Real-Time Detection of Parkinson’s States Based on Wearable Sensor With Embedded Machine Learning

This study presents a novel embedded wearable Parkinson’s states detection system. This system integrates data collection using inertial measurement unit (IMU), feature extraction, machine learning classification algorithm, and Parkinson’s states warning cue into a custom printed circuit board. Fifty Parkinson’s disease (PD) patients experiencing lower limb movement disturbance and 50 age matched healthy controls were enrolled in this study, with two IMU sensors attached to the outside of each ankle and asked to perform three tasks used in clinical to evaluate the severity of PD bradykinesia. In the first phase, the performance of seven state-of-the-art machine learning algorithms to classify lower limb movement symptoms from PD patients with healthy controls was evaluated. In the second phase, the support vector machine with radial basis function classifier demonstrated the best classification performance for the left heel tapping under the knee flexion state task, leading to its selection for microcontroller implantation. The proposed wearable Parkinson’s states detection system includes the microcontroller, IMU sensor, charging module, result indicator module, battery, power switch, and reset module. The total area of the printed circuit board is nearly $6.1\times 3.5$ cm, the whole detection device weighs 28.2 g, the feature extraction and model classification time needed is about 0.103 s, and the power consumption during the active mode is 0.195 W. The embedded machine learning on local microcontroller eliminates the need for data offloading or cloud-based interference, allowing for direct and timely inference of detection result for new subjects.