Wearable Devices for Movement Monitoring of Patients with Parkinson's Disease_Journal of Biomedical Engineering

Authors：

LILiang ^1,3 , YUQian ¹ , XUBaoteng ¹ , BAIQifan ¹ , ZHANGYunpeng ¹ , ZHANGHuijun ² , MAOChengjie ² , LIUChunfeng ² ,  WANGShouyan ¹

1. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China;
2. The Second Hospital Affiliated to Suzhou University, Suzhou 215004, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China;

Corresponding author：

WANGShouyan, Email: swang@sibet.ac.cn

Keywords：

miniature; low power; wireless; quantitative

DOI：

10.7507/1001-5515.20160187

Video：

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Quantitative assessment of the symptoms of Parkinson's disease is the key for precise diagnosis and treatment and essential for long term management over years. The challenges of quantitative assessment on Parkinson's disease are rich information, ultra-low load, long term and large range monitoring in free-moving condition. In this paper, we developed wearable devices with multiple sensors to monitor and quantify the movement symptoms of Parkinson's disease. Five wearable sensors were used to record motion signals from bilateral forearms, legs and waist. A local area network based on low power Wi-Fi technology was built for long distance wireless data transmission. A software was developed for signal recording and analyzing. The size of each sensor was 39 mm×33 mm×16 mm and the weight was 18g. The sensors were rechargeable and able to run 12 hours. The wireless transmission radius is about 45 m. The wearable devices were tested in patients and normal subjects. The devices were reliable and accurate for movement monitoring in hospital.

Citation： LILiang, YUQian, XUBaoteng, BAIQifan, ZHANGYunpeng, ZHANGHuijun, MAOChengjie, LIUChunfeng, WANGShouyan. Wearable Devices for Movement Monitoring of Patients with Parkinson's Disease. Journal of Biomedical Engineering, 2016, 33(6): 1183-1190. doi: 10.7507/1001-5515.20160187 Copy

1.	CALABRESE V P. Projected number of People with Parkinson disease in the most populous nations, 2005 through 2030[J]. Neurology, 2007, 69(2): 223-224.
2.	MAETZLER W, DOMINGOS J, SRULIJES K, et al. Quantitative wearable sensors for objective assessment of Parkinson's disease[J]. Mov Disord, 2013, 28(12): 1628-1637.
3.	LANG A E, EBERLY S, GOETZ C G, et al. Movement disorder society unified Parkinson disease rating scale experiences in daily living: longitudinal changes and correlation with other assessments[J]. Mov Disord, 2013, 28(14): 1980-1986.
4.	PATEL S, LORINCZ K, HUGHES R, et al. Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors[J]. IEEE Trans Inf Technol Biomed, 2009, 13(6): 864-873.
5.	MOSTILE G, GIUFFRIDA J P, ADAM O R, et al. Correlation between Kinesia system assessments and clinical tremor scores in patients with essential tremor[J]. Mov Disord, 2010, 25(12): 1938-1943.
6.	UHRÍKOVÁ Z, SPRDLÍK O, HOSKOVCOVÁ M, et al. Validation of a new tool for automatic assessment of tremor frequency from video recordings[J]. J Neurosci Methods, 2011, 198(1): 110-113.
7.	MERA T, VITEK J L, ALBERTS J L, et al. Kinematic optimization of deep brain stimulation across multiple motor symptoms in Parkinson's disease[J]. J Neurosci Methods, 2011, 198(2): 280-286.
8.	OESS N P, WANEK J, CURT A. Design and evaluation of a low-cost instrumented glove for hand function assessment[J]. J Neuroeng Rehabil, 2012, 9(1): 2.
9.	ROY S H, COLE B T, GILMORE L D, et al. High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity[J]. Mov Disord, 2013, 28(8): 1080-1087.
10.	PARISI F, FERRARI G, GIUBERTI M, et al. Body-Sensor-Network-Based kinematic characterization and comparative outlook of UPDRS scoring in Leg agility, Sit-to-Stand, and Gait tasks in parkinson's disease[J]. IEEE J Biomed Health Inform, 2015, 19(6): 1777-1793.
11.	汪丰, 邹亚,乔子晏,等.帕金森患者步行运动的定量分析[J].东南大学学报:自然科学版,2015,45(2):266-269.
12.	孙亮, 陈彪.定量运动检测在帕金森病早期诊断和病情评估中的应用价值[J].中国神经精神疾病杂志,2012,38(7):416,438,442,448.
13.	柴远波, 赵春雨.短距离无线通信技术及应用[M].北京:电子工业出版社,2015:29-57.
14.	斯托林斯. 无线通信与网络[M].何军,译.2版.北京:清华大学出版社,2005:335-368.
15.	周奇才, 张慧群,熊肖磊,等.基于Socket网络通信中C心跳包实现方法的研究[J].机电一体化,2014,12(10):11-14, 19.
16.	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.
17.	OUNG Q W, MUTHUSAMY H, LEE H L, et al. Technologies for assessment of motor disorders in Parkinson's disease: a review[J]. Sensors (Basel), 2015, 15(9): 21710-21745.

