A Low-Cost Wearable Gait Analyzer Using IMU Sensors and Machine Learning for Early Parkinson's Tremor Prediction: An Electrical Engineering Perspective

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting motor control, with early symptoms including subtle gait abnormalities and resting tremors. Early detection is crucial for timely intervention and disease management. This paper presents a low-cost, wearable gait analysis system from an electrical engineering perspective, utilizing an Inertial Measurement Unit (IMU) sensor with comprehensive signal conditioning and power management circuits, integrated with classical machine learning algorithms for early tremor prediction. The system employs adaptive power management techniques to ensure extended battery life while maintaining signal fidelity for accurate feature extraction. Designed with cost-effective commercial-off-the-shelf (COTS) components, the device incorporates proper signal conditioning, noise filtering, and wireless communication modules. Experimental validation shows classification accuracy exceeding 90% while achieving 48 hours of continuous operation on a single charge, demonstrating the system's efficacy as a non-invasive, power-efficient solution for early PD screening and long-term monitoring.