Parkinson’s Disease (PD) is a neurodegenerativedisorder that manifests through slowly progressing symptoms,such as tremor, voice degradation and bradykinesia. Automateddetection of such symptoms has recently received much attentionby the research community, owing to the clinical benefits associated with the early diagnosis of the disease. Unfortunately,most of the approaches proposed so far, operate under a strictlylaboratory setting, thus limiting their potential applicability inreal world conditions. In this work, we present a method for automatically detecting tremorous episodes related to PD, based on acceleration signals. We propose to address the problemat hand, as a case of Multiple-Instance Learning, wherein asubject is represented as an unordered bag of signal segmentsand a single, expert-provided, ground-truth. We employ adeep learning approach that combines feature learning and alearnable pooling stage and is trainable end-to-end. Results ona newly introduced dataset of accelerometer signals collectedin-the-wild confirm the validity of the proposed approach.
