Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning

Parkinson’s Disease (PD) is a slowly evolving neurologicaldisease that affects about 1% of the population above 60years old, causing symptoms that are subtle at first, but whoseintensity increases as the disease progresses. Automated detectionof these symptoms could offer clues as to the early onset of thedisease, thus improving the expected clinical outcomes of thepatients via appropriately targeted interventions. This potentialhas led many researchers to develop methods that use widelyavailable sensors to measure and quantify the presence of PDsymptoms such as tremor, rigidity and braykinesia. However,most of these approaches operate under controlled settings,such as in lab or at home, thus limiting their applicabilityunder free-living conditions. In this work, we present a methodfor automatically identifying tremorous episodes related to PD,based on IMU signals captured via a smartphone device. Wepropose a Multiple-Instance Learning approach, wherein a subject is represented as an unordered bag of accelerometer signalsegments and a single, expert-provided, tremor annotation. Ourmethod combines deep feature learning with a learnable poolingstage that is able to identify key instances within the subjectbag, while still being trainable end-to-end. We validate our algorithmon a newly introduced dataset of 45 subjects, containingaccelerometer signals collected entirely in-the-wild. The good classification performance obtained in the conducted experimentssuggests that the proposed method can efficiently navigate the noisy environment of in-the-wild recordings.&nbsp