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

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 1068 - 1077
• Main Authors: Chen, Xin, Cai, Shibo, Yu, Longjie, Li, Xiaoling, Fan, Bingfei, Du, Mingyu, Liu, Tao, Bao, Guanjun
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Algorithms; Angular velocity; attention mechanism; bidirectional long short-term memory (Bi-LSTM); Classification algorithms; convolutional neural network (CNN); Convolutional neural networks; Deep learning; Exoskeleton; Exoskeleton Device; Exoskeletons; Feature extraction; Force; Humans; inertial measurement unit (IMU); Inertial sensing devices; Learning algorithms; Legged locomotion; Machine learning; Machine learning algorithms; Momentum transfer; Movement; phase identification; Phases; Rehabilitation robots; Sensors; Sit-to-stand transition; Sitting Position; Standing Position; Velocity; Wearable Electronic Devices; Wearable technology
• DOI: 10.1109/TNSRE.2024.3366907
• ISSN: 1534-4320; 1558-0210

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 transition into five phases, namely, the initial sitting phase, the flexion momentum phase, the momentum transfer phase, the extension phase, and the stable standing phase based on the preprocessed acceleration and angular velocity data. We then employed a threshold method to recognize the initial sitting and the stable standing phases. Finally, we designed a novel CNN-BiLSTM-Attention algorithm to identify the three transition phases, namely, the flexion momentum phase, the momentum transfer phase, and the extension phase. Fifteen subjects were recruited to perform sit-to-stand transition experiments under a specific paradigm. A combination of the acceleration and angular velocity data features for the sit-to-stand transition phase identification were validated for the model performance improvements. The integration of the CNN, Bi-LSTM, and Attention modules demonstrated the reasonableness of the proposed algorithms. The experimental results showed that the proposed CNN-BiLSTM-Attention algorithm achieved the highest average classification accuracy of 99.5% for all five phases when compared to both traditional machine learning algorithms and deep learning algorithms on our customized dataset (STS-PD). The proposed sit-to-stand phase recognition algorithm could serve as a foundation for the control of wearable exoskeletons and is important for the further development of intelligent wearable exoskeleton rehabilitation robots.