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.

帕金森病是一种慢性神经退行性疾病，伴有多种运动和非运动临床症状。帕金森病的诊断面临许多挑战，例如过度依赖主观量表评分以及诊断过程中缺乏客观指标。有必要开发高效、便捷的方法来协助医生诊断帕金森病。在本文中，我们研究从帕金森病患者的步态视频中获得的骨骼序列，以早期发现该疾病。我们设计了一个基于特征张量融合的 Transformer 网络来捕捉帕金森病的微妙表现。最初，我们充分利用关节之间的距离信息，将其转换为多元时间序列分类任务。然后，我们构建了双塔来发现序列通道内和序列通道之间的依赖关系。最后，采用张量融合层来集成两个塔的特征。在我们的实验中，我们的模型表现出了优于当前最先进算法的性能，使用 PD-Walk 数据集区分帕金森病患者和健康个体的准确率达到 86.8%。