journal article·2025-07

Prediction of Self-Care Behaviors in Patients Using High-Density Surface Electromyography Signals and an Improved Whale Optimization Algorithm-Based LSTM Model

Shuai Huang, Dan Liu, Youfa Fu, Jiadui Chen, Ling He, Jing Yan, Di Yang

Journal of Bionic Engineering· Volume 22· Issue 4
Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Emotion and Mood Recognition · Context-Aware Activity Recognition Systems

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (56)

Show source-provided references
1. Owolabi, M. O., Thrift, A. G., Mahal, A., Ishida, M., Martins, S., Johnson, W. D., Pandian, J., Abd-Allah, F., Yaria, J., Phan, H. T., Roth, G., Gall, S. L., Beare, R., Phan, T. G., Mikulik, R., Akinyemi, R. O., Norrving, B., Brainin, M., & Feigin, V. L. (2022). Primary stroke prevention worldwide: Translating evidence into action. The Lancet Public Health,7, e74–e85. https://doi.org/10.1016/S2468-2667(21)00230-910.1016/S2468-2667(21)00230-9 ↗
2. Asowata, O. J., Okekunle, A. P., Olaiya, M. T., Akinyemi, J., Owolabi, M., & Akpa, O. M. (2024). Stroke risk prediction models: A systematic review and meta-analysis. Journal of the Neurological Sciences,460, 122997. https://doi.org/10.1016/j.jns.2024.12299710.1016/j.jns.2024.122997 ↗
3. Beebe, J. A., & Lang, C. E. (2009). Active range of motion predicts upper extremity function 3 months after stroke. Stroke,40, 1772–1779. https://doi.org/10.1161/strokeaha.108.53676310.1161/strokeaha.108.536763 ↗
4. Chen, W. Y., Li, G. Y., Li, N., Wang, W. X., Yu, P., Wang, R. Q., Xue, X. J., Zhao, X. G., & Liu, L. Q. (2022). Soft exoskeleton with fully actuated thumb movements for grasping assistance. IEEE Transactions on Robotics,38, 2194–2207. https://doi.org/10.1109/TRO.2022.314890910.1109/TRO.2022.3148909 ↗
5. Bagneschi, T., Chiaradia, D., Righi, G., Popolo, G. D., Frisoli, A., & Leonardis, D. (2023). A soft hand exoskeleton with a novel tendon layout to improve stable wearing in grasping assistance. IEEE Transactions on Haptics,16, 311–321. https://doi.org/10.1109/TOH.2023.327390810.1109/TOH.2023.3273908 ↗
6. Ishisaki, H., Tabata, K., Tsuji, T., & Watanabe, T. (2023). Hand grasping assist glove combining exoskeleton structure and pneumatically driven actuator. In: Proceedings of the 2023 IEEE/SICE International Symposium on System Integration (SII), Atlanta, USA, pp. 1–5.10.1109/SII55687.2023.10039272 ↗
7. Tang, Z. Q., Heung, H. L., Shi, X. Q., Tong, K. Y., & Li, Z. (2022). Probabilistic model-based learning control of a soft pneumatic glove for hand rehabilitation. IEEE Transactions on Biomedical Engineering,69, 1016–1028. https://doi.org/10.1109/TBME.2021.311189110.1109/TBME.2021.3111891 ↗
8. Feucht, M., Gupta, A., Sabau, D., Nowak, T., Coimbatore, R., Powley, T., & Ward, M. (2022). Non-invasive, spatiotemporal characterization of muscle activation patterns from vagus nerve stimulation in human subjects. The FASEB Journal. https://doi.org/10.1096/fasebj.2022.36.S1.R458710.1096/fasebj.2022.36.S1.R4587 ↗
9. Xu, L. K., Zhang, K. Q., Yang, G. K., & Chu, J. (2022). Gesture recognition using dual-stream CNN based on fusion of sEMG energy kernel phase portrait and IMU amplitude image. Biomedical Signal Processing and Control,73, 103364. https://doi.org/10.1016/j.bspc.2021.10336410.1016/j.bspc.2021.103364 ↗
10. Buongiorno, D., Cascarano, G. D., Feudis, I. D., Brunetti, A., Carnimeo, L., Dimauro, G., & Bevilacqu, V. (2021). Deep learning for processing electromyographic signals: A taxonomy-based survey. Neurocomputing,452, 549–565. https://doi.org/10.1016/j.neucom.2020.06.13910.1016/j.neucom.2020.06.139 ↗
11. Atzori, M., Gijsberts, A., Kuzborskij, I., Elsig, S., Mittaz Hager, A. G., Deriaz, O., Castellini, C., Müller, H., & Caputo, B. (2015). Characterization of a benchmark database for myoelectric movement classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering,23, 73–83. https://doi.org/10.1109/TNSRE.2014.232849510.1109/TNSRE.2014.2328495 ↗
