Wireless Sensor-Based Smart-Clothing Platform for ECG Monitoring

The goal of this study is to use wireless sensor technologies to develop a smart clothes service platform for health monitoring. Our platform consists of smart clothes, a sensor node, a gateway server, and a health cloud. The smart clothes have fabric electrodes to detect electrocardiography (ECG) signals. The sensor node improves the accuracy of QRS complexes detection by morphology analysis and reduces power consumption by the power-saving transmission functionality. The gateway server provides a reconfigurable finite state machine (RFSM) software architecture for abnormal ECG detection to support online updating. Most normal ECG can be filtered out, and the abnormal ECG is further analyzed in the health cloud. Three experiments are conducted to evaluate the platform's performance. The results demonstrate that the signal-to-noise ratio (SNR) of the smart clothes exceeds 37 dB, which is within the “very good signal” interval. The average of the QRS sensitivity and positive prediction is above 99.5%. Power-saving transmission is reduced by nearly 1980 times the power consumption in the best-case analysis.

[1]  Wen-Dien Chang,et al.  Comparison of Heart Rate Variability between Mild and Severe Depression in Menopausal Women with Low Exercise Behavior , 2013 .

[2]  Keri J. Heilman,et al.  Accuracy of the LifeShirt® (Vivometrics) in the detection of cardiac rhythms , 2007, Biological Psychology.

[3]  Benno Torgler,et al.  Heart rate variability, the autonomic nervous system, and neuroeconomic experiments , 2011 .

[4]  J. Gorman,et al.  Heart rate variability in depressive and anxiety disorders. , 2000, American heart journal.

[5]  Chun-Yao Huang,et al.  Effect of tiotropium on heart rate variability in stable chronic obstructive pulmonary disease patients. , 2015, Journal of aerosol medicine and pulmonary drug delivery.

[6]  Stefano Giordano,et al.  An improved DFA for fast regular expression matching , 2008, CCRV.

[7]  Manuel Blanco-Velasco,et al.  ECG signal denoising and baseline wander correction based on the empirical mode decomposition , 2008, Comput. Biol. Medicine.

[8]  Wan-Young Chung,et al.  Wireless Machine-to-Machine Healthcare Solution Using Android Mobile Devices in Global Networks , 2013, IEEE Sensors Journal.

[9]  Henry Been-Lirn Duh,et al.  A Wearable Sensing System for Tracking and Monitoring of Functional Arm Movement , 2011, IEEE/ASME Transactions on Mechatronics.

[10]  H. Bogte,et al.  Heart Rate Variability and Sustained Attention in ADHD Children , 1999, Journal of abnormal child psychology.

[11]  Joel J. P. C. Rodrigues,et al.  Toward ubiquitous mobility solutions for body sensor networks on healthcare , 2012, IEEE Communications Magazine.

[12]  Pau-Choo Chung,et al.  Mining Physiological Conditions from Heart Rate Variability Analysis , 2010, IEEE Computational Intelligence Magazine.

[13]  Chun-Yao Huang,et al.  Pulmonary rehabilitation improves heart rate variability at peak exercise, exercise capacity and health-related quality of life in chronic obstructive pulmonary disease. , 2014, Heart & lung : the journal of critical care.

[14]  Ig-Jae Kim,et al.  Activity Recognition Using Wearable Sensors for Elder Care , 2008, 2008 Second International Conference on Future Generation Communication and Networking.

[15]  Yang Hao,et al.  Detecting Vital Signs with Wearable Wireless Sensors , 2010, Sensors.

[16]  Mika P. Tarvainen,et al.  Software for advanced HRV analysis , 2004, Comput. Methods Programs Biomed..

[17]  Vladimir Medved,et al.  Standards for Reporting EMG Data , 2000, Journal of Electromyography and Kinesiology.

[18]  Fabrice Axisa,et al.  Flexible technologies and smart clothing for citizen medicine, home healthcare, and disease prevention , 2005, IEEE Transactions on Information Technology in Biomedicine.

[19]  Lizheng Shi,et al.  Whether New Cooperative Medical Schemes Reduce the Economic Burden of Chronic Disease in Rural China , 2013, PloS one.

[20]  Soo Dong Kim,et al.  A service-based approach to developing Android Mobile Internet Device (MID) applications , 2009, 2009 IEEE International Conference on Service-Oriented Computing and Applications (SOCA).

[21]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[22]  Pablo Laguna,et al.  QT variability and HRV interactions in ECG: quantification and reliability , 2006, IEEE Transactions on Biomedical Engineering.

[23]  Wen-Tsai Sung,et al.  Mobile Physiological Measurement Platform With Cloud and Analysis Functions Implemented via IPSO , 2014, IEEE Sensors Journal.

[24]  Chung-Chih Lin,et al.  Wireless Health Care Service System for Elderly With Dementia , 2006, IEEE Transactions on Information Technology in Biomedicine.

[25]  V. C. Padaki,et al.  Smart Vest: wearable multi-parameter remote physiological monitoring system. , 2008, Medical engineering & physics.

[26]  Kim Chang,et al.  Global Wireless Machine-to-Machine Standardization , 2011, IEEE Internet Computing.

[27]  George B. Moody,et al.  A robust open-source algorithm to detect onset and duration of QRS complexes , 2003, Computers in Cardiology, 2003.

[28]  WuYao-Kuang,et al.  Effect of Tiotropium on Heart Rate Variability in Stable Chronic Obstructive Pulmonary Disease Patients , 2015 .

[29]  Aleksandar Milenkovic,et al.  Journal of Neuroengineering and Rehabilitation Open Access a Wireless Body Area Network of Intelligent Motion Sensors for Computer Assisted Physical Rehabilitation , 2005 .

[30]  W. Zong,et al.  A QT interval detection algorithm based on ECG curve length transform , 2006, 2006 Computers in Cardiology.