Prognosticating Parkinson’s: Harnessing Wearable Sensors and Attention Mechanism for Enhanced Motor Symptom Prediction

Abstract

Parkinson’s disease (PD) is a progressive neurological disorder that results in loss of dopaminergic neurons in brain and has a profound effect in patients, particularly because of the motor symptoms, including bradykinesia, tremor and dyskinesia. The focus of this thesis is to enhance the prediction and monitoring of such symptoms by developing an encoder-decoder based transformer model in conjunction with wearable inertial sensors for multi-label classification. The proposed model adopts an encoderdecoder structure within a transformer framework and employs multiple attention heads to capture subtle features and temporal relationships from the data gathered by the smartphones, smartwatches, and wrist-worn devices. Notably, wrist-worn sensors provide particularly rich data, contributing to exceptional performance with diagnostic accuracies of 99.11% with phone data, 96.61% with smartwatch data, and 99.34% with wrist-worn sensor data for multi-label classification. This approach emphasizes on how the placement of sensors affects the performance, as the wrist-worn sensors perform better than those placed in other positions. The model also achieves low false positive and false negative rates, along with strong precision and recall values, and exhibits good generalizability. Advanced preprocessing techniques, including normalization, feature scaling, and sequence generation, further enhance model efficiency and address class imbalance. This innovative solution not only improves the management of PD but also enhances patients’ quality of life through a targeted and responsive approach to care. The findings highlight the potential of this technology to transform Parkinson’s disease management, paving the way for early detection and close monitoring of patient symptoms through a novel multi-label classification architecture. Thus, the incorporation of these technologies improves diagnostic assessment and understanding of motor symptom evolution, enabling more targeted and efficient treatment plans based on individual patient conditions, ultimately enhancing PD management and patient quality of life.