12. Fatimah, B., Singh, P., Singhal, A., & Pachori, R. B. (2021). Hand movement recognition from sEMG signals using fourier decomposition method. Biocybernetics and Biomedical Engineering,41, 690–703. https://doi.org/10.1016/j.bbe.2021.03.00410.1016/j.bbe.2021.03.004 ↗
13. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software,95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.00810.1016/j.advengsoft.2016.01.008 ↗
14. Zhao, Z. M., Guo, W. C., Xu, Y., & Sheng, X. J. (2024). A biosignal quality assessment framework for high-density sEMG decomposition. Biomedical Signal Processing and Control,90, 105800. https://doi.org/10.1016/j.bspc.2023.10580010.1016/j.bspc.2023.105800 ↗
15. Wu, L., Zhang, X., Zhang, X., Chen, X., & Chen, X. (2021). Metric learning for novel motion rejection in high-density myoelectric pattern recognition. Knowledge-Based Systems,227, 107165. https://doi.org/10.1016/j.knosys.2021.10716510.1016/j.knosys.2021.107165 ↗
16. Clarke, A. K., Atashzar, S. F., Vecchio, A. D., Barsakcioglu, D., Muceli, S., Paul, B., Filip, U., Holobar, A., & Dario, F. (2021). Deep learning for robust decomposition of high-density surface EMG signals. IEEE Transactions on Biomedical Engineering,68, 526–534. https://doi.org/10.1109/TBME.2020.300650810.1109/TBME.2020.3006508 ↗
17. Chen, M. Q., & Zhou, P. (2024). 2CFastICA: A novel method for high density surface EMG decomposition based on kernel constrained fastICA and correlation constrained fastICA. IEEE Transactions on Neural Systems and Rehabilitation Engineering,32, 2177–2186. https://doi.org/10.1109/TNSRE.2024.339882210.1109/TNSRE.2024.3398822 ↗
18. Massa, S. M., Riboni, D., & Nazarpour, K. (2024). Explainable ai-powered graph neural networks for hd EMG-based gesture intention recognition. IEEE Transactions on Consumer Electronics,70, 4499–4506. https://doi.org/10.1109/TCE.2023.333342110.1109/TCE.2023.3333421 ↗
19. Wang, Z. T., Li, G., & Yang, L. (2021). Dynamic hand gesture recognition based on micro-doppler radar signatures using hidden gauss–markov models. IEEE Geoscience and Remote Sensing Letters,18, 291–295. https://doi.org/10.1109/LGRS.2020.297482110.1109/LGRS.2020.2974821 ↗
20. Jiang, X. Y., Meng, L., Liu, X. Y., Fan, J. H., Ye, X. M., Dai, C. Y., & Chen, W. (2023). Optimizing the cross-day performance of electromyogram biometric decoder. IEEE Internet of Things Journal,10, 4388–4402. https://doi.org/10.1109/JIOT.2022.321873910.1109/JIOT.2022.3218739 ↗
21. Zhang, Z., Yang, K., Qian, J., & Zhang, L. (2019). Real-time surface EMG pattern recognition for hand gestures based on an artificial neural network. Sensors,19(14), 3170. https://doi.org/10.3390/s1914317010.3390/s19143170 ↗
22. Li, G. F., Tang, H., Sun, Y., Kong, J. Y., Jiang, G. Z., Jiang, D., Tao, B., Xu, S., & Liu, H. H. (2019). Hand gesture recognition based on convolution neural network. Cluster Computing,22, 2719–2729. https://doi.org/10.1007/s10586-017-1435-x10.1007/s10586-017-1435-x ↗
23. Fuentes-Alvarez, R., Hernandez, J. H., Matehuala-Moran, I., Alfaro-Ponce, M., Lopez-Gutierrez, M., Salazar, S., & Lozano, R. (2022). Assistive robotic exoskeleton using recurrent neural networks for decision taking for the robust trajectory tracking. Expert Systems with Applications,193, 116482. https://doi.org/10.1016/j.eswa.2021.11648210.1016/j.eswa.2021.116482 ↗
24. Atzori, M., Cognolato, M., & Müller, H. (2016). Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands. Frontiers in Neurorobotics. https://doi.org/10.3389/fnbot.2016.0000910.3389/fnbot.2016.00009 ↗
25. Too, J., Abdullah, A. R., Mohd Saad, N., Mohd Ali, N., & Tengku Zawawi, T. N. S. (2018). Deep convolutional neural network for featureless electromyogram pattern recognition using time-frequency distribution. Sensor Letters,16, 92–99. https://doi.org/10.1166/sl.2018.392610.1166/sl.2018.3926 ↗
26. Zhang, L. J., & Zeng, X. Q. (2019). Research on gesture recognition of sEMG based on deep neural network. Computer Engineering and Applications,55, 113–119. https://doi.org/10.3778/j.issn.1002-8331.1902-021010.3778/j.issn.1002-8331.1902-0210 ↗
