Abstract
Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research involved simple trials without any significant analytical algorithms. This study provides a way of recognizing human motion by combining textile stretch sensors based on single-walled carbon nanotubes (SWCNTs) and spandex fabric (PET/SP) and machine learning algorithms in a realistic application. In the study, the performance of the system will be evaluated by identification rate and accuracy of the motion standardized. This research aims to provide a realistic motion sensing wearable product without unnecessary heavy and uncomfortable electronic devices.
Highlights
Wearable technology, especially wearable sensors, has become mainstream these days, and attracted great interest from researchers
The results show that the gauge factor (GF) ranges from 4.1 to 8.5, and depends on the the single-walled carbon nanotubes (SWCNTs) nanostructure
The algorithm of the stretch sensor is simple and the performance of the monitoring model was enhanced by machine learning algorithms
Summary
Especially wearable sensors, has become mainstream these days, and attracted great interest from researchers. Most of the operating mechanisms of sensors are based on a relationship between some physical or chemical quantity such as temperature, pressure, stretch, light, sound, vibration, distance, humidity, pH, and electrical properties such as resistance, electromagnetism, or the capacitance of constituent conductive materials. According to this principle, a popular design approach for wearable sensors is to integrate electronic devices including temperature guage, stretch, proximity, accelerometry, and pulse-oximeter sensors into a small hard packet added on clothes, jewelry [2,8,9,10,11] or directly on the skin [12,13,14]. Dobkin et al [17]
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