Assessment of Parkinson’s Disease Severity Using Gait Data: A Deep Learning-Based Multimodal Approach

The ability to regularly assess Parkinson’s disease (PD) symptoms outside of complex laboratories supports remote monitoring and better treatment management. Multimodal sensors are beneficial for sensing different motor and non-motor symptoms, but simultaneous analysis is difficult due to complex dependencies between different modalities and their different format and data properties. Multimodal machine learning models can analyze such diverse modalities together, thereby enhancing holistic understanding of the data and overall patient state. The Unified Parkinson’s Disease Rating Scale (UPDRS) is commonly used for PD symptoms severity assessment. This study proposes a Perceiver-based multimodal machine learning framework to predict UPDRS scores. We selected a gait dataset of 93 PD patients and 73 control subjects from the PhysioNet repository. This dataset includes two-minute walks from each participant using 16 Ground Reaction Force (GRF) sensors, placing eight on each foot. This experiment used both raw gait timeseries signals and extracted features from these GRF sensors. The Perceiver architecture’s hyperparameters were selected manually and through Genetic Algorithms (GA). The performance of the framework was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and linear Correlation Coefficient (CC). Our multimodal approach achieved a MAE of 2.23 ± 1.31, a RMSE of 5.75 ± 4.16 and CC of 0.93 ± 0.08 in predicting UPDRS scores, outperforming previous studies in terms of MAE and CC. This multimodal framework effectively integrates different data modalities, in this case illustrating by predicting UPDRS scores using sensor data. It can be applied to diverse decision support applications of similar natures where multimodal analysis is needed.