27. Nasri, N., Orts-Escolano, S., Gomez-Donoso, F., & Cazorla, M. (2019). Inferring static hand poses from a low-cost non-intrusive sEMG sensor. Sensors,19(2), 371. https://doi.org/10.3390/s1902037110.3390/s19020371 ↗
28. Shen, S., Li, M. L., Mao, F., Chen, X. R., & Ran, R. (2024). Gesture recognition using mlp-mixer with cnn and stacking ensemble for sEMG signals. IEEE Sensors Journal,24, 4960–4968. https://doi.org/10.1109/JSEN.2023.334752910.1109/JSEN.2023.3347529 ↗
29. Hochreiter, S. (1998). The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems,06, 107–116. https://doi.org/10.1142/S021848859800009410.1142/S0218488598000094 ↗
30. Samadani, A. (2018). Gated recurrent neural networks for EMG-based hand gesture classification. A comparative study. In: Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, USA, pp. 1–4.10.1109/EMBC.2018.8512531 ↗
31. Yu, C. L., Fan, S. R., Liu, Y., & Shu, Y. B. (2022). End-side gesture recognition method for UAV control. IEEE Sensors Journal,22, 24526–24540. https://doi.org/10.1109/JSEN.2022.321882910.1109/JSEN.2022.3218829 ↗
32. Li, J., Wei, L. X., Wen, Y. T., Liu, X. G., & Wang, H. R. (2023). An approach to continuous hand movement recognition using sEMG based on features fusion. The Visual Computer,39, 2065–2079. https://doi.org/10.1007/s00371-022-02465-710.1007/s00371-022-02465-7 ↗
33. Li, C., Xie, C. Y., Zhang, B. C., Chen, C., & Han, J. G. (2018). Deep fisher discriminant learning for mobile hand gesture recognition. Pattern Recognition,77, 276–288. https://doi.org/10.1016/j.patcog.2017.12.02310.1016/j.patcog.2017.12.023 ↗
34. Zhong, C. T., Li, G., Meng, Z., Li, H. J., Yildiz, A. R., & Mirjalili, S. (2025). Starfish optimization algorithm (SFOA): A bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers. Neural Computing and Applications,37, 3641–3683. https://doi.org/10.1007/s00521-024-10694-110.1007/s00521-024-10694-1 ↗
35. Fu, Y. F., Liu, D., Chen, J. D., & He, L. (2024). Secretary bird optimization algorithm: A new metaheuristic for solving global optimization problems. Artificial Intelligence Review,57, 123. https://doi.org/10.1007/s10462-024-10729-y10.1007/s10462-024-10729-y ↗
36. Yıldız, B. S., Mehta, P., Panagant, N., Mirjalili, S., & Yildiz, A. R. (2022). A novel chaotic Runge Kutta optimization algorithm for solving constrained engineering problems. Journal of Computational Design and Engineering,9, 2452–2465. https://doi.org/10.1093/jcde/qwac11310.1093/jcde/qwac113 ↗
37. Abderazek, H., Yildiz, A. R., & Sait, S. M. (2019). Mechanical engineering design optimisation using novel adaptive differential evolution algorithm. International Journal of Vehicle Design,80, 285–329. https://doi.org/10.1504/IJVD.2019.10987310.1504/IJVD.2019.109873 ↗
38. Mehta, P., Yildiz, B. S., Sait, S. M., & Yildiz, A. R. (2022). Hunger games search algorithm for global optimization of engineering design problems. Materials Testing,64, 524–532. https://doi.org/10.1515/mt-2022-001310.1515/mt-2022-0013 ↗
39. Yıldız, B. S., Kumar, S., Panagant, N., Mehta, P., Sait, S. M., Yildiz, A. R., Pholdee, N., Bureerat, S., & Mirjalili, S. (2023). A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems. Knowledge-Based Systems,271, 110554. https://doi.org/10.1016/j.knosys.2023.11055410.1016/j.knosys.2023.110554 ↗
40. Kunakote, T., Sabangban, N., Kumar, S., Tejani, G. G., Panagant, N., Pholdee, N., Bureerat, S., & Yildiz, A. R. (2022). Comparative performance of twelve metaheuristics for wind farm layout optimisation. Archives of Computational Methods in Engineering,29, 717–730. https://doi.org/10.1007/s11831-021-09586-710.1007/s11831-021-09586-7 ↗
41. Kumar, S., Yıldız, B. S., Mehta, P., Panagant, N., Sait, S. M., Mirjalili, S., & Yildiz, A. R. (2023). Chaotic marine predators algorithm for global optimization of real-world engineering problems. Knowledge-Based Systems,261, 110192. https://doi.org/10.1016/j.knosys.2022.11019210.1016/j.knosys.2022.110192 ↗
