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  Published Paper Details:

  Paper Title

MEASURING STROKE REHABILITATION BY POSTURE DETECTION USING NEURAL NETWORKS

  Authors

  Mukhil Sundararaj Gowthaman,  Dhaarini Srinivasan,  Mrudhula Raya,  Swathi R

  Keywords

Artificial Intelligence, Stroke, Recurrent Neural Network, Sensors

  Abstract


More than 795 thousand people in the US experience a stroke, per year. Out of this, around 610 thousand are RST or new strokes. Strokes are a leading factor of mortality for Americans with 140 thousand people dying annually. [3] Stroke incidence has increased drastically in the young population, with over 20% of those affected being under the age of 45. Hemiparesis, which occurs following a stroke, is a significant motor impairment that affects up to 65 percent of stroke sufferers because of a high rate of muscle frailty. Muscle pain is a significant role in delayed recovery following a stroke in victims, and it is also a common symptom reported in patients. This can result in a patient's range of physical activities being limited or even immobilized. Between 30% and 60% of stroke victims report having less physical range in the damaged body portion when performing daily activities. The feasibility of a 4 by 4 excible sensor matrix which uses paper as a base is explored and a pressure sensor based on paper is developed. This helps reduce the cost and fabrication time. Additionally, the matrix's static and dynamic properties are discussed. Despite considerable variance in response between sensors due to the structural material qualities, the sensor may be efficiently used to continuously evaluate stroke rehabilitation patients' recovery. The matrix's performance is evaluated using a neural network that has been designed to recognize the location of a load using the values of individual sensors.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2512748

  Paper ID - 219062

  Page Number(s) - g675-g681

  Pubished in - Volume 13 | Issue 12 | December 2025

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Mukhil Sundararaj Gowthaman,  Dhaarini Srinivasan,  Mrudhula Raya,  Swathi R,   "MEASURING STROKE REHABILITATION BY POSTURE DETECTION USING NEURAL NETWORKS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 12, pp.g675-g681, December 2025, Available at :http://www.ijcrt.org/papers/IJCRT2512748.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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