Unique Automated Lower Limb Design for Monoplegia Using Emg Sensor Signals
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-65392-6_27
Reference20 articles.
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2. Mokri, C., Bamdad, M., Abolghasemi, V.: Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques. Med. Biol. Eng. Comput. 60, 683–699 (2022)
3. Iqbal, N., et al.: Neuromechanical signal-based parallel and scalable model for lower limb movement recognition. IEEE Sens. J. 21(14), 16213–16221 (2021). https://doi.org/10.1109/jsen.2021.3076114
4. Vijayvargiya, A., Gupta, V., Kumar, R., Dey, N., Tavares, J.M.R.S.: A hybrid WD-EEMD SEMG feature extraction technique for lower limb activity recognition. IEEE Sens. J. 21(18), 20431–20439 (2021). https://doi.org/10.1109/jsen.2021.3095594
5. Wang, J., Dai, Y., Si, X.: Analysis and recognition of human lower limb motions based on electromyography (EMG) signals. Electronics 10(20), 2473 (2021). https://doi.org/10.3390/electronics10202473
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