Wireless Sensors with Edge Deep Learning for Detecting and Alerting the Freezing of Gait Symptoms in Parkinson's Patients*

This paper presents the design of a wireless sensor network for detecting and alerting the freezing of gait (FoG) symptoms in patients with Parkinson's disease. A novel button pin type sensor node design was developed for easy attachment. Three sensor nodes, each integrating a 3-axis accelerometer, can be placed on a patient at their ankle, thigh, and truck. Each sensor node can independently detect FoG using an on-device deep learning (DL) model, featuring a convolutional neural network (CNN). The DL model outputs from the three sensor nodes are processed in a central node using a majority voting algorithm. In a validation using a public dataset, the prototype developed achieved an FoG detection sensitivity of 88.8% and an F1 score of 85.34%, using less than 20k trainable parameters per sensor node. Once FoG is detected, an auditory signal will be generated to alert users, and the alarm signal will also be sent to mobile phones for further actions if needed. The sensor node can be easily recharged wirelessly by inductive coupling. The system is self-contained and processes all user data locally without streaming data to external devices or the cloud, thus eliminating the cybersecurity risks and power penalty associated with wireless data transmission. The developed methodology can be used in a wide range of applications.