Waste textiles as the versatile triboelectric energy-harvesting platform for self-powered applications in sports and athletics

The triboelectric nanogenerator (TENG) has emerged as a promising technology for the conversion of mechanical energy into electrical energy. There is widespread development in the field of the internet of things and wireless sensor networks to realize future portable and wearable electronic devices. Sports science is a big sector to explore self-powered sensors to reduce the usage of batteries and complicated conventional sensors. The TENG can be one of the best alternatives to realize energy harvesting, impact monitoring, and performance evaluation of athletes in the area of intelligent sports. This work presents a new design for TENG by collecting worn-out textiles from the waste bin to useful self-powered applications in intelligent sports for the first time. The circular economy of the worn-out textile can promote the eco-friendly concept of recycling, reuse, and reducing for various energy devices. The waste materials were evaluated by various techniques such as structural, morphology, and FT-IR. The single electrode mode TENG was fabricated using the various positive triboelectric textile layers and negative triboelectric textile layers. The new device structure of 4 fingers knitted TENG (F-TENG) was able to deliver a peak to peak voltage of 4.2 V and peak-to-peak current output of 2.7 nA by simple stretching and releasing. Further, the energy harvesting from various sports activities was collated such as dribbling the ball, running, and stretching exercises. The digital signal processing techniques such as wavelet transform and wavelet packet transform were introduced to shed light upon the usage of TENG-based sensing units in sports facilities such as self-powered punching power recognition and edge ball judgment system. • Waste to energy approach- circular economy promotion from textile waste. • Single electrode mode TENG based on waste rubber/wool produces a power density output of 5.30 μW/cm2. • The knitted F-TENG showed an accuracy of 99.64% during practical exercise using ANN technique. • A self-powered sensor for intelligent sports and activity monitoring.