Combining Gyroscope and Electromyogram Analysis for the Detection of Resting Tremor and Muscle Activity in Parkinson's Disease

In this paper, we focus on designing and implementing a wearable device for detecting Parkinson's disease (PD) symptoms by analyzing resting tremors and abnormal muscle activity which contribute to PD combining gyroscope and electromyogram(EMG) analysis. Using advanced sensor technology, real-time data about movement and muscle activity is captured by the device. Here, we outline a hardware framework for optimizing data acquisition by identifying sensors to be used, their placement and integration strategies. In order to analyze data, machine learning algorithms are used to distinguish between tremors and muscle activity that are specific to Parkinson's disease and normal movements using classification technique. By enabling proactive healthcare interventions and customized patient management strategies, the proposed device represents a promising tool for the detection of early-stage Parkinson's disease.