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

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.

Deep Learning for Monitoring of Human Gait: A Review

“…Against the aim to distinguish FOG events from normal gait, evaluation on the Daphnet FOG dataset [75] from 10 subjects yielded an accuracy of 90.60%. Several other deep ANNs [76], [77] have been trained and tested for human activity recognition from raw spatiotemporal datasets, including the FOG dataset used in [75]. Rad et al [76] and Hammerla et al [78] used a CNN performing well in human activity recognition; however, the performance on the FOG dataset was weaker.…”

“…Several other deep ANNs [76], [77] have been trained and tested for human activity recognition from raw spatiotemporal datasets, including the FOG dataset used in [75]. Rad et al [76] and Hammerla et al [78] used a CNN performing well in human activity recognition; however, the performance on the FOG dataset was weaker. Murad and Pyun [79] improved the FOG recognition accuracy to 94.1% with their proposed deep RNN trained on the Daphnet FOG dataset.…”

Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors

“…Wearable sensors have been successfully demonstrated in healthcare research. Applications commonly used include continuous monitoring of activities of daily living [21,22], gait, and mobility [23,24]. Although the increasing use of wearable sensors poses great challenges to data analyses as the sensors record significant amounts of time-series data, the rapid development of data analytic methods has enabled vast amounts of data to be processed, revealing hidden information.…”

Introduction to Extreme Seeking Entropy

“…Taoum et al presented ND and data fusion methods to identify acute respiratory problems [17]. Rad introduced ND for gait and movement monitoring to diagnosis Parkinson's disease and autism spectrum disorders [18]. Burlina used ND algorithms in the diagnosis of different muscle diseases [19].…”