Parkinson's disease (PD) is the second most common neurodegenerative disease affecting millions worldwide. Bespoke subject-specific treatment (medication or deep brain stimulation (DBS)) is critical for management, yet depends on precise assessment cardinal PD symptoms-bradykinesia, rigidity and tremor. Clinician diagnosis is the basis of treatment, yet it allows only a cross-sectional assessment of symptoms which can vary on an hourly basis and is liable to inter-and intra-rater subjectivity across human examiners. Automated symptomatic assessment has attracted significant interest to optimise treatment regimens between clinician visits, however, no wearable has the capacity to simultaneously assess all three cardinal symptoms. Challenges in the measurement of rigidity, mapping muscle activity out-of-clinic and sensor fusion have inhibited translation. In this study, we address all through a novel wearable sensor system and machine learning algorithms. The sensor system is composed of a forcesensor, three inertial measurement units (IMUs) and four custom mechanomyography (MMG) sensors. The system was tested in its capacity to predict Unified Parkinson's Disease Rating Scale (UPDRS) scores based on quantitative assessment of bradykinesia, rigidity and tremor in PD patients. 23 PD patients were tested with the sensor system in parallel with exams conducted by treating clinicians and Manuscript

Deep brain stimulation (DBS) is currently being used as a treatment for symptoms of Parkinson's disease (PD). Tracking symptom severity progression and deciding the optimal stimulation parameters for people with PD is extremely difficult. This study presents a sensor system that can quantify the three cardinal motor symptoms of PD - rigidity, bradykinesia and tremor. The first phase of this study assesses whether data recorded from the system during physical examinations can be used to correlate to clinician's severity score using supervised machine learning (ML) models. The second phase concludes whether the sensor system can distinguish differences before and after DBS optimisation by a clinician when Unified Parkinson's Disease Rating Scale (UPDRS) scores did not change. An average accuracy of 90.9 % was achieved by the best ML models in the first phase, when correlating sensor data to clinician's scores. Adding on to this, in the second phase of the study, the sensor system was able to pick up discernible differences before and after DBS optimisation sessions in instances where UPDRS scores did not change.

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