基于可穿戴式传感装置的帕金森运动症状量化评估的研究进展

doi:10.3969/j.issn.0258-8021.2018.02.013

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (804 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The quantitative assessment of motor symptoms of Parkinson's disease (PD) has remained a significant challenge since PD was first defined in 1817. However, with benefits of MEMS (Micro-Electro-Mechanical Systems) motion sensors for high-precision motion tracking, together with the multivariate statistical analysis and estimation theory, PD motor symptoms quantification has become available. In this article, at first, the MEMS motion sensing technique and the typical wearable systems for the quantification of PD motor symptoms were introduced. Secondly, the quantification methods of the four primary motor symptoms and their correlations to the UPDRS judgments of neurologists were compared and analyzed. After that, the impacts of motor fluctuations and the side effects of treatments such as dyskinesia for the quantification of PD symptoms were discussed. This paper also introduced the recent advances in quantitative assessment of parkinsonian motor symptoms based on wearable devices, and provides a system structure for the research of quantitative assessment of PD motor symptoms.

Key words： parkinsonian motor symptoms MEMS motion sensors wearable devices quantitative assessment UPDRS

Received: 12 January 2017

PACS:

R318

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Dai Houde
	Xiong Yongsheng
	Cai Guoen
	Lin Zhirong
	Ye Qinyong

Cite this article:

Dai Houde,Xiong Yongsheng,Cai Guoen, et al. Recent Advances in Quantitative Assessment of Parkinsonian Motor Symptoms Based on Wearable Devices[J]. Chinese Journal of Biomedical Engineering, 2018, 37(2): 229-236.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2018.02.013 OR http://cjbme.csbme.org/EN/Y2018/V37/I2/229

