TY - GEN
T1 - Running Pace Adjustment and Training Distance Fitting with Fuzzy Logic and Machine Learning
AU - Dziomdziora, Adam
AU - Taibi, Davide
N1 - Publisher Copyright:
© 2022 IEEE.
jufoid=88355
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - fuzzy logic in running knowledge-based systems
KW - machine learning in sports activity
KW - recom-mendation system
U2 - 10.1109/ISCIT55906.2022.9931228
DO - 10.1109/ISCIT55906.2022.9931228
M3 - Conference contribution
AN - SCOPUS:85142381288
T3 - International Symposium on Communications and Information Technologies
SP - 189
EP - 194
BT - 2022 21st International Symposium on Communications and Information Technologies, ISCIT 2022
PB - IEEE
T2 - International Symposium on Communications and Information Technologies
Y2 - 27 September 2022 through 30 September 2022
ER -