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Sensors (Basel). 2018 Oct 19;18(10). pii: E3533. doi: 10.3390/s18103533.

Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson's Disease and Autism Spectrum Disorders.

Author information

1
Institute for Computing and Information Science, Radboud University, 6525EC Nijmegen, The Netherlands. n.mrad@cs.ru.nl.
2
Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy. n.mrad@cs.ru.nl.
3
Fondazione Bruno Kessler, 38123 Trento, Italy. n.mrad@cs.ru.nl.
4
Institute for Computing and Information Science, Radboud University, 6525EC Nijmegen, The Netherlands. mail@twanvl.nl.
5
Faculty of Management, Science and Technology, Open University of the Netherlands, 6419AT Heerlen, The Netherlands. mail@twanvl.nl.
6
Fondazione Bruno Kessler, 38123 Trento, Italy. furlan@fbk.eu.
7
Institute for Computing and Information Science, Radboud University, 6525EC Nijmegen, The Netherlands. elenam@cs.ru.nl.

Abstract

Detecting and monitoring of abnormal movement behaviors in patients with Parkinson's Disease (PD) and individuals with Autism Spectrum Disorders (ASD) are beneficial for adjusting care and medical treatment in order to improve the patient's quality of life. Supervised methods commonly used in the literature need annotation of data, which is a time-consuming and costly process. In this paper, we propose deep normative modeling as a probabilistic novelty detection method, in which we model the distribution of normal human movements recorded by wearable sensors and try to detect abnormal movements in patients with PD and ASD in a novelty detection framework. In the proposed deep normative model, a movement disorder behavior is treated as an extreme of the normal range or, equivalently, as a deviation from the normal movements. Our experiments on three benchmark datasets indicate the effectiveness of the proposed method, which outperforms one-class SVM and the reconstruction-based novelty detection approaches. Our contribution opens the door toward modeling normal human movements during daily activities using wearable sensors and eventually real-time abnormal movement detection in neuro-developmental and neuro-degenerative disorders.

KEYWORDS:

Parkinson’s disease; autism spectrum disorder; deep learning; denoising autoencoders; freezing of gait; normative modeling; novelty detection; stereotypical motor movements

PMID:
30347656
PMCID:
PMC6211024
DOI:
10.3390/s18103533
[Indexed for MEDLINE]
Free PMC Article

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