IoT-Based Smart System for Arthritis Pain Monitoring and Relief

Abstract: Arthritis and knee joint disorders are among the most prevalent musculoskeletal conditions globally, causing chronic pain, reduced mobility, and difficulty in daily activities. Conventional healthcare systems rely on periodic hospital visits, subjective pain assessments, and manual therapies, which fail to provide continuous monitoring or timely automated relief. This paper presents an IoT-Based Smart System for Arthritis Pain Monitoring and Relief that integrates wearable sensors, embedded processing, machine learning, and IoT cloud connectivity into a unified rehabilitation solution. The system employs an IMU accelerometer (MPU6050), temperature sensor (LM35), force sensor (FSR), and heart rate sensor (MAX30100) to continuously collect real-time physiological and motion data from the patient’s knee. An Arduino UNO microcontroller processes the sensor inputs, displays readings on a 16×2 LCD, and transmits data to the cloud via an ESP8266 Wi-Fi module. A Python-based Decision Tree classifier, trained on multi-parameter sensor data, predicts pain severity as Low, Medium, or High. Based on the predicted level, the system automatically activates a Peltier module to deliver heat therapy (medium pain) or cold therapy (high pain), while recommending exercise for low pain conditions. Experimental results demonstrate accurate pain classification and real-time therapy activation, offering a cost-effective, user-friendly, and intelligent solution for home-based arthritis rehabilitation.

Keywords: IoT, Arthritis Pain Monitoring, Machine Learning, Decision Tree, Peltier Module, Wearable Sensors, Smart Healthcare, Real-Time Therapy, Arduino, Knee Rehabilitation.