# Parkinson’s Disease Detection via Bilateral Gait Camera Sensor Fusion Using CMSA-Net and Implementation on Portable Device

## TL;DR

This paper introduces a new method for detecting Parkinson’s disease using gait analysis with a camera-based system and a novel neural network, implemented in a portable device.

## Contribution

The paper introduces CMSA-Net, a novel fusion network with a new loss function, and a portable device for PD detection using gait video features.

## Key findings

- The proposed CMSA-Net with MMD loss achieved 89.10% accuracy and 81.11% F1-score on a hospital-collected gait dataset.
- The portable device supports multiple operating modes for use in residential and elder care settings.
- The method outperformed existing approaches in PD detection performance.

## Abstract

The annual increase in the incidence of Parkinson’s disease (PD) underscores the critical need for effective detection methods and devices. Gait video features based on camera sensors, as a crucial biomarker for PD, are well-suited for detection and show promise for the development of portable devices. Consequently, we developed a single-step segmentation method based on Savitzky–Golay (SG) filtering and a sliding window peak selection function, along with a Cross-Attention Fusion with Mamba-2 and Self-Attention Network (CMSA-Net). Additionally, we introduced a loss function based on Maximum Mean Discrepancy (MMD) to further enhance the fusion process. We evaluated our method on a dual-view gait video dataset that we collected in collaboration with a hospital, comprising 304 healthy control (HC) samples and 84 PD samples, achieving an accuracy of 89.10% and an F1-score of 81.11%, thereby attaining the best detection performance compared with other methods. Based on these methodologies, we designed a simple and user-friendly portable PD detection device. The device is equipped with various operating modes—including single-view, dual-view, and prior information correction—which enable it to adapt to diverse environments, such as residential and elder care settings, thereby demonstrating strong practical applicability.