Designing comfortable smart clothing: for infants’ health monitoring

This paper proposed a remote health monitoring system for the elderly based on smart home gateway. The proposed system consists of three parts: the smart clothing, the smart home gateway, and the health care server. The smart clothing collects the elderly’s electrocardiogram (ECG) and motion signals. The home gateway is used for data transmission. The health care server provides services of data storage and user information management; it is constructed on the Windows-Apache-MySQL-PHP (WAMP) platform and is tested on the Ali Cloud platform. To resolve the issues of data overload and network congestion of the home gateway, an ECG compression algorithm is applied. System demonstration shows that the ECG signals and motion signals of the elderly can be monitored. Evaluation of the compression algorithm shows that it has a high compression ratio and low distortion and consumes little time, which is suitable for home gateways. The proposed system has good scalability, and it is simple to operate. It has the potential to provide long-term and continuous home health monitoring services for the elderly.

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Realization and Technology Acceptance Test of a Wearable Cardiac Health Monitoring and Early Warning System with Multi-Channel MCGs and ECG

Sensors ◽

10.3390/s18103538 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3538 ◽

Cited By ~ 7

Author(s):

Wen-Yen Lin ◽

Hong-Lin Ke ◽

Wen-Cheng Chou ◽

Po-Cheng Chang ◽

Tsai-Hsuan Tsai ◽

...

Keyword(s):

Technology Acceptance ◽

Health Monitoring ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Left Ventricular ◽

Left Ventricular Ejection ◽

Cardiac Health ◽

Smart Clothing ◽

Monitoring And Early Warning

In this work, a wearable smart clothing system for cardiac health monitoring with a multi-channel mechanocardiogram (MCG) has been developed to predict the myo-cardiac left ventricular ejection fraction (LVEF) function and to provide early risk warnings to the subjects. In this paper, the realization of the core of this system, i.e., the Cardiac Health Assessment and Monitoring Platform (CHAMP), with respect to its hardware, firmware, and wireless design features, is presented. The feature values from the CHAMP system have been correlated with myo-cardiac functions obtained from actual heart failure (HF) patients. The usability of this MCG-based cardiac health monitoring smart clothing system has also been evaluated with technology acceptance model (TAM) analysis and the results indicate that the subject shows a positive attitude toward using this wearable MCG-based cardiac health monitoring and early warning system.

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Intelligent health monitoring system based on smart clothing

International Journal of Distributed Sensor Networks ◽

10.1177/1550147718794318 ◽

2018 ◽

Vol 14 (8) ◽

pp. 155014771879431 ◽

Cited By ~ 3

Author(s):

Chung-Chih Lin ◽

Chih-Yu Yang ◽

Zhuhuang Zhou ◽

Shuicai Wu

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Empirical Mode Decomposition ◽

Monitoring System ◽

Health Monitoring ◽

Hidden Markov ◽

Fall Detection ◽

Health Monitoring System ◽

Smart Clothing ◽

Mode Decomposition

In this study, we proposed an intelligent health monitoring system based on smart clothing. The system consisted of smart clothing and sensing component, care institution control platform, and mobile device. The smart clothing is a wearable device for electrocardiography signal collection and heart rate monitoring. The system integrated our proposed fast empirical mode decomposition algorithm for electrocardiography denoising and hidden Markov model–based algorithm for fall detection. Eight kinds of services were provided by the system, including surveillance of signs of life, tracking of physiological functions, monitoring of the activity field, anti-lost, fall detection, emergency call for help, device wearing detection, and device low battery warning. The performance of fast empirical mode decomposition and hidden Markov model were evaluated by experiment I (fast empirical mode decomposition evaluation) and experiment II (fall detection), respectively. The accuracy and sensitivity of R-peak detection using fast empirical mode decomposition were 96.46% and 98.75%, respectively. The accuracy, sensitivity, and specificity of fall detection using hidden Markov model were 97.92%, 90.00%, and 99.50%, respectively. The system was evaluated in an elderly long-term care institution in Taiwan. The results of the satisfaction survey showed that both the caregivers and the elders are willing to use the proposed intelligent health monitoring system. The proposed system may be used for long-term health monitoring.

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