|
|
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 |
|
|
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.
|
Received: 08 September 2020
Published: 07 May 2021
|
|
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
|
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
|
|
[1] |
DORSEY E R, CONSTANTINESCU R, THOMPSON J P, et al Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030[J]. Neurology, 2007, 68 (5): 384- 386
doi: 10.1212/01.wnl.0000247740.47667.03
|
|
|
[2] |
ZHANG Z X, ROMAN G C, HONG Z, et al Parkinson's disease in China: prevalence in Beijing, Xian, and Shanghai[J]. Lancet, 2005, 365 (9459): 595- 597
doi: 10.1016/S0140-6736(05)70801-1
|
|
|
[3] |
DAVIE C A A review of Parkinson's disease[J]. British Medical Bulletin, 2008, 86 (1): 109- 127
doi: 10.1093/bmb/ldn013
|
|
|
[4] |
JANKOVIC J Parkinson's disease: clinical features and diagnosis[J]. Journal of Neurology, Neurosurgery and Psychiatry, 2008, 79 (4): 368- 376
doi: 10.1136/jnnp.2007.131045
|
|
|
[5] |
COTZIAS G C, PAPAVASILIOU P S, GELLENE R Modification of Parkinsonism-chronic treatment with L-Dopa[J]. The New England Journal of Medicine, 1969, 280 (7): 337- 345
doi: 10.1056/NEJM196902132800701
|
|
|
[6] |
KEIJSERS N L, HORSTINK M W, GIELEN S C Ambulatory motor assessment in Parkinson's disease[J]. Movement Disorders, 2006, 21 (1): 34- 44
doi: 10.1002/mds.20633
|
|
|
[7] |
HSSAYENI M D, BURACK M A, GHORAANI B. Automatic assessment of medication states of patients with Parkinson's disease using wearable sensors [C]// Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Orlando: IEEE, 2016: 6082-6085.
|
|
|
[8] |
HSSAYENI M D, BURACK M A, M D, et al. Wearable-based mediation state detection in individuals with Parkinson's disease [EB/OL]. (2019–12–10) [2020–11–02]. https://arxiv.org/abs/1809.06973.
|
|
|
[9] |
SAMà A, PéREZ-LOPEZ C, ROMAGOSA J. Dyskinesia and motor state detection in Parkinson's disease patients with a single movement sensor [C]// Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Diego: IEEE, 2012: 1194-1197.
|
|
|
[10] |
HAMMERLA N Y, FISHER J, ANDRAS P, et al. PD disease state assessment in naturalistic environments using deep learning [C]// 29th AAAI Conference on Artificial Intelligence. Austin: AAAI, 2015: 1742-1748.
|
|
|
[11] |
ESKOFIER B M, LEE S I, DANEAULT J F, et al. Recent machine learning advancements in sensor-based mobility analysis: deep learning for Parkinson's disease assessment [C]// Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Orlando: IEEE, 2016: 655-658.
|
|
|
[12] |
TSIPOURAS M G, TZALLAS A T, FOTIADIS D I, et al. On automated assessment of Levodopa-induced dyskinesia in Parkinson's disease [C]// Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Boston: IEEE, 2011: 2679-2682.
|
|
|
[13] |
GOETZ C G, TILLEY B C, SHAFTMAN S R, et al Movement disorder society-sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results[J]. Movement Disorders, 2008, 23 (15): 2129- 2170
doi: 10.1002/mds.22340
|
|
|
[14] |
O'SUILLEABHAIN P E, DEWEY R B J Validation for tremor quantification of an electromagnetic tracking device[J]. Movement Disorders, 2001, 16 (2): 265- 271
doi: 10.1002/mds.1064
|
|
|
[15] |
BEUTER A, GEOFFROY A, CORDO The measurement of tremor using simple laser systems[J]. Journal of Neuroscience Methods, 1994, 53 (1): 47- 54
doi: 10.1016/0165-0270(94)90143-0
|
|
|
[16] |
ZITO G A, GERBER S M, URWYLER P, et al. Development and pilot testing of a novel electromechanical device to measure wrist rigidity in Parkinson’s disease [C]// Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. [S. l. ]: IEEE, 2018: 4885–4888.
|
|
|
[17] |
ALLEN D P, PLAYFER J R, ALY N M On the use of low-cost computer peripherals for the assessment of motor dysfunction in Parkinson's disease-quantification of Bradykinesia using target tracking tasks[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2007, 15 (2): 286- 294
doi: 10.1109/TNSRE.2007.897020
|
|
|
[18] |
PASTOR M A, JAHANSHAHI M, ARTIEDA J, et al Performance of repetitive wrist movements in parkinson's disease[J]. Brain, 1992, 115 (3): 875- 891
doi: 10.1093/brain/115.3.875
|
|
|
[19] |
LONINI L, DAI A, SHAWEN N, et al Wearable sensors for Parkinson's disease: which data are worth collecting for training symptom detection models[J]. NPJ Digital Medicine, 2018, 1 (1): 64- 71
doi: 10.1038/s41746-018-0071-z
|
|
|
[20] |
PATEL S, LORINCZ K, HUGHES R, et al Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors[J]. IEEE Transactions on Information Technology in Biomedicine, 2009, 13 (6): 864- 873
doi: 10.1109/TITB.2009.2033471
|
|
|
[21] |
MANCINI M, PRIEST K C, NUTT J G, et al. Quantifying freezing of gait in Parkinson's disease during the instrumented timed up and go test [C]// Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Diego: IEEE, 2012: 1198-1201.
|
|
|
[22] |
VINCE J. Geometric algebra for computer graphics [M]. London: Springer, 2008: 39-48.
|
|
|
[23] |
HEMINGWAY E G, O'REILLY O M Perspectives on Euler angle singularities, gimbal lock, and the orthogonality of applied forces and applied moments[J]. Multibody System Dynamics, 2018, 44 (1): 31- 56
doi: 10.1007/s11044-018-9620-0
|
|
|
[24] |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60 (6): 84- 90
doi: 10.1145/3065386
|
|
|
[25] |
HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
|
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|