[1] Parkinson J. An Essay on the shaking palsy[J]. J Neuropsychiatry Clin Neurosci, 2002, 14(14): 223-236.
[2] Jankovic J. Parkinson's disease: clinical features and diagnosis[J]. J Neurol Neurosurg Psychiatry, 2008, 79(4): 368-376.
[3] 刘疏影, 陈彪. 帕金森病流行现状[J]. 中国现代神经疾病杂志, 2016, 16(2): 98-101.
[4] 中华医学会神经病学分会帕金森病及运动障碍学组. 中国帕金森病治疗指南(第三版)[J]. 中华神经科杂志, 2014, 47(6): 428-433.
[5] Goetz CG, Tilley BC, Shaftman SR. Movement disorder society-sponsored revision of the unified Parkinson's disease rating scale (MDS-UPDRS): Scale presentation and clinimetric testing results[J]. Mov Disord, 2008, 23(15): 2129-2170.
[6] 中华医学会神经病学分会帕金森病及运动障碍学组. 中国帕金森病的诊断标准(2016版)[J]. 中华神经科杂志, 2016, 49(4): 268-271.
[7] Massano J, Bhatia KP. Clinical approach to Parkinson's disease: features, diagnosis, and principles of management [J]. Cold Spring Harb Perspect Med, 2012, 2(6): a008870-a008870.
[8] Okun MS. Deep-brain stimulation for Parkinson's disease [J]. N Engl J Med, 2013, 368(5): 483-484.
[9] Miocinovic S, Somayajula S, Chitnis S, et al. History, applications, and mechanisms of deep brain stimulation[J]. JAMA Neurol, 2013, 70(2): 163-171.
[10] Pahwa R, Lyons KE. Handbook of Parkinson's Disease[M]. (Fifth Edition). New York: CRC Press, 2013.
[11] Shaeffer DK. MEMS inertial sensors: A tutorial overview [J]. IEEE Commun Mag, 2013, 51(4): 100-109.
[12] 帕金森病监测仪[J]. 中国医疗器械杂志, 2014(3): 189-189.
[13] Kinesia-Great Lakes Neuro Technologies. Objective Parkinson's assessment: Wearables, ambulatory monitoring, & telemedicine [EB/OL]. http://glneurotech.com/kinesia/.html, 2016-12-01/2017-01-10.
[14] Niazmand K, Tonn K, Kalaras A, et al. Quantitative evaluation of parkinson's disease using sensor based smart glove[C] // 24th International Symposium on Computer-Based Medical Systems (CBMS). Bristol: IEEE, 2011: 1-8.
[15] Dai HD, D'Angelo LT. Quantitative assessment of tremor during deep-brain stimulation using a wearable glove system[C] //IEEE International Workshop of Internet-of-Things Networking and Control (IoT-NC). New Orleans: IEEE, 2013: 81-85.
[16] Dai HD, D'Angelo LT. A portable system for quantitative assessment of parkinsonian bradykinesia during deep-brain stimulation surgery[C] //2nd International Conference on Advances in Biomedical Engineering (ICABME). Tripoli: IEEE, 2013: 77-80.
[17] Dai HD, Otten B, Mehrkens JH, et al. A portable system for quantitative assessment of parkinsonian rigidity[C] //35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka: IEEE, 2013: 6591-6594.
[18] Dai HD, Otten B, Mehrkens JH, et al. A novel glove monitoring system used to quantify neurological symptoms during deep-brain stimulation surgery[J]. IEEE Sens J, 2013, 13(9): 3193-3202.
[19] Dai HD, Lin HJ, Lueth TC. Quantitative assessment of parkinsonian bradykinesia based on an inertial measurement unit[J]. Biomed Eng Online, 2015, 14: 68-68.
[20] Dai HD, Zhang PY, Lueth TC. Quantitative Assessment of Parkinsonian Tremor Based on an Inertial Measurement Unit[J]. Sensors, 2015, 15: 25055-25071.
[21] Zhao Y, Anhalt F, Fietzek UM, et al. Multi-Cue Unit: An independent device and actuator of a wearable system for gait-support in Parkinson patients[C] //IEEE International Conference on Microwaves, Communications, Antennas and Electronics Systems (COMCAS). Tel Aviv: IEEE, 2013, 143(6): 1-5.
[22] Coy JA, Pfeiffer JH, Krieger YS, et al. Mechatronic device for the optimization of the DBS-electrode placement[C] //6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob). Singapore: IEEE, 2016: 214-219.
[23] Coy JA, Mehrkens JH, Roppenecker DB, et al. Finding the center of Parkinson's disease. A novel measurement device for quantifying motor symptoms during DBS-surgery[C] //IEEE International Conference on Robotics and Biomimetics (ROBIO). Bali: IEEE, 2014: 1691-1696.
[24] Zhao Y, Ramesberger S, Fietzek UM, et al. A novel wearable laser device to regulate stride length in Parkinson's disease[C] //35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka: IEEE, 2013: 5895-5898.
[25] Intel Corporation. The Michael J. Fox Foundation and Intel Join Forces to Improve Parkinson's Disease Monitoring and Treatment through Advanced Technologies. https://newsroom.intel.com/news-releases/.html,2014-08-13/2017-01-10.
[26] Apple Incorporated. ResearchKit and CareKit-Empowering medical researchers, doctors, and you. [EB/OL]. http://www.apple.com/researchkit/.html, 2015-03-10/2017-01-10.
[27] 中国科学院苏州生物医学工程技术研究所. 一种用于定量评估帕金森患者运动功能的智能手环[P]. 中国: 201510020813.1, 2015-04-22.
[28] Salarian A, Russmann H, Wider C, et al. Quantification of tremor and bradykinesia in Parkinson's Disease using a novel ambulatory monitoring system[J]. IEEE Trans Biomed Eng, 2007, 54(2): 313-322.
[29] Cole BT, Roy SH, De Luca CJ, et al. Dynamical learning and tracking of tremor and dyskinesiafrom wearable sensors[J]. IEEE Trans Neural Syst Rehabil Eng, 2014, 22(5): 982-991.
[30] Patrick SK, Denington AA, Gauthier MJ, et al. Quantification of the UPDRS rigidity scale [J]. IEEE Trans Neural Syst Rehabil Eng, 2001, 9(1): 31-41.
[31] Salarian A, Russmann H, Vingerhoets FJ, et al. Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring[J]. IEEE Trans Biomed Eng, 2004, 51(8): 1434-1443.
