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Open Access
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Chethan P. J., Aishwarya B. R., Amitha G. S., Ananya V. K., Anjali C. R.,
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- Assistant Professor, Student, Student, Student, Student Department of computer science and engineering, P.E.S. Institute of Technology and Management, Shivamogga, Virupina Koppa, Department of computer science and engineering, P.E.S. Institute of Technology and Management, Shivamogga, Virupina Koppa, Department of computer science and engineering, P.E.S. Institute of Technology and Management, Shivamogga, Virupina Koppa, Department of computer science and engineering, P.E.S. Institute of Technology and Management, Shivamogga, Virupina Koppa, Department of computer science and engineering, P.E.S. Institute of Technology and Management, Shivamogga, Virupina Koppa Karnataka, Karnataka, Karnataka, Karnataka, Karnataka India, India, India, India, India
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Abstract
nStroke is a major health concern worldwide, often resulting in impaired motor functions and affecting the quality of life for affected individuals. This research introduces an innovative approach to predicting stroke recovery rates by leveraging movement analysis during computer interaction. The proposed system aims to provide a non-invasive and automated solution to assess the rehabilitation progress of stroke survivors. The system utilizes advanced motion tracking technologies to capture and analyze the fine-grained movements exhibited during computer-based tasks. Machine learning algorithms are employed to correlate these movement patterns with established indicators of stroke recovery, allowing for personalized predictions tailored to individual patients. This approach offers a more dynamic and responsive assessment compared to traditional methods.
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Keywords: AIML, Computer vision, CNN, Rehabilitation, Movement Analysis.
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Computer Technology & Applications(jocta)]
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References
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[1] Kashi S, Polak RF, Lerner B, Rokach L, Levy-Tzedek S. A machine-learning model for automatic detection of movement compensations in stroke patients. IEEE Transactions on Emerging Topics in Computing. 2020 Apr 23;9(3):1234-47. [2] Kashi S, Polak RF, Lerner B, Rokach L, Levy-Tzedek S. A machine-learning model for automatic detection of movement compensations in stroke patients. IEEE Transactions on Emerging Topics in Computing. 2020 Apr 23;9(3):1234-47. [3] Hwang YT, Lu WA, Lin BS. Use of functional data to model the trajectory of an inertial measurement unit and classify levels of motor impairment for stroke patients. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2022 Mar 25;30:925-35. [4] Gomez-Arrunategui JP, Eng JJ, Hodgson AJ. Monitoring Arm Movements Post-Stroke for Applications in Rehabilitation and Home Settings. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2022 Aug 10;30:2312-21. [5] Guo N, Wang X, Duanmu D, Huang X, Li X, Fan Y, Li H, Liu Y, Yeung EH, To MK, Gu J. SSVEP-based brain computer interface controlled soft robotic glove for post-stroke hand function rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2022 Jun 22;30:1737-44. [6] Mak J, Kocanaogullari D, Huang X, Kersey J, Shih M, Grattan ES, Skidmore ER, Wittenberg GF, Ostadabbas S, Akcakaya M. Detection of stroke-induced visual neglect and target response prediction using augmented reality and electroencephalography. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2022 Jul 4;30:1840-50. [8] Teixeira-Salmela LF, Parreira VF, Britto RR, Brant TC, Inácio ÉP, Alcântara TO, Carvalho IF. Respiratory pressures and thoracoabdominal motion in community-dwelling chronic stroke survivors. Archives of physical medicine and rehabilitation. 2005 Oct 1;86(10):1974-8. [9] Bhatt A, Shah N, Horn M, Bhatt S, Yanushkevich S, Almekhlafi M. Assessment of Post-Stroke Motor Function Weakness using Pressure Sensor Data. In2021 IEEE Symposium Series on Computational Intelligence (SSCI) 2021 Dec 5 (pp. 1-7). IEEE. [10] Tang QP, Wang GQ, Huang XS, Yan ML, Han GD, Pu QQ, Ouyang CH, Zhan HL, Feng JH, Yang QD. The influence of different movements on ambulatory blood pressure in hypertensive subacute stroke patients. Journal of International Medical Research. 2012 Apr;40(2):590-600.
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| Volume | 15 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | May 3, 2024 | |
| Accepted | July 22, 2024 | |
| Published | August 1, 2024 |
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