisting patients to stand stably. In this study, we aim to propose a method for segmenting and identifying the sit-to-stand phase using two inertial sensors. First, we defined the sit-to-stand...

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Autori principali: Xin Chen, Shibo Cai, Longjie Yu, Xiaoling Li, Bingfei Fan, Mingyu Du, Tao Liu, Guanjun Bao
Natura: Articolo
Lingua:English
Pubblicazione: IEEE 2024-01-01
Serie:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Soggetti:
Sit-to-stand transition
phase identification
convolutional neural network (CNN)
bidirectional long short-term memory (Bi-LSTM)
attention mechanism
inertial measurement unit (IMU)
Accesso online:https://ieeexplore.ieee.org/document/10439259/

Accesso online

https://ieeexplore.ieee.org/document/10439259/

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