Multimodal Gait Recognition for Neurodegenerative Diseases

TL;DR

This paper introduces a multimodal gait recognition model that fuses data from multiple sensors to improve diagnosis and assessment of neurodegenerative diseases, especially Parkinson's disease.

Contribution

A novel hybrid neural network architecture combining spatial feature extraction, correlative memory, and multi-switch discrimination for enhanced gait pattern classification.

Findings

Outperforms existing methods in classification accuracy.

Effectively distinguishes between different neurodegenerative diseases.

Accurately assesses disease severity levels.

Abstract

In recent years, single modality based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognised that each of the established approaches has different strengths and weaknesses. As an important motor symptom, gait disturbance is usually used for diagnosis and evaluation of diseases; moreover, the use of multi-modality analysis of the patient's walking pattern compensates for the one-sidedness of single modality gait recognition methods that only learn gait changes in a single measurement dimension. The fusion of multiple measurement resources has demonstrated promising performance in the identification of gait patterns associated with individual diseases. In this paper, as a useful tool, we propose a novel hybrid model to learn the gait differences between three neurodegenerative diseases, between patients with different severity levels of Parkinson's disease and between healthy individuals and patients, by fusing and aggregating data from multiple sensors. A spatial feature extractor (SFE) is applied to generating representative features of images or signals. In order to capture temporal information from the two modality data, a new correlative memory neural network (CorrMNN) architecture is designed for extracting temporal features. Afterwards, we embed a multi-switch discriminator to associate the observations with individual state estimations. Compared with several state-of-the-art techniques, our proposed framework shows more accurate classification results.