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Validation of Inertial Sensing-Based Wearable Device for Tremor and Bradykinesia Quantification

随机森林 支持向量机 计算机科学 可穿戴计算机 人工智能 评定量表 机器学习 惯性测量装置 物理医学与康复 非参数统计 基本事实 参数统计 模式识别(心理学) 医学 数学 统计 嵌入式系统
作者
Houde Dai,Guoen Cai,Zhirong Lin,Zengwei Wang,Qinyong Ye
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:25 (4): 997-1005 被引量:52
标识
DOI:10.1109/jbhi.2020.3009319
摘要

Neurologists judge the severity of Parkinsonian motor symptoms according to clinical scales, and their judgments exist inconsistent because of differences in clinical experience. Correspondingly, inertial sensing-based wearable devices (ISWDs) produce objective and standardized quantifications. However, ISWDs indirectly quantify symptoms by parametric modeling of angular velocities and linear accelerations nd trained by the judgments of several neurologists through supervised learning algorithms. Hence, the ISWD outputs are biased along with the scores provided by neurologists. To investigate the effectiveness ISWDs for Parkinsonian symptoms quantification, technical verification and clinical validation of both tremor and bradykinesia quantification methods were carried out. A total of 45 Parkinson's disease patients and 30 healthy controls performed the tremor and finger-tapping tasks, which were tracked simultaneously by an ISWD and a 6-axis high-precision electromagnetic tracking system (EMTS). The Unified Parkinson's Disease Rating Scale (UPDRS) prescribed parameters obtained from the EMTS, which directly provides linear and rotational displacements, were compared with the scores provided by both the ISWD and seven neurologists. EMTS-based parameters were regarded as the ground truth and were employed to train several common machine learning (ML) algorithms, i.e., support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF) algorithms. Inconsistency among the scores provided by the neurologists was proven. Besides, the quantification performance (sensitivity, specificity, and accuracy) of the ISWD employed with ML algorithms were better than that of the neurologists. Furthermore, EMTS can be utilized to both modify the quantification algorithms of ISWDs and improve the assessment skills of young neurologists.
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