Telemedicine Based Neuro- rehabilitation System with AI-Assisted Remote Monitoring and Therapy Support

Abstract

Neuro- recuperation requires nonstop monitoring and timely remedial feedback to ameliorate patient recovery, which is frequently limited by geographical walls and lack of frequent clinicalsupervision. Toaddressthischallenge,thiswork proposes a Telemedicine- grounded neuron- recuperation system for remote monitoring and remedy backing integrated with an artificialintelligence- drivendecision-making model. Thesystem employsapiezoelectricdetectortocapturemusclemovementand recuperation exertion signals from cases performing remedy exercises at home. These analog signals are digitized using an ADC and reused by a NodeMCU ESP8266 regulator, which transmits the data securely to a pall garçon through an IoT frame. A web- grounded Telemedicine platform enables real-time visualization of patient data for both cases and healthcare professionals. To enhance clinical decision support, a K- Nearest Neighbors(KNN) machine literacy algorithm is enforced on the pall to dissect uprooted features and prognosticate the case’s recuperationstatus, distributed as normal, perfecting, orcritical. Grounded on the prognosticated condition, the system provides original feedback through an TV and vibration motor, aiding guided remedy. Remote croakers can cover progress, assess recovery trends, and recommend remedy adaptations without physical presence. The proposed system improves availability, reduces sanitarium visits, and supports data- driven individualized neuro- recuperation, making it suitable for cost- effective and scalable remote healthcare operations.