Open Access
Issue |
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
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Article Number | 02025 | |
Number of page(s) | 9 | |
Section | Machine Learning in Healthcare and Finance | |
DOI | https://doi.org/10.1051/itmconf/20257002025 | |
Published online | 23 January 2025 |
- J. Schröder, T. Van Criekinge, E. Embrechts, X. Celis, J. Van Schuppen, S. Truijen, W. Saeys, Combining the benefits of tele-rehabilitation and virtual reality-based balance training: a systematic review on feasibility and effectiveness. Disability and Rehabilitation: Assistive Technology, 14, 2-11 (2019). [CrossRef] [Google Scholar]
- I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, I. Chouvarda, Machine learning and data mining methods in diabetes research. Computational and Structural Biotechnology Journal, 15, 104-116 (2017). [CrossRef] [Google Scholar]
- R. Rouhi, M. Jafari, S. Kasaei, P. Keshavarzian, Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Systems with Applications, 42, 990-1002 (2015). [CrossRef] [Google Scholar]
- L. Lu, Y. Tan, M. Klaic, M. P. Galea, F. Khan, A. Oliver, E. Zhao, Evaluating rehabilitation progress using motion features identified by machine learning. IEEE Transactions on Biomedical Engineering, 68, 1417-1428 (2020). [Google Scholar]
- O. Oyebode, J. Fowles, D. Steeves, R. Orji, Machine learning techniques in adaptive and personalized systems for health and wellness. International Journal of Human-Computer Interaction, 39, 1938-1962 (2023). [CrossRef] [Google Scholar]
- Z. Wei, Z. Q. Zhang, S. Q. Xie, Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2024). [Google Scholar]
- Y. Qiu, J. Wang, Z. Jin, H. Chen, M. Zhang, L. Guo, Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training. Biomedical Signal Processing and Control, 72, 103323 (2022). [CrossRef] [Google Scholar]
- S. Deb, M. F. Islam, S. Rahman, S. Rahman, Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 410-419 (2022). [CrossRef] [Google Scholar]
- Y. Liao, A. Vakanski, M. Xian, A deep learning framework for assessing physical rehabilitation exercises. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28, 468-477 (2020). [CrossRef] [Google Scholar]
- P. Levinger, D. T. Lai, K. Webster, R. K. Begg, J. Feller, Support Vector Machines for detecting recovery from knee replacement surgery using quantitative gait measures. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 4875–4878 (2007). [Google Scholar]
- V. Venkataraman, P. Turaga, N. Lehrer, M. Baran, T. Rikakis, S. L. Wolf, Decision support for stroke rehabilitation therapy via describable attribute-based decision trees. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3154–3159 (2014). [Google Scholar]
- N. S. H. Salim, N. Z. Azlan, H. I. Hassan, A. N. Nordin, S. Hosen, Full hand pose recognition in performing daily activities for tele-rehabilitation based on decision tree algorithm. Mekatronika: Journal of Intelligent Manufacturing and Mechatronics, 6, 8191 (2024). [Google Scholar]
- S. Ren, W. Wang, Z. G. Hou, B. Chen, X. Liang, J. Wang, L. Peng, Personalized gait trajectory generation based on anthropometric features using random forest. Journal of Ambient Intelligence and Humanized Computing, 1–12 (2023). [Google Scholar]
- Y. Gloumakov, A. J. Spiers, A. M. Dollar, Dimensionality reduction and motion clustering during activities of daily living: Three-, four-, and seven-degree-of-freedom arm movements. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28, 2826-2836 (2020). [CrossRef] [Google Scholar]
- Y. Gloumakov, A. J. Spiers, A. M. Dollar, Dimensionality reduction and motion clustering during activities of daily living: Decoupling hand location and orientation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28, 2955-2965 (2020). [CrossRef] [Google Scholar]
- A. E. F. Da Gama, T. de Menezes Chaves, P. Fallavollita, L. S. Figueiredo, V. Teichrieb, Rehabilitation motion recognition based on the international biomechanical standards. Expert Systems with Applications, 116, 396-409 (2019). [CrossRef] [Google Scholar]
- Z. Yang, M. H. Rafiei, A. Hall, C. Thomas, H. A. Midtlien, A. Hasselbach, L. V. Gauthier, A novel methodology for extracting and evaluating therapeutic movements in game-based motion capture rehabilitation systems. Journal of Medical Systems, 42, 114 (2018). [CrossRef] [MathSciNet] [Google Scholar]
- G. N. Pradhan, B. Prabhakaran, Clustering of human motions based on feature-level fusion of multiple body sensor data. In Proceedings of the 1st ACM International Health Informatics Symposium, 66–75 (2010). [CrossRef] [Google Scholar]
- J. F. S. Lin, D. Kulić, Online segmentation of human motion for automated rehabilitation exercise analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22, 168-180 (2013). [Google Scholar]
- D. Boe, A. A. Portnova-Fahreeva, A. Sharma, V. Rai, A. Sie, P. Preechayasomboon, E. Rombokas, Dimensionality reduction of human gait for prosthetic control. Frontiers in Bioengineering and Biotechnology, 9, 724626 (2021). [CrossRef] [Google Scholar]
- Y. Liao, A. Vakanski, M. Xian, D. Paul, R. Baker, A review of computational approaches for evaluation of rehabilitation exercises. Computers in Biology and Medicine, 119, 103687 (2020). [CrossRef] [Google Scholar]
- L. Tao, A. Paiement, D. Damen, M. Mirmehdi, S. Hannuna, M. Camplani, I. Craddock, A comparative study of pose representation and dynamics modelling for online motion quality assessment. Computer Vision and Image Understanding, 148, 136-152 (2016). [CrossRef] [Google Scholar]
- F. Sardari, A. Paiement, M. Mirmehdi, View-invariant pose analysis for human movement assessment from RGB data. In Image Analysis and Processing-ICIAP 2019: 20th International Conference, Trento, Italy, 237–248 (2019). [Google Scholar]
- D. Tang, Hybridized hierarchical deep convolutional neural network for sports rehabilitation exercises. IEEE Access, 8, 118969-118977 (2020). [Google Scholar]
- K. S. Lee, S. Chae, H. S. Park, Optimal time-window derivation for human-activity recognition based on convolutional neural networks of repeated rehabilitation motions. In 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), 583586 (2019). [Google Scholar]
- M. Panwar, D. Biswas, H. Bajaj, M. Jöbges, R. Turk, K. Maharatna, A. Acharyya, Rehab-net: Deep learning framework for arm movement classification using wearable sensors for stroke rehabilitation. IEEE Transactions on Biomedical Engineering, 66, 3026-3037 (2019). [CrossRef] [Google Scholar]
- Z. A. Zhu, Y. C. Lu, C. H. You, C. K. Chiang, Deep learning for sensor-based rehabilitation exercise recognition and evaluation. Sensors, 19, 887 (2019). [CrossRef] [Google Scholar]
- G. Xu, A. Song, H. Li, Adaptive impedance control for upper-limb rehabilitation robot using evolutionary dynamic recurrent fuzzy neural network. Journal of Intelligent & Robotic Systems, 62, 501-525 (2011). [CrossRef] [Google Scholar]
- M. Ghislieri, G. L. Cerone, M. Knaflitz, V. Agostini, Long short-term memory (LSTM) recurrent neural network for muscle activity detection. Journal of NeuroEngineering and Rehabilitation, 18, 1-15 (2021). [CrossRef] [Google Scholar]
- Z. Zhou, B. Liang, G. Huang, B. Liu, J. Nong, L. Xie, Individualized gait generation for rehabilitation robots based on recurrent neural networks. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 273-281 (2020). [Google Scholar]
- M. Liu, B. Peng, M. Shang, Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network. Complex & Intelligent Systems, 8, 2813-2824 (2022). [CrossRef] [Google Scholar]
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