Comparing laboratory and in-the-wild data for continuous Parkinson's Disease tremor detection

Abstract

Passive, continuous monitoring of Parkinson's Disease (PD) symptoms in the wild (i.e., in home environments) could improve disease management, thereby improving a patient's quality of life. We envision a system that uses machine learning to automatically detect PD symptoms from accelerometer data collected in the wild. Building such systems, however, is challenging because it is difficult to obtain labels of symptom occurrences in the wild. Many researchers therefore train machine learning algorithms on laboratory data with the assumption that findings will translate to the wild. This paper assesses how well laboratory data represents wild data by comparing PD symptom (tremor) detection performance of three models on both lab and wild data. Findings indicate that, for this application, laboratory data is not a good representation of wild data. Results also show that training on wild data, even though labels are less precise, leads to better performance on wild data than training on accurate labels from laboratory data.Clinical relevance - Results in this paper suggest that, when building a system for in-the-wild PD symptom detection, it is better to train machine learning algorithms on data from the wild as opposed to from the lab, even though wild labels are less precise. This paper also presents a newly released dataset for PD tremor detection in lab and wild environments.