Self-Powered and Self-Functional Cotton Sock Using Piezoelectric and Triboelectric Hybrid Mechanism for Healthcare and Sports Monitoring.
By Minglu Zhu, Qiongfeng Shi, Tianyiyi He, Zhiran Yi, Yiming Ma, Bin Yang, Tao Chen, Chengkuo Lee. ACS Nano, 2019
399 citations recorded by the source corpus.
Wearable devices rely on hybrid mechanisms that possess the advantages of establishing a smarter system for healthcare, sports monitoring, and smart home applications. Socks with sensing capabilities can reveal more direct sensory information on the body for longer duration in daily life. However, the limitation of suitable materials for smart textile makes the development of multifunctional socks a major challenge. In this paper, we have developed a self-powered and self-functional sock (S2-sock) to realize diversified functions including energy harvesting and sensing various physiological signals, i.e., gait, contact force, sweat level, etc., by hybrid integrating poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS)-coated fabric triboelectric nanogenerator (TENG) and lead zirconate titanate (PZT) piezoelectric chips. An output power of 1.71 mW is collected from a PEDOT:PSS-coated sock with mild jumping at 2 Hz and load resistance of 59.7 MΩ. The study shows that cotton socks worn daily can potentially be a power source for enabling self-sustained socks comprising wireless transmission modules and integrated circuits in the future. We also investigate the influences of environmental humidity, temperature, and weight variations and verify that our S2-sock can successfully achieve walking pattern recognition and motion tracking for smart home applications. On the basis of the sensor fusion concept, the outputs from TENG and PZT sensors under exercise activities are effectively merged together for quick detection of the sweat level. By leveraging the hybrid S2-sock, we can achieve more functionality in the applications of foot-based energy harvesting and monitoring the diversified physiological signals for healthcare, smart homes, etc.
View Paper: https://www.semanticscholar.org/paper/72b374e118b64539367a0e800f0e30b5b8cbabcc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- Progress in TENG technology—A journey from energy harvesting to nanoenergy and nanosystem (2020, methodology): “The assembled PETENG monitors a wide variety of environmental and physiological factors such as humidity, temperature, and pressure variation during on foot motion.(127) Such ambulatory pattern monitoring capabilities were also investigated using PE-TENG,(297-299) allowing for data collection for ga”
- Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications (2020, background): “…sock, which provides the simple walking pattern recognition and motion-tracking information for smart home applications, but the reported sock can only distinguish gait patterns with large differences and the recognition accuracy is highly affectable by the environmental variations 46 .”