[32] Jankovic J. Motor fluctuations and dyskinesias in Parkinson's disease: clinical manifestations[J]. Mov Disord. 2005, 20 Suppl 11: S11-S16.
[33] Timmer J, Haussler S, Lauk M, et al. Pathological tremors: Deterministic chaos or nonlinear stochastic oscillators? [J]. Chaos, 2000, 10(1): 278-288.
[34] Elble RJ, Pullman SL, Matsumoto JY, et al. Tremor amplitude is logarithmically related to 4-and 5-point tremor rating scales [J]. Brain, 2006, 129(10): 2660-2666.
[35] Giuffrida JP, Riley DE, Maddux BN, et al. Clinically deployable Kinesia^TM technology for automated tremor assessment [J]. Mov Disord, 2009, 24(5): 723-730.
[36] Daneault JF, Carignan B, Codère C, et al. Using a smart phone as a standalone platform for detection and monitoring of pathological tremors[J]. Front Hum Neurosci, 2013, 6(2): 357-357.
[37] Kostikis N, Hristu-Varsakelis D,Arnaoutoglou M, et al. A Smartphone-Based Tool for Assessing Parkinsonian Hand Tremor [J]. IEEE J Biomed Health Inform, 2015, 19(6): 1835-1842.
[38] 郄剑文, 贾方秀, 鲁鹏威. 基于MEMS的帕金森病震颤实时评估系统[J]. 电子技术应用, 2016, 42(2): 42-45.
[39] Espay AJ, Giuffrida JP, Chen R, et al.Differential response of speed, amplitude, and rhythm to dopaminergic medications in Parkinson's disease[J]. Mov Disord, 2011, 26(14): 2504-2508.
[40] Heldman DA, Filipkowski DE, Riley DE, et al. Automated motion sensor quantification of gait and lower extremity bradykinesia[C] //Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). San Diego: IEEE, 2012: 1956-1959.
[41] Mentzel TQ, Lieverse R, Levens A, et al. Reliability and validity of an instrument for the assessment of bradykinesia[J]. Psychiatry Res, 2016, 238: 189-195.
[42] Martinez-Manzanera O, Roosma E, Beudel M, et al. A Method for Automatic and Objective Scoring of Bradykinesia Using Orientation Sensors and Classification Algorithms [J]. IEEE Trans Biomed Eng, 2016, 63(5): 1016-1024.
[43] Kwon Y, Park SH, Kim JW, et al. Quantitative evaluation of parkinsonian rigidity during intra-operative deep brain stimulation [J]. BioMed Mater Eng, 2014, 24(6): 2273-2281.
[44] Xia R, Sun J, Threlkeld AJ. Analysis of interactive effect of stretch reflex and shortening reaction on rigidity in parkinson's disease [J]. Clin Neurophysiol, 2009, 120(7): 1400-1407.
[45] Xia R, Muthumani A, Mao ZH, et al. Quantification of neural reflex and muscular intrinsic contributions to parkinsonian rigidity[J]. Exp Brain Res, 2016, 234(12): 3587-3595.
[46] Patrick SK, Denington AA, Gauthier MJ, et al. Quantification of the UPDRS rigidity scale [J]. IEEE Trans Neural Syst Rehabil Eng, 2001, 9(1): 31-41.
[47] Fung VS, Burne JA, Morris JG. Objective quantification of resting and activated parkinsonian rigidity: A comparison of angular impulse and work scores [J]. Mov Disord, 2000, 15(1): 48-55.
[48] Mak MK, Wong EC, Hui-Chan CW. Quantitative measurement of trunk rigidity in parkinsonian patients[J]. J Neurol, 2007, 254(2): 202-209.
[49] Park BK, Kwon Y, Kim JW, et al.Analysis of viscoelastic properties of wrist joint for quantification of parkinsonian rigidity [J]. IEEE Trans Neural Syst Rehabil Eng, 2011, 19(2): 167-176.
[50] Kaminsky TA, Dudgeon BJ, Billingsley FF, et al. Virtual cues and functional mobility of people with Parkinson's disease: a single-subject pilot study [J]. J Rehabil Res Dev, 2007, 44(3): 437-448.
[51] Hundza SR, Hook WR, Harris CR, et al. Accurate and Reliable Gait Cycle Detection in Parkinson's Disease[J]. IEEE Trans Neural Syst Rehabil Eng, 2014, 22(1): 127-137.
[52] Demonceau M, Donneau AF, Croisier JL, et al. Contribution of a trunk accelerometer system to thecharacterization of gait in patients with mild-to-moderate Parkinson's Disease[J]. IEEE J Biomed Health Inform 2015, 19(6): 1803-1808.
[53] Nancy JY, Khanna NH, Arputharaj K. A Q-backpropagated time delay neural network for diagnosing severity of gait disturbances in Parkinson's disease [J]. J Biomed Inform, 2016, 60: 169-176.
[54] Lin ZR, Dai HD, Xiong YS, et al. Quantification assessment of bradykinesia in Parkinson's disease based on a wearable device[C] // 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Seogwipo: IEEE, 2017, 803-806.
[55] Burkhard PR, Shale H, Langston JW, et al.Quantification of dyskinesia in Parkinson's disease: validation of a novel instrumental method[J]. Mov Disord, 1999, 14(5):754-763.
[56] Griffiths RI, Kotschet K, Arfon S, et al. Automated Assessment of bradykinesia and dyskinesia in Parkinson's disease[J]. J Parkinsons Dis, 2012, 2(1): 47-55.
[57] Pulliam CL, Burack MA, Heldman DA, et al. Motion sensor dyskinesia assessment during activities of daily living[J]. J Parkinsons Dis, 2014, 4(4): 609-615.
[58] Tsipouras MG1, Tzallas AT, Rigas G, et al. Automated Levodopa-induced dyskinesia assessment[C] //2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Buenos Aires: IEEE. 2010, 2411-2414.
[59] Lopane G, Mellone S, Chiari L, et al. Dyskinesia detection and monitoring by a single sensor in patients with Parkinson's disease[J]. Mov Disord, 2015, 30(9): 1267-1271.
[60] Ramsperger R, Meckler S, Heger T, et al. Continuous leg dyskinesia assessment in Parkinson's disease -clinical validity and ecological effect [J]. Parkinsonism Relat Disord. 2016, 26: 41-46.