Using Artificial Intelligence Tools for Fitness Exercising

In the last few years there is a raise in use of Artificial Intelligence (AI) tools in everyday life. Although AI is getting better day by day, caution is needed when using it as an exercise tool. Animation is appropriate for PE teachers, coaches and students. Key cognitive level in this scenario is conditional (check chapter Scenarios), while scenario can be performed in 6 weeks.

This video demonstrates how to use AI Chatbots in the fitness monitoring and raise the learning process to a higher level with the help of prompts generated in the interactive report.

AI can serve as a valuable tool for exercise planning or seamlessly integrates into wearable devices. AI smart devices in sport domain are important since they can be used for medical diagnosis and treatment and can also serve as a motivation and support in improving health, physical fitness and consequently quality of life. By monitoring physical activity and daily movement behaviour, wearable smart devices harness the power of AI to generate personalised training plans targeting specific fitness component that we would like to improve such as cardio-respiratory endurance. These devices alert us if we deviate from prescribed pacing or heart rate zones.

Although AI is getting better day by day, caution needs to be taken when using them.

See also
References / Further reading
  1. Dhanasekaran, S., Andersen, A., Karlsen, R., Håkansson, A., & Henriksen, A. (2023). Data Collection and Analysis Methods for Smart Nudging to Promote Physical Activity: Protocol for a Mixed Methods Study. DOI: 10.3233/SHTI230293

  2. Nahavandi, D., Alizadehsani, R., Khosravi, A., & Acharya, U. R. (2022). Application of artificial intelligence in wearable devices: Opportunities and challenges. Computer Methods and Programs in Biomedicine, 213, 106541. DOI: 10.1016/j.cmpb.2021.106541

  3. Olyanasab, A., & Annabestani, M. (2024). Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review. Journal of Personalized Medicine, 14(2), 203. DOI: 10.3390/jpm14020203

     

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