Wi-Fi-Based Human Activity Recognition for Continuous, Whole-Room Monitoring of Motor Functions in Parkinson's Disease

Abstract

Parkinson's disease is a progressive neurodegenerative disorder with significant fluctuations throughout the day, making accurate drug treatment difficult. A home-based long-term monitoring system is essential to address this challenge. Contemporary approaches to activity monitoring have focused on wearable devices and computer vision systems. Wearable devices are often uncomfortable and not ideal for long-term monitoring, while computer vision is plagued with significant privacy concerns. In this context, Wi-Fi sensing presents itself as an advantageous alternative due to its non-invasive and privacy-preserving properties. However, current human activity recognition methodologies lack the specificity to identify disease-related symptoms within everyday activities. Furthermore, the efficiency of human activity recognition methods in processing continuous data streams in real time is a crucial aspect that needs thorough assessment. This study proposes a novel approach for human activity recognition using Wi-Fi signals. Traditional methods for signal processing are avoided by converting the ratio of channel state information from antenna pairs into images. These images are then processed using a convolutional neural network to detect movements related to diseases in a large dataset. The experiments utilize a laptop PC with Intel Wi-Fi Link 5300 and a receiver equipped with three external 12 dB omnidirectional antennas in the 2.4 GHz band and cover various daily activities. The proposed method has demonstrated remarkable accuracy, with an average recognition rate of 93.8% in validation. It also showcased a consistent accuracy range of 91.9% to 95.2% in generalization tests, proving its effectiveness in different environments, with various individuals, and under assorted Wi-Fi configurations. A performance test of our method revealed that it processes raw CSI to recognition results in just 0.65 seconds per second of data, highlighting its potential for real-time applications.