The fast-paced and high-impact nature of martial arts makes judging these competitions challenging. Judgment accuracy is improved through the smartification of these sports, and athletes' training quality can also be quantitatively measured. Smartification enables the identification of strengths and weaknesses in athletes, allowing the coach to tailor precise training programs for them. A flexible and lightweight triboelectric nanogenerator (FL-TENG), which operates in single-electrode mode, was fabricated using polyethylene (PE) foam, silicone rubber, and carbon fiber (CF). The FL-TENG has the potential to be integrated as a self-powered sensor into martial arts training and protective equipment due to its similarity in constituent materials. It can detect the power and location of applied strikes, identifying weak strikes that do not score points or foul strikes. A sample of the FL-TENG measuring 4x4 cm 2 generates a voltage of 21.5 V under a force of 22 N. Since these self-powered sensors operate independently of external power sources, they can be used in large numbers and diverse dimensions in various equipment, enabling the generation of comprehensive and diverse datasets. Given the numerous advantages of artificial intelligence (AI) over human intelligence (HI), AI can be utilized to analyze these datasets. As a roadmap for incorporating artificial intelligence in the smartification of combat sports, 40 experimental matches were conducted, and 80 heat maps that include patterns of strikes from both winning and losing players were extracted. 80% of the maps were used for training, while the remaining 20% were identified with 93.75 % accuracy by Machine Learning (ML). The brilliant results obtained from this research herald a revolutionary breakthrough in smartifying combat sports.
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