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Ding Z, Hu T, Li Y, Li L, Li Q, Jin P, Yi C. A Novel Active Learning Framework for Cross-Subject Human Activity Recognition from Surface Electromyography. SENSORS (BASEL, SWITZERLAND) 2024; 24:5949. [PMID: 39338694 PMCID: PMC11435705 DOI: 10.3390/s24185949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Revised: 08/27/2024] [Accepted: 09/11/2024] [Indexed: 09/30/2024]
Abstract
Wearable sensor-based human activity recognition (HAR) methods hold considerable promise for upper-level control in exoskeleton systems. However, such methods tend to overlook the critical role of data quality and still encounter challenges in cross-subject adaptation. To address this, we propose an active learning framework that integrates the relation network architecture with data sampling techniques. Initially, target data are used to fine tune two auxiliary classifiers of the pre-trained model, thereby establishing subject-specific classification boundaries. Subsequently, we assess the significance of the target data based on classifier discrepancy and partition the data into sample and template sets. Finally, the sampled data and a category clustering algorithm are employed to tune model parameters and optimize template data distribution, respectively. This approach facilitates the adaptation of the model to the target subject, enhancing both accuracy and generalizability. To evaluate the effectiveness of the proposed adaptation framework, we conducted evaluation experiments on a public dataset and a self-constructed electromyography (EMG) dataset. Experimental results demonstrate that our method outperforms the compared methods across all three statistical metrics. Furthermore, ablation experiments highlight the necessity of data screening. Our work underscores the practical feasibility of implementing user-independent HAR methods in exoskeleton control systems.
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Affiliation(s)
- Zhen Ding
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Tao Hu
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China; (T.H.); (Y.L.); (L.L.); (Q.L.); (P.J.)
| | - Yanlong Li
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China; (T.H.); (Y.L.); (L.L.); (Q.L.); (P.J.)
| | - Longfei Li
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China; (T.H.); (Y.L.); (L.L.); (Q.L.); (P.J.)
| | - Qi Li
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China; (T.H.); (Y.L.); (L.L.); (Q.L.); (P.J.)
| | - Pengyu Jin
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China; (T.H.); (Y.L.); (L.L.); (Q.L.); (P.J.)
| | - Chunzhi Yi
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China;
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Xiang L, Gu Y, Gao Z, Yu P, Shim V, Wang A, Fernandez J. Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors. Comput Biol Med 2024; 170:108016. [PMID: 38277923 DOI: 10.1016/j.compbiomed.2024.108016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/14/2024] [Accepted: 01/19/2024] [Indexed: 01/28/2024]
Abstract
The ankle joint plays a crucial role in gait, facilitating the articulation of the lower limb, maintaining foot-ground contact, balancing the body, and transmitting the center of gravity. This study aimed to implement long short-term memory (LSTM) networks for predicting ankle joint angles, torques, and contact forces using inertial measurement unit (IMU) sensors. Twenty-five healthy participants were recruited. Two IMU sensors were attached to the foot dorsum and the vertical axis of the distal anteromedial tibia in the right lower limb to record acceleration and angular velocity during running. We proposed a LSTM-MLP (multilayer perceptron) model for training time-series data from IMU sensors and predicting ankle joint biomechanics. The model underwent validation and testing using a custom nested k-fold cross-validation process. The average values of the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) for ankle dorsiflexion joint and moment, subtalar inversion joint and moment, and ankle joint contact forces were 0.89 ± 0.04, 0.75 ± 1.04, and 2.96 ± 4.96 for walking, and 0.87 ± 0.07, 0.88 ± 1.26, and 4.1 ± 7.17 for running, respectively. This study demonstrates that IMU sensors, combined with LSTM neural networks, are invaluable tools for evaluating ankle joint biomechanics in lower limb pathological diagnosis and rehabilitation, offering a cost-effective and versatile alternative to traditional experimental settings.
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Affiliation(s)
- Liangliang Xiang
- Faculty of Sports Science, Ningbo University, Ningbo, China; Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Yaodong Gu
- Faculty of Sports Science, Ningbo University, Ningbo, China; Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
| | - Zixiang Gao
- Faculty of Sports Science, Ningbo University, Ningbo, China; Faculty of Engineering, University of Pannonia, Veszprém, Hungary
| | - Peimin Yu
- Faculty of Sports Science, Ningbo University, Ningbo, China; Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand; Center for Medical Imaging, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Justin Fernandez
- Faculty of Sports Science, Ningbo University, Ningbo, China; Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand; Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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