  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. Three sensor nodes, each integrating a 3-axis accelerometer, can be
placed on a patient at ankle, thigh, and truck. Each sensor node can
independently detect FoG using an on-device deep learning (DL) model, featuring
a squeeze and excitation convolutional neural network (CNN). In a validation
using a public dataset, the prototype developed achieved a FoG detection
sensitivity of 88.8% and an F1 score of 85.34%, using less than 20 k 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.
