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

Nastaran Mohammadian Rad,Twan Van Laarhoven,Elena Marchiori,Cesare Furlanello

doi:10.3390/s18103533

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.

Highlights

Recent advances in wearable sensor technology, and Inertial Measurement Unit (IMU) sensors, have provided an effective platform for remote monitoring of patients with motor malfunctions such as Parkinson’s Disease (PD) [1] and Autism Spectrum Disorder (ASD) [2]
We addressed the problem of automatic abnormal movement detection in ASD and PD patients in a novelty detection framework
In the normative modeling framework, we used a convolutional denoising autoencoder to learn the distribution of the normal human movements from the accelerometer signals

Summary

Introduction

Recent advances in wearable sensor technology, and Inertial Measurement Unit (IMU) sensors, have provided an effective platform for remote monitoring of patients with motor malfunctions such as Parkinson’s Disease (PD) [1] and Autism Spectrum Disorder (ASD) [2]. IMUs contain built-in accelerometers, gyroscopes and magnetometer sensors allowing one to measure the angular velocity and linear acceleration of body parts during movement. In psychiatric clinical studies, IMUs provide the possibility to measure the kinetic symptoms and phenotypes automatically, and, they enable caregivers to follow up on the progress of diseases and the quality of interventions more frequently than the current clinical practices [3,4]. ASD and PD are respectively neuro-developmental and neuro-degenerative disorders, each with different symptoms involving atypical motor movements.

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Discussion

Conclusion