1. CALABRESE V P. Projected number of People with Parkinson disease in the most populous nations, 2005 through 2030[J]. Neurology, 2007, 69(2): 223-224.
2. MAETZLER W, DOMINGOS J, SRULIJES K, et al. Quantitative wearable sensors for objective assessment of Parkinson's disease[J]. Mov Disord, 2013, 28(12): 1628-1637.
3. LANG A E, EBERLY S, GOETZ C G, et al. Movement disorder society unified Parkinson disease rating scale experiences in daily living: longitudinal changes and correlation with other assessments[J]. Mov Disord, 2013, 28(14): 1980-1986.
4. PATEL S, LORINCZ K, HUGHES R, et al. Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors[J]. IEEE Trans Inf Technol Biomed, 2009, 13(6): 864-873.
5. MOSTILE G, GIUFFRIDA J P, ADAM O R, et al. Correlation between Kinesia system assessments and clinical tremor scores in patients with essential tremor[J]. Mov Disord, 2010, 25(12): 1938-1943.
6. UHRÍKOVÁ Z, SPRDLÍK O, HOSKOVCOVÁ M, et al. Validation of a new tool for automatic assessment of tremor frequency from video recordings[J]. J Neurosci Methods, 2011, 198(1): 110-113.
7. MERA T, VITEK J L, ALBERTS J L, et al. Kinematic optimization of deep brain stimulation across multiple motor symptoms in Parkinson's disease[J]. J Neurosci Methods, 2011, 198(2): 280-286.
8. OESS N P, WANEK J, CURT A. Design and evaluation of a low-cost instrumented glove for hand function assessment[J]. J Neuroeng Rehabil, 2012, 9(1): 2.
9. ROY S H, COLE B T, GILMORE L D, et al. High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity[J]. Mov Disord, 2013, 28(8): 1080-1087.
10. PARISI F, FERRARI G, GIUBERTI M, et al. Body-Sensor-Network-Based kinematic characterization and comparative outlook of UPDRS scoring in Leg agility, Sit-to-Stand, and Gait tasks in parkinson's disease[J]. IEEE J Biomed Health Inform, 2015, 19(6): 1777-1793.
11. 汪丰, 邹亚,乔子晏,等.帕金森患者步行运动的定量分析[J].东南大学学报:自然科学版,2015,45(2):266-269.
12. 孙亮, 陈彪.定量运动检测在帕金森病早期诊断和病情评估中的应用价值[J].中国神经精神疾病杂志,2012,38(7):416,438,442,448.
13. 柴远波, 赵春雨.短距离无线通信技术及应用[M].北京:电子工业出版社,2015:29-57.
14. 斯托林斯. 无线通信与网络[M].何军,译.2版.北京:清华大学出版社,2005:335-368.
15. 周奇才, 张慧群,熊肖磊,等.基于Socket网络通信中C心跳包实现方法的研究[J].机电一体化,2014,12(10):11-14, 19.
16. 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.
17. OUNG Q W, MUTHUSAMY H, LEE H L, et al. Technologies for assessment of motor disorders in Parkinson's disease: a review[J]. Sensors (Basel), 2015, 15(9): 21710-21745.

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