Predicting Parkinson's disease from wrist worn accelerometer data from UK Biobank

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Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide, with a prevalence of roughly 9.4M in 2020. The disease is caused by the gradual degeneration of dopaminergic neurons in the substantia nigra pars compacta, which leads to symptoms related to changes in motor ability. Though the condition often presents during the later stages of life, the prevalence of this disease continues to rise, with a 57% increase from 2016 to 2020. It is predicted that by 2040, neurodegenerative diseases will surpass cancer as a leading cause of disease related death. While PD does not directly cause death, it can significantly reduce the quality of life of sufferers and there are many PD related symptoms that can be fatal. A particular concern with PD, is that it currently has no cure, and no known definitive test for diagnosis. Current work is exploring the use of UK Biobank data to build a Gaussian Mixture Model (GMM) that distinguishes 202 PD subjects from 380 total subjects, given a week of accelerometery extracted from wrist worn smart watches [1]. This found reasonable success; however, several limitations were noted, which give rise to potential further research in this field. The paper uses a GMM that takes chosen data-driven features, hence does not take advantages of known motor related PD symptoms, including tremors, a high frequency shaking, and bradykinesia, a slowness of movement, nor a range of models capable of extracting these features. In addition, there is a lack of analysis of false positives of the model, which may give an indication of behaviours that resemble that of PD sufferers, and therefore a better understanding of the prediction. Aims: Build a deep learning model for PD detection: The investigation will begin with the use of transformer networks to predict PD from periods of gait from the UK biobank data set. The periods of gait will first be extracted from the full accelerometery spanning 1 week. This will be validated using known datasets used for identifying gait from accelerometery. These gait periods will then be input to a transformer network to deliver a PD detector. Develop a severity score for monitoring PD related motor symptoms: It is useful for purposes of monitoring to be able to identify interventions that have either positive or negative effects on PD related motor symptoms. To do so, it is necessary to have a score that gives an indication of the severity and regularity of PD related motor systems, to give an indication of the severity of PD observed over a given time window. To do so, it possible to use a score related to the probability output by the model used to diagnostically predict PD. Develop an understanding of relevant features that aid PD prediction: As there is no definitive diagnosis for PD, it is useful to gather as much knowledge as possible from algorithms used to predict PD. It may be possible to extract features from the accelerometer data taken that are critical in the model operation. These features can then be compared to experts/known literature, to build a better consensus for PD diagnosis. This is possible using various explainable AI techniques. Apply the principles used in achieving the previous aims, for similar motor related diseases: There are many conditions and diseases that may be indicated by a change in motor ability, and features that can be picked up by accelerometery. This includes Rheumatoid Arthritis, Amyotrophic Lateral Sclerosis, Kennedy's disease etc. It is possible that the framework for techniques used during this research can also be applied to the diagnosis and monitoring of those diseases. This work is in collaboration with GlaxoSmithKline plc. This project falls within the EPSRC Healthcare technologies Theme and is related to the Clinical technologies research area. Reference: [1] Williamson et al (2021) Detecting Parkinson's Disease from Wrist-Worn Accelerometry in the UK Biobank Sensors, 21(6), 2047