42. Meng, Z., Qian, Q. C., Xu, M. Q., Yu, B., Yıldız, A. R., & Mirjalili, S. (2023). PINN-FORM: A new physics-informed neural network for reliability analysis with partial differential equation. Computer Methods in Applied Mechanics and Engineering,414, 116172. https://doi.org/10.1016/j.cma.2023.11617210.1016/j.cma.2023.116172 ↗
43. Karaduman, A., Yıldız, B. S., & Yıldız, A. R. (2019). Experimental and numerical fatigue-based design optimisation of clutch diaphragm spring in the automotive industry. International Journal of Vehicle Design,80, 330–345. https://doi.org/10.1504/IJVD.2019.10987510.1504/IJVD.2019.109875 ↗
44. Yildiz, B. S. (2024). Enhancing the performance of a additive manufactured battery holder using a coupled artificial neural network with a hybrid flood algorithm and water wave algorithm. Materials Testing,66, 1557–1563. https://doi.org/10.1515/mt-2024-021710.1515/mt-2024-0217 ↗
45. Sait, S. M., Mehta, P., Gürses, D., & Yildiz, A. R. (2023). Cheetah optimization algorithm for optimum design of heat exchangers. Materials Testing,65, 1230–1236. https://doi.org/10.1515/mt-2023-001510.1515/mt-2023-0015 ↗
46. Gürses, D., Mehta, P., Sait, S. M., & Yildiz, A. R. (2022). African vultures optimization algorithm for optimization of shell and tube heat exchangers. Materials Testing,64, 1234–1241. https://doi.org/10.1515/mt-2022-005010.1515/mt-2022-0050 ↗
47. Anosri, S., Panagant, N., Champasak, P., Bureerat, S., Thipyopas, C., Kumar, S., Panagant, N., Yildiz, B. S., & Yıldız, A. R. (2023). A comparative study of state-of-the-art metaheuristics for solving many-objective optimization problems of fixed wing unmanned aerial vehicle conceptual design. Archives of Computational Methods in Engineering,30, 3657–3671. https://doi.org/10.1007/s11831-023-09914-z10.1007/s11831-023-09914-z ↗
48. Meng, Z., Yıldız, B. S., Li, G., Zhong, C., Mirjalili, S., & Yildiz, A. R. (2023). Application of state-of-the-art multiobjective metaheuristic algorithms in reliability-based design optimization: A comparative study. Structural and Multidisciplinary Optimization,66, 191. https://doi.org/10.1007/s00158-023-03639-010.1007/s00158-023-03639-0 ↗
49. Abderazek, H., Sait, S. M., & Yildiz, A. R. (2019). Optimal design of planetary gear train for automotive transmissions using advanced meta-heuristics. International Journal of Vehicle Design,80, 121–136. https://doi.org/10.1504/IJVD.2019.10986210.1504/IJVD.2019.109862 ↗
50. Tserkis, S., Assad, S. M., Koy Lam, P., & Narang, P. (2025). Quantifying total correlations in quantum systems through the pearson correlation coefficient. Physics Letters A. https://doi.org/10.1016/j.physleta.2025.13043210.1016/j.physleta.2025.130432 ↗
51. Yuval-Greenberg, S., & Deouell, L. Y. (2009). The broadband-transient induced gamma-band response in scalp EEG reflects the execution of saccades. Brain Topography,22, 3–6. https://doi.org/10.1007/s10548-009-0077-610.1007/s10548-009-0077-6 ↗
52. Nadimi-Shahraki, M. H., Zamani, H., & Mirjalili, S. (2022). Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study. Computers in Biology and Medicine,148, 105858. https://doi.org/10.1016/j.compbiomed.2022.10585810.1016/j.compbiomed.2022.105858 ↗
53. Qing, X. Y., & Niu, Y. G. (2018). Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy,148, 461–468. https://doi.org/10.1016/j.energy.2018.01.17710.1016/j.energy.2018.01.177 ↗
54. Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Proceedings of the MHS'95. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43.10.1109/MHS.1995.494215 ↗
55. Storn, R., & Price, K. (1997). Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization,11, 341–359. https://doi.org/10.1023/A:100820282132810.1023/A:1008202821328 ↗
56. Trojovský, P., & Dehghani, M. (2022). Pelican optimization algorithm: a novel nature-inspired algorithm for engineering applications. Sensors.,22(3), 855. https://doi.org/10.3390/s2203085510.3390/s22030855 ↗