Abstract：
:Parkinson's Disease (PD) is a slowly evolving neurological disease that affects about [Formula: see text] of the population above 60 years old, causing symptoms that are subtle at first, but whose intensity increases as the disease progresses. Automated detection of these symptoms could offer clues as to the early onset of the disease, thus improving the expected clinical outcomes of the patients via appropriately targeted interventions. This potential has led many researchers to develop methods that use widely available sensors to measure and quantify the presence of PD symptoms 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 applicability under free-living conditions. In this work, we present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device. We propose a Multiple-Instance Learning approach, wherein a subject is represented as an unordered bag of accelerometer signal segments and a single, expert-provided, tremor annotation. Our method combines deep feature learning with a learnable pooling stage that is able to identify key instances within the subject bag, while still being trainable end-to-end. We validate our algorithm on a newly introduced dataset of 45 subjects, containing accelerometer signals collected entirely in-the-wild. The good classification performance obtained in the conducted experiments suggests that the proposed method can efficiently navigate the noisy environment of in-the-wild recordings.
journal_name
IEEE J Biomed Health Informjournal_title
IEEE journal of biomedical and health informaticsauthors
Papadopoulos A,Kyritsis K,Klingelhoefer L,Bostanjopoulou S,Chaudhuri KR,Delopoulos Adoi
10.1109/JBHI.2019.2961748subject
Has Abstractpub_date
2020-09-01 00:00:00pages
2559-2569issue
9eissn
2168-2194issn
2168-2208journal_volume
24pub_type
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