Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach
Biomechanical analysis of the human lower limbs plays a critical role in movement assessment, injury prevention, and rehabilitation guidance. Traditional gait analysis techniques, such as optical motion capture systems and biomechanical force platforms, are limited by high costs, operational complex...
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| Format: | Article |
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PeerJ Inc.
2025-05-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2888.pdf |
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| author | Shu Xu Tao Wang Zenghui Ding Yu Wang Tongsheng Wan Dezhang Xu Xianjun Yang Ting Sun Meng Li |
| author_facet | Shu Xu Tao Wang Zenghui Ding Yu Wang Tongsheng Wan Dezhang Xu Xianjun Yang Ting Sun Meng Li |
| author_sort | Shu Xu |
| collection | DOAJ |
| description | Biomechanical analysis of the human lower limbs plays a critical role in movement assessment, injury prevention, and rehabilitation guidance. Traditional gait analysis techniques, such as optical motion capture systems and biomechanical force platforms, are limited by high costs, operational complexity, and restricted applicability. In view of this, this study proposes a cost-effective and user-friendly approach that integrates inertial measurement units (IMUs) with a novel deep learning framework for real-time lower limb joint torque estimation. The proposed method combines time-frequency domain analysis through continuous wavelet transform (CWT) with a hybrid architecture comprising multi-head self-attention (MHSA), bidirectional long short-term memory (Bi-LSTM), and a one-dimensional convolutional residual network (1D Conv ResNet). This integration enhances feature extraction, noise suppression, and temporal dependency modeling, particularly for non-stationary and nonlinear signals in dynamic environments. Experimental validation on public datasets demonstrates high accuracy, with a root mean square error (RMSE) of 0.16 N·m/kg, Coefficient of Determination (R2) of 0.91, and Pearson correlation coefficient of 0.95. Furthermore, the framework outperforms existing models in computational efficiency and real-time applicability, achieving a single-cycle inference time of 152.6 ms, suitable for portable biomechanical monitoring systems. |
| format | Article |
| id | doaj-art-3ae14fcb18c24fafbb47e52c9b43eb96 |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-3ae14fcb18c24fafbb47e52c9b43eb962025-08-20T02:00:13ZengPeerJ Inc.PeerJ Computer Science2376-59922025-05-0111e288810.7717/peerj-cs.2888Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approachShu Xu0Tao Wang1Zenghui Ding2Yu Wang3Tongsheng Wan4Dezhang Xu5Xianjun Yang6Ting Sun7Meng Li8Science Island Branch, Graduate School of USTC, University of Science and Technology of China, Hefei, Anhui, ChinaInstitute of Intelligent Machines, Chinese Academy of Sciences, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, ChinaInstitute of Intelligent Machines, Chinese Academy of Sciences, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, ChinaScience Island Branch, Graduate School of USTC, University of Science and Technology of China, Hefei, Anhui, ChinaScience Island Branch, Graduate School of USTC, University of Science and Technology of China, Hefei, Anhui, ChinaAnhui Key Laboratory of Advanced Numerical Control & Servo Technology (Cultivating Base), Wuhu, Anhui, ChinaInstitute of Intelligent Machines, Chinese Academy of Sciences, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, ChinaScience Island Branch, Graduate School of USTC, University of Science and Technology of China, Hefei, Anhui, ChinaThe Second Affiliated Hospital, Anhui University of Chinese Medicine, Hefei, Anhui, ChinaBiomechanical analysis of the human lower limbs plays a critical role in movement assessment, injury prevention, and rehabilitation guidance. Traditional gait analysis techniques, such as optical motion capture systems and biomechanical force platforms, are limited by high costs, operational complexity, and restricted applicability. In view of this, this study proposes a cost-effective and user-friendly approach that integrates inertial measurement units (IMUs) with a novel deep learning framework for real-time lower limb joint torque estimation. The proposed method combines time-frequency domain analysis through continuous wavelet transform (CWT) with a hybrid architecture comprising multi-head self-attention (MHSA), bidirectional long short-term memory (Bi-LSTM), and a one-dimensional convolutional residual network (1D Conv ResNet). This integration enhances feature extraction, noise suppression, and temporal dependency modeling, particularly for non-stationary and nonlinear signals in dynamic environments. Experimental validation on public datasets demonstrates high accuracy, with a root mean square error (RMSE) of 0.16 N·m/kg, Coefficient of Determination (R2) of 0.91, and Pearson correlation coefficient of 0.95. Furthermore, the framework outperforms existing models in computational efficiency and real-time applicability, achieving a single-cycle inference time of 152.6 ms, suitable for portable biomechanical monitoring systems.https://peerj.com/articles/cs-2888.pdfInertial measurement units (IMU)Lower limb joint torqueDeep learning approachBi-LSTMContinuous wavelet transform (CWT) |
| spellingShingle | Shu Xu Tao Wang Zenghui Ding Yu Wang Tongsheng Wan Dezhang Xu Xianjun Yang Ting Sun Meng Li Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach PeerJ Computer Science Inertial measurement units (IMU) Lower limb joint torque Deep learning approach Bi-LSTM Continuous wavelet transform (CWT) |
| title | Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach |
| title_full | Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach |
| title_fullStr | Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach |
| title_full_unstemmed | Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach |
| title_short | Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach |
| title_sort | estimation of lower limb torque a novel hybrid method based on continuous wavelet transform and deep learning approach |
| topic | Inertial measurement units (IMU) Lower limb joint torque Deep learning approach Bi-LSTM Continuous wavelet transform (CWT) |
| url | https://peerj.com/articles/cs-2888.pdf |
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