Edge Deep Learning Enabled Freezing of Gait Detection in Parkinson's Patients

TL;DR

This paper introduces a wireless sensor network with deep learning for real-time detection of freezing of gait in Parkinson's patients, achieving high accuracy with low computational cost and ensuring data privacy.

Contribution

It presents a novel on-device deep learning model integrated into wearable sensors for accurate, privacy-preserving FoG detection in Parkinson's disease patients.

Findings

Achieved 88.8% sensitivity in FoG detection

F1 score of 85.34% with less than 20k parameters per sensor

System operates locally without external data streaming

Abstract

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.