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Rehabilitation after knee injury or surgery requires continuous monitoring, guided exercises, and controlled movement to ensure effective recovery. Conventional rehabilitation methods depend heavily on frequent hospital visits and direct supervision by physiotherapists, which can be time consuming and inconvenient for patients. This paper presents an IoT based Knee Tele Rehabilitation System designed to enable remote monitoring, assessment, and assisted control of knee rehabilitation exercises. The system employs an ESP32 microcontroller as the central control unit integrated with motion, pulse, oxygen saturation, and temperature sensors to track patient movement and physiological parameters in real time. A stepper motor mechanism supports motor assisted therapy, while visual and audio indicators provide immediate feedback to the patient. The system operates in manual and automatic modes through an Android application connected to a cloud server. In manual mode, patients follow prescribed exercise instructions, and if repeated unsuccessful attempts occur, the system switches to automatic mode to assist movement according to predefined parameters. The system also stops operation when abnormal physiological conditions are detected and resumes after stabilization. By integrating real time sensing, intelligent automation, and remote supervision, the proposed system improves safety, accuracy, and accessibility in knee rehabilitation while reducing dependence on in person clinical visits and supporting personalized recovery programs.
Keywords:
Internet of Things, tele rehabilitation, knee therapy, remote monitoring, physiotherapy automation
Cite Article:
"KNEEIoT – AUTOMATED KNEE MOVEMENT MONITORING USING IoT FOR TELEREHABILITATION", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.a675-a678, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605082.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator