چکیده مقاله
This study highlights the significant potential of Artificial Intelligence AI in enhancing exercise physiology applications A ۱۲ week AI guided training program led to notable improvements in key physiological parameters, including VO₂ Max, heart rate variability HRV , lactate threshold, and EMG activation, demonstrating AI’s effectiveness in personalized training Predictive AI models, particularly neural networks, accurately estimated performance improvements and identified injury risks, with high accuracy and low RMSE values These results suggest that AI can provide real time, data driven insights to help coaches and exercise physiologists make informed decisions that improve both performance and safety Beyond elite athletic performance, AI applications offer meaningful benefits for public health and chronic disease management By continuously monitoring physiological responses and delivering individualized exercise recommendations, AI can promote long term adherence to physical activity, supporting general health improvements However, the study has limitations The sample consisted of healthy adults aged ۱۸–۳۵, limiting the generalizability of findings to older populations, clinical patients, or sedentary individuals Additionally, reliance on wearable devices may introduce measurement variability, and individual physiological differences could affect outcomes Future research should focus on integrating AI with advanced wearable technologies, virtual reality VR , and augmented reality AR to develop immersive, adaptive training environments The creation of explainable AI XAI models will enhance transparency, enabling users to better understand AI driven recommendations and fostering trust in these systems AI represents a transformative tool in exercise physiology, providing innovative solutions for personalized training, injury prevention, and health promotion Its continued adoption has the potential to revolutionize both athletic performance and population level health interventions, paving the way for exercise prescriptions that are individualized, evidence based, and responsive to real time physiological data
کلیدواژهها
نویسندگان
فرشید گنجی
Academic Staff, mehraaeen Institute, Bandar Anzali
Sakineh Awazzadeh Samani
Secretary of Physical Education, Education and Training, Bandar Anzali
رزومه علمی کاربران
درباره سیویلیکاتبلیغات در سیویلیکاآمار علمی ایرانتقویم علمی ایرانداده کاوی مقالاتطرح های پژوهشی
شیوه ارجاع
در صورتی که می خواهید در اثر پژوهشی خود به این مقاله ارجاع دهید، به سادگی می توانید از عبارت زیر در بخش منابع و مراجع استفاده نمایید: Ganji، Farshid و Awazzadeh Samani، Sakineh،1404،Application of artificial intelligence in exercise physiology،اولین کنفرانس بین المللی فلسفه، تعلیم و تربیت و علوم ورزشی،تهران،https://civilica.com/doc/2595372
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی فلسفه، تعلیم و تربیت و علوم ورزشی30 بهمن 1404 · تهران