Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson's Disease and Autism Spectrum Disorders.
Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson's Disease and Autism Spectrum Disorders.
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DOI：
10.3390/s18103533
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发表时间：
2018-10-19
期刊：
影响因子：
--
通讯作者：
Marchiori E
中科院分区：
文献类型：
--
作者：
Mohammadian Rad N;van Laarhoven T;Furlanello C;Marchiori E
检测和监测帕金森病（PD）和自闭症谱系障碍（ASD）患者的异常运动行为，有助于调整护理和医疗措施，提高患者的生活质量。文献中常用的监督方法需要对数据进行注释，这是一个耗时且成本高昂的过程。在本文中，我们提出了深度规范建模作为一种概率新奇检测方法，其中我们对可穿戴传感器记录的正常人体运动的分布进行建模，并尝试在新奇检测框架中检测PD和ASD患者的异常运动。在所提出的深度规范模型中，运动障碍行为被视为正常范围的极端，或者等同地，被视为与正常运动的偏离。在三个基准数据集上的实验表明，该方法的有效性优于单类SVM和基于重构的新奇检测方法。我们的贡献为使用可穿戴传感器在日常活动中模拟正常人体运动以及最终在神经发育和神经退行性疾病中实时检测异常运动打开了大门。
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.
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