Wrist attitude-based Parkinson's disease ON/OFF state assessment after medication

doi:10.3785/j.issn.1008-973X.2021.04.005

Journal of ZheJiang University (Engineering Science)

2021, Vol. 55

Issue (4): 639-647 DOI: 10.3785/j.issn.1008-973X.2021.04.005

Wrist attitude-based Parkinson's disease ON/OFF state assessment after medication

Teng ZHANG1,2,3(

),Xin-long JIANG1,2,3,*(

),Yi-qiang CHEN1,2,3,Qian CHEN1,2,3,Tao-mian MI4,Piu CHAN4

1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
2. Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing 100190, China
3. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
4. National Clinical Research Center for Geriatric Disorders, XuanWu Hospital, Capital Medical University, Beijing 100053, China

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Abstract

A Parkinson's disease ON/OFF state detection method based on wrist attitude was proposed aiming at the high-precision ON/OFF state detection of Parkinson's disease patients in pervasive healthcare scenarios. The motion sensor data worn on the wrist were utilized to obtain the wrist attitude feature with attitude determination, which was used as the input of the convolutional neural network to classify the ON/OFF state of Parkinson's disease. The comparative experiment on the clinical patient test data shows that using attitude information can obtain a 20.3% accuracy improvement compared with the optimal results of raw sensor data. The convolutional neural network reduced the amount of model parameters by 90.4% while maintaining a detection accuracy of 88.7%. This accuracy is similar to that of the current optimal network structure. Experiments conducted on the free movement data of clinical patients show that the method can predict the patient's ON/OFF state under unrestricted actions, and achieves an accuracy of 91.5% in the ON state and 94.4% in the OFF state.

Key words： Parkinson's disease wrist attitude motor state assessment wearable sensor deep learning

Received: 08 September 2020 Published: 07 May 2021

CLC:

TP 391

Fund: 国家自然科学基金资助项目（61972383，61902379）；2020年度中国残联课题资助项目（CJFJRRB23-2020）；河北省自然科学基金资助项目（F2019207061）

Corresponding Authors: Xin-long JIANG E-mail: zhangteng19s@ict.ac.cn;jiangxinlong@ict.ac.cn

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Cite this article:

Teng ZHANG,Xin-long JIANG,Yi-qiang CHEN,Qian CHEN,Tao-mian MI,Piu CHAN. Wrist attitude-based Parkinson's disease ON/OFF state assessment after medication. Journal of ZheJiang University (Engineering Science), 2021, 55(4): 639-647.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.04.005 OR http://www.zjujournals.com/eng/Y2021/V55/I4/639

基于腕部姿态的帕金森病用药后开-关期检测

针对医疗普适场景下的帕金森病患者高精度用药开-关期检测问题，提出基于腕部姿态的帕金森病开-关期检测方法.利用佩戴在手腕处的运动传感器数据进行姿态解算，得到腕部姿态信息特征，作为卷积神经网络输入进行帕金森病开-关期状态分类. 在医院临床患者测试数据上进行的对比实验表明，与采用运动传感器原始数据的最优结果相比，采用姿态信息能够提升20.3%的检测准确率；与当前最优的网络结构相比，该方法所采用的卷积神经网络在保持相似检测准确率（88.7%）的前提下，将模型参数量降低90.4%. 在医院临床患者自由活动数据上进行的实验表明，该方法能够在非限定动作下预测患者开-关期状态，达到开期91.5%和关期94.4%的准确率.

关键词： 帕金森疾病, 腕部姿态, 运动状态评估, 可穿戴传感器, 深度学习

Fig.1 Patients' ON/OFF state corresponding to UPDRS score

Fig.2 Overview of ON/OFF state assessment

Fig.3 Rotational coordinate transformations

Fig.4 CNN network architectures for PD state assessment

Fig.5 Residual learning block

Tab.1 Total params of various networks

Tab.2 Participants demographics

Tab.3 Test result of ON/OFF state with wrist attitude

Fig.6 Accuracy of various neural networks on validation

Fig.7 Training profile on raw sensor data and quaternion

Fig.8 Predictions of patient's ON/OFF state corresponding to UPDRS score during whole test period


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