Abstract
This research studies the difficulties in identifying activity patterns in individuals with Parkinson’s disease through triaxial accelerometer data, where the fundamental discrepancy between millisecond-level sensor outputs and minute-level activity labels presents considerable preprocessing obstacles. To maintain label consistency, we omitted minutes displaying mixed activity, resulting in some data loss. Traditional machine learning techniques, such sliding window segmentation, time and frequency domain feature extraction, and random forests, provide valuable insights. Our principal contribution resides in deep learning. We suggested a hybrid model for deep learning named DeepConvLSTM-Attention. This model integrates convolutional neural networks for the extraction of robust local features with recurrent neural networks augmented by attention mechanisms to proficiently capture temporal dependencies. The findings from our studies indicate that DeepConvLSTM Attention significantly improves both the F1 score and overall accuracy compared to standalone LSTM and CNN integrated with LSTM architectures. This underscores its capacity for enhanced activity identification and tailored therapy approaches in the management of Parkinson’s disease.
https://ror.org/00p991c53 
