Multimodal Gait Recognition for Neurodegenerative Diseases
This research addresses the problem of accurately classifying neurodegenerative diseases and their severity using multimodal gait analysis, which is important for clinicians and patients.
This paper proposes a novel hybrid model that fuses and aggregates data from multiple sensors to learn gait differences for neurodegenerative diseases. The model successfully classifies between three neurodegenerative diseases, different severity levels of Parkinson's disease, and between healthy individuals and patients, outperforming several state-of-the-art techniques.
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.