Twin-tower transformer network for skeleton-based Parkinson’s disease early detection

Abstract

Parkinson’s disease is a chronic neurodegenerative condition accompanied by a variety of motor and non-motor clinical symptoms. Diagnosing Parkinson’s disease presents many challenges, such as excessive reliance on subjective scale scores and a lack of objective indicators in the diagnostic process. Developing efficient and convenient methods to assist doctors in diagnosing Parkinson’s disease is necessary. In this paper, we study the skeleton sequences obtained from gait videos of Parkinsonian patients for early detection of the disease. We designed a Transformer network based on feature tensor fusion to capture the subtle manifestations of Parkinson’s disease. Initially, we fully utilized the distance information between joints, converting it into a multivariate time series classification task. We then built twin towers to discover dependencies within and across sequence channels. Finally, a tensor fusion layer was employed to integrate the features from both towers. In our experiments, our model demonstrated superior performance over the current state-of-the-art algorithm, achieving an 86.8% accuracy in distinguishing Parkinsonian patients from healthy individuals using the PD-Walk dataset.