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 recognized 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 multimodality 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 article, 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. Afterward, we embed a multiswitch 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.

Highlights

  • N EURODEGENERATIVE diseases (NDDs) are described as disorders with selective loss of neurons and distinct involvement of functional systems

  • Gaits of patients with different severity degrees of NDDs are of various appearances that can be quantified by the unified Parkinson’s disease rating scale (UPDRS), unified Huntington’s disease rating scale (UHDRS), and Hoehn & Yahr rating scale (H & Y) [2, 5,6,7]

  • We describe the architecture of the correlative memory neural network (CorrMNN) for temporal feature extraction, followed by detailed discussion on the individual components of CorrMNN

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Summary

Introduction

N EURODEGENERATIVE diseases (NDDs) are described as disorders with selective loss of neurons and distinct involvement of functional systems. Since flexion and extension motions of two lower limbs are regulated by the central nervous system, the gait of a patient with a neurodegenerative disorder would become abnormal due to deterioration of motor neurons. Analysis of gait parameters is invaluable for better understanding of the mechanisms of movement disorders and the development of NDDs. Patients with neurodegenerative diseases have their own unique gait patterns [1]. Gaits of patients with different severity degrees of NDDs are of various appearances that can be quantified by the unified Parkinson’s disease rating scale (UPDRS), unified Huntington’s disease rating scale (UHDRS), and Hoehn & Yahr rating scale (H & Y) [2, 5,6,7]. We design a learning framework to effectively analyse the gait characteristics and identify individual diseases and disease severity

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