Parkinson's disease (PD) is a neurodegenerative disease with frequently changing motor symptoms where continuous symptom monitoring enables more targeted treatment. Classical time series classification (TSC) and deep learning techniques have limited performance for PD symptom monitoring using wearable accelerometer data because PD movement patterns are complex, but datasets are small. We investigate InceptionTime and RandOm Convolutional KErnel Transform (ROCKET) because they are state-of-the-art for TSC and promising for PD symptom monitoring: InceptionTime's high learning capacity is suited to modeling complex movement patterns while ROCKET is suited to small datasets. We used a random search to find the highest-scoring InceptionTime architecture and compared it to ROCKET with a ridge classifier and a multi-layer perceptron (MLP) on wrist motions of PD patients. We find that all approaches are suitable for estimating tremor severity and bradykinesia presence but struggle with detecting dyskinesia. ROCKET performs better for dyskinesia, whereas InceptionTime is slightly better for tremor and bradykinesia but has much higher variability in performance. Both outperform the MLP. In conclusion, both InceptionTime and ROCKET are suitable for continuous symptom monitoring, with the choice depending on the symptom of interest and desired robustness.

介绍了使用InceptionTime和RandOm Convolutional KErnel Transform(ROCKET)处理可穿戴加速度计数据在帕金森病患者的症状监测方面的研究，结果显示这两种方法都适用于连续症状监测，但选择取决于所关注的症状和期望的稳健性。

基于手腕动作的时间序列分类用于帕金森病检测