  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, with InceptionTime's high learning capacity being
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 (MLP) on wrist motion data from PD
patients. Our findings indicate that all approaches are suitable for estimating
tremor severity and bradykinesia presence but encounter challenges in detecting
dyskinesia. ROCKET demonstrates superior performance in identifying dyskinesia,
whereas InceptionTime exhibits slightly better performance in tremor and
bradykinesia detection. Notably, both methods outperform the multi-layer
perceptron. In conclusion, InceptionTime exhibits the capability to classify
complex wrist motion time series and holds the greatest potential for
continuous symptom monitoring in PD.
