{'fraction_non_alphanumeric': 0.01795472287275566, 'fraction_numerical': 0.00546448087431694, 'mean_word_length': 5.133971291866029, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Running Pace Adjustment and Training Distance Fitting with Fuzzy Logic and Machine Learning

A sedentary lifestyle and lack of sports favor the occurrence of many civilization diseases. To address the problem, the UN set 17 Sustainable Development Goals to be achieved glob-ally by 2030. They assume an enduring improvement in the life quality of present and future generations. One of the UN objects is “Goal 3: Good health and well-being”, focusing on ensuring a healthy life for all people and promoting well-being. An active lifestyle improves health by reducing the number and frequency of illnesses. This paper aims to develop an Artificial Intelligence (AI) system to provide training recommendations and evaluate decision-making algorithms for running pace adjustment and training distance fitting based on fuzzy logic. The data collected from running sessions enabled the construction of an AI system based on the data from the sports watch and personal feelings from the athlete regarding his emotions during each kilometer of the run. Comparing the system indications with information from the user due to fuzzy inference allowed a runner to increase endurance. Hence, using the provided recommendations, training can be intensified and training sensations - maintained.