A Wearable H-shirt for Exercise ECG Monitoring and Individual Lactate Threshold Computing

	A Wearable H-shirt for Exercise ECG Monitoring and Individual Lactate Threshold Computing
	Fangmin Sun; Chenfu Yi; Weinan Li; Ye Li
刊名	Computers in Industry
	2017
文献子类	期刊论文
英文摘要	Millions of people worldwide are involved in daily physical exercise and fitness, which implies the growing demand for wearable devices and gadgets monitoring these activities. To ensure the comfort of exercise, wearable health monitoring devices are the most lucrative for real-time and continuous electrocardiogram (ECG) monitoring, which is essential for making individualized fitness plans and avoiding the occurrence of cardiac arrest. Moreover, the ECG monitoring data obtained are instrumental in the computation of the individual lactate threshold heart rate, which is the inflection point of the heart rate-time curve and indicates the exercise intensity level, at which the blood concentration of lactate and lactic acid begins to exponentially increase and will result in overstraining, unless the exercise efforts (running speed or muscle intensity) are temporarily reduced. The respective monitoring and overstrain-warning devices have a large potential for the individual assessment of the endurance performance, as well as for prescribing intensities in the endurance training. However, the common drawbacks of commercial wearable devices used in healthcare and medical fields for continuous monitoring of ECG, electroencephalogram, respiration, temperature, SpO2, and human activity, such as smart helmets, wearable patches, and activity-tracking bands, are motion artifacts and unstable skin-electrode contact, which jeopardize the accurate measurements during exercise. Given this, the Health shirt (H-shirt) integrated with conductive fabric ECG electrodes enabling the accurate ECG monitoring during physical exercise has been introduced and validated by the authors. In this paper, the modified design of the sports H-shirt with the lactate threshold heart rate computing is proposed and implemented. High elasticity of the H-shirt not only ensures a stable skin-electrode contact but also avoids the discomfort caused by the measurement. A highly integrated printed circuit board is used to filter, amplify, digitalize, and transmit the ECG signal sampled from the electrodes, while a high input impedance amplifier circuit is designed to improve its signal-to-noise ratio. The Bluetooth low energy (BLE) is used as the data transmission protocol between the H-shirt and the mobile phone. A mobile phone-based ECG analysis and exercise evaluation platform have been developed, which allow one to detect six types of abnormal ECG (tachycardia, bradycardia, a dropped beat, premature beat, bigeminy, and trigeminy) and individual lactate threshold, as well as to provide real-time health warning and exercise optimization hints through mobile phone voice messages. Being powered by a 150 mAh 3.7 V lithium battery, the H-shirt operated for at least 24 h and was tested on four volunteers of both genders and age from 14 to 32 years, whose ECG and the lactate threshold heart rate values were accurately measured during their test exercise on a treadmill with running speed up to 16 km/h and used as a reference for the users to adjust their exercise intensity. The available options of adding sensors for measurement of more physiological parameters by the proposed sports H-shirt make it quite lucrative for the rehabilitation exercise monitoring.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/12606]
专题	深圳先进技术研究院_数字所
作者单位	Computers in Industry
推荐引用方式 GB/T 7714	Fangmin Sun,Chenfu Yi,Weinan Li,et al. A Wearable H-shirt for Exercise ECG Monitoring and Individual Lactate Threshold Computing[J]. Computers in Industry,2017.
APA	Fangmin Sun,Chenfu Yi,Weinan Li,&Ye Li.(2017).A Wearable H-shirt for Exercise ECG Monitoring and Individual Lactate Threshold Computing.Computers in Industry.
MLA	Fangmin Sun,et al."A Wearable H-shirt for Exercise ECG Monitoring and Individual Lactate Threshold Computing".Computers in Industry (2017).