Intelligent assistive robot design based on big data analysis and biomechanical analysis
Abstract
To improve the training effectiveness of rehabilitation training for patients with lower limb injuries, the research optimized the long short-term memory network algorithm using convolutional neural network algorithm, and conducted big data analysis on the biomechanics of the human lower limb based on the optimized algorithm. Through the results of big data analysis, the mechanical response mechanism of the human lower limb during movement was studied, and a rehabilitation training intelligent assistive robot that aligns more closely with the biomechanical properties of the human body was designed. An analysis of the biomechanics of the lower limbs of the human body showed that under different exercise states, the muscle strength of the gastrocnemius and soleus muscles in the lower limbs showed similar trends, with the gluteus maximus muscle strength reaching its maximum value in the first 20% of the gait cycle. After optimizing the intelligent assistive robot based on this result, the weekly training efficiency of patients increased to 92.3%. From the above results, it can be concluded that the proposed intelligent assistive robot can significantly improve the rehabilitation training efficiency of patients with lower limb injuries.
References
1. Alshingiti Z, Alaqel R, Al-Muhtadi J, et al. A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN. Electronics. 2023; 12(1): 232. doi: 10.3390/electronics12010232
2. Augustin VA, Son HS, Baur I, et al. Detecting subclinical keratoconus by biomechanical analysis in tomographically regular keratoconus fellow eyes. European Journal of Ophthalmology. 2021; 32(2): 815-822. doi: 10.1177/11206721211063740
3. Cai L, Liu Y, Wei Z, et al. Robot‐assisted rehabilitation training improves knee function and daily activity ability in older adults following total knee arthroplasty. Research in Nursing & Health. 2022; 46(2): 203-209. doi: 10.1002/nur.22290
4. Chen C, Seo H, Zhao Y. A novel pavement transverse cracks detection model using WT-CNN and STFT-CNN for smartphone data analysis. International Journal of Pavement Engineering. 2021; 23(12): 4372-4384. doi: 10.1080/10298436.2021.1945056
5. Elshazly TM, Bourauel C, Aldesoki M, et al. Computer-aided finite element model for biomechanical analysis of orthodontic aligners. Clinical Oral Investigations. 2022; 27(1): 115-124. doi: 10.1007/s00784-022-04692-7
6. Fanelli S, Pratici L, Salvatore FP, et al. Big data analysis for decision-making processes: challenges and opportunities for the management of health-care organizations. Management Research Review. 2022; 46(3): 369-389. doi: 10.1108/mrr-09-2021-0648
7. Hughes G, Dai B. The influence of decision making and divided attention on lower limb biomechanics associated with anterior cruciate ligament injury: a narrative review. Sports Biomechanics. 2021; 22(1): 30-45. doi: 10.1080/14763141.2021.1898671
8. Kumar R, Kumar P, Kumar Y. Integrating big data driven sentiments polarity and ABC-optimized LSTM for time series forecasting. Multimedia Tools and Applications. 2021; 81(24): 34595-34614. doi: 10.1007/s11042-021-11029-1
9. Lloyd DG, Jonkers I, Delp SL, et al. The History and Future of Neuromusculoskeletal Biomechanics. Journal of Applied Biomechanics. 2023; 39(5): 273-283. doi: 10.1123/jab.2023-0165
10. Loro A, Borg MB, Battaglia M, et al. Balance Rehabilitation through Robot-Assisted Gait Training in Post-Stroke Patients: A Systematic Review and Meta-Analysis. Brain Sciences. 2023; 13(1): 92. doi: 10.3390/brainsci13010092
11. Madan A, Kumar D. CNN-Based Models for Emotion and Sentiment Analysis Using Speech Data. ACM Transactions on Asian and Low-Resource Language Information Processing. 2024; 23(10): 1-24. doi: 10.1145/3687303
12. Mercan N, Yıldırım A, Dere Y. Biomechanical Analysis of Tibiofibular Syndesmosis Injury Fixation Methods: A Finite Element Analysis. The Journal of Foot and Ankle Surgery. 2023; 62(1): 107-114. doi: 10.1053/j.jfas.2022.05.007
13. Ouendi N, Hubaut R, Pelayo S, et al. The rehabilitation robot: factors influencing its use, advantages and limitations in clinical rehabilitation. Disability and Rehabilitation: Assistive Technology. 2022; 19(3): 546-557. doi: 10.1080/17483107.2022.2107095
14. Pastor T, Zderic I, van Knegsel KP, et al. Biomechanical analysis of helical versus straight plating of proximal third humeral shaft fractures. Archives of Orthopaedic and Trauma Surgery. 2023; 143(8): 4983-4991. doi: 10.1007/s00402-023-04814-0
15. Pustokhina IV, Pustokhin DA, Lydia EL, et al. Hyperparameter search based convolution neural network with Bi-LSTM model for intrusion detection system in multimedia big data environment. Multimedia Tools and Applications. 2021; 81(24): 34951-34968. doi: 10.1007/s11042-021-11271-7
16. Sun P, Shan R, Wang S. An Intelligent Rehabilitation Robot With Passive and Active Direct Switching Training: Improving Intelligence and Security of Human–Robot Interaction Systems. IEEE Robotics & Automation Magazine. 2023; 30(1): 72-83. doi: 10.1109/mra.2022.3228490
17. Tao Y, Luo J, Tian J, et al. The role of robot-assisted training on rehabilitation outcomes in Parkinson’s disease: a systematic review and meta-analysis. Disability and Rehabilitation. 2023; 46(18): 4049-4067. doi: 10.1080/09638288.2023.2266178
18. Tsai TC, Chiang MH. A Lower Limb Rehabilitation Assistance Training Robot System Driven by an Innovative Pneumatic Artificial Muscle System. Soft Robotics. 2023; 10(1): 1-16. doi: 10.1089/soro.2020.0216
19. Putra Utama AB, Wibawa AP, Muladi M, et al. PSO based Hyperparameter tuning of CNN Multivariate Time- Series Analysis. Jurnal Online Informatika. 2022; 7(2): 193-202. doi: 10.15575/join.v7i2.858
20. Wang C, Peng L, Hou ZG. A Control Framework for Adaptation of Training Task and Robotic Assistance for Promoting Motor Learning With an Upper Limb Rehabilitation Robot. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2022; 52(12): 7737-7747. doi: 10.1109/tsmc.2022.3163916
21. Zhao CG, Ju F, Sun W, et al. Effects of Training with a Brain–Computer Interface-Controlled Robot on Rehabilitation Outcome in Patients with Subacute Stroke: A Randomized Controlled Trial. Neurology and Therapy. 2022; 11(2): 679-695. doi: 10.1007/s40120-022-00333-z
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