Multimodal Gait Recognition for Neurodegenerative Diseases

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.

Automated summary

This article proposes a novel hybrid model to learn gait differences between different neurodegenerative diseases, Parkinson's disease severity levels, and healthy individuals and patients through fusion and aggregation of data from multiple sensors. The model utilizes a spatial feature extractor and a new correlative memory neural network architecture to capture temporal information, along with a multiswitch discriminator to associate observations with individual state estimations. Compared to several state-of-the-art techniques, the framework shows more accurate classification results.