AI-based personalized exercise and health recommendation system using hybrid modeling

Introduction Growing rate of sedentary living and chronic diseases has brought to light the necessity of smart systems that are able to provide individualized fitness instructions. Methods This paper suggests a hybrid health and fitness recommendation system, which incorporates machine learning models that are supervised alongside a rule-based reasoning system to produce personalized fitness and exercise recommendations. The system interprets structured user information such as demographic variables, body composition, cardiovascular variables, exercise habits, and experience to categorize the intensity of activities, deduce fitness objectives and estimate calorie loss. Evaluation algorithms based on multiple regression and classification were tested to predict the estimate of energy expenditure as well as classify the level of activity, whilst a goal inference engine turned predictive results into recommendations that could be acted upon. Results The experiments showed a high level of prediction and classification of activities, which proved the effectiveness of integrating physiological indicators of behavioral features. Discussion The suggested hybrid method enhances explanatory ability and decreases reliance on continuous data of wearable sensors thus it can be used in the real world to implement in the mobile health application and fitness management system. The system offers individualized exercise plans based on ability of the user and preventive healthcare through safe and sustainable exercise. Altogether, the research develops a scalable and interpretable intelligent recommendation system that can help to engage and lead to healthier lifestyles.