Donié Cedric, Das Neha, Endo Satoshi, Hirche Sandra
TUM School of Computation, Information and Technology, Department of Computer Engineering, Chair of Information-oriented Control, Technical University of Munich, Munich, Germany.
Munich Institute of Robotics and Machine Intelligence (MIRMI), Munich, Germany.
Sci Rep. 2025 May 31;15(1):19140. doi: 10.1038/s41598-025-04263-2.
Parkinson's disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monitoring for more targeted treatment. Classical time series classification and deep learning techniques have demonstrated limited efficacy in monitoring PD symptoms using wearable accelerometer data due to complex PD movement patterns and the small size of available datasets. We investigate InceptionTime and RandOm Convolutional KErnel Transform (ROCKET) as they are promising for PD symptom monitoring. InceptionTime's high learning capacity is well-suited to modeling complex movement patterns, while ROCKET is suited to small datasets. With random search methodology, we identify the highest-scoring InceptionTime architecture and compare its performance to ROCKET with a ridge classifier and a multi-layer perceptron on wrist motion data from PD patients. Our findings indicate that all approaches can learn to estimate tremor severity and bradykinesia presence with moderate performance but encounter challenges in detecting dyskinesia. Among the presented approaches, ROCKET demonstrates higher scores in identifying dyskinesia, whereas InceptionTime exhibits slightly better performance in tremor and bradykinesia estimation. Notably, both methods outperform the multi-layer perceptron. In conclusion, InceptionTime can classify complex wrist motion time series and holds potential for continuous symptom monitoring in PD with further development.
帕金森病（PD）是一种神经退行性疾病，其特征是运动症状频繁变化，因此需要持续进行症状监测以实现更有针对性的治疗。由于帕金森病运动模式复杂且可用数据集规模较小，传统的时间序列分类和深度学习技术在使用可穿戴加速度计数据监测帕金森病症状方面显示出有限的效果。我们研究了InceptionTime和随机卷积核变换（ROCKET），因为它们在帕金森病症状监测方面颇具潜力。InceptionTime的高学习能力非常适合对复杂运动模式进行建模，而ROCKET则适用于小型数据集。通过随机搜索方法，我们确定了得分最高的InceptionTime架构，并将其与使用岭分类器和多层感知器的ROCKET在帕金森病患者手腕运动数据上的性能进行比较。我们的研究结果表明，所有方法都能够学习估计震颤严重程度和运动迟缓的存在，性能中等，但在检测异动症方面面临挑战。在所提出的方法中，ROCKET在识别异动症方面得分更高，而InceptionTime在震颤和运动迟缓估计方面表现略好。值得注意的是，这两种方法都优于多层感知器。总之，InceptionTime可以对复杂的手腕运动时间序列进行分类，并在进一步发展后具有在帕金森病中进行持续症状监测的潜力。
