## 3 Results
[41] talked about an enhanced framework for personalized well-being recommendations that integrates fuzzy inference and evolutionary computing called F-EvoRecSys. The system improves the diversity of PA suggestions by considering user exercise habits. Through simulations and user studies, F-EvoRecSys demonstrates its effectiveness in offering more tailored and varied health recommendations. [PERSON] et al. [40] developed an automated system for personalized diet and exercise recommendations based on obesity classification. Using machine learning algorithms like DTC, the system classifies users' obesity levels and provides tailored diet and PA plans. The proposed system aims to enhance individual health by offering precise, data-driven recommendations to support weight management and overall well-being. Lastly, in [PERSON] et al. [39], \"Yourcare\", a diet and fitness recommendation system that leverages ML algorithms, is presented. It uses K-MC and RFC among others to provide personalized diet and PA plans based on user data like age, weight, and fitness goals. The system achieves high accuracy, particularly with the XGBC, for predicting optimal health recommendations. * Physical Activity and Sleep Quality. Under this label, only two works are located. The first one belongs to [PERSON] et al. [43], in which the development of RS for IoT-enabled Quantified-Self applications was presented, focusing on three approaches: Virtual Coach, Virtual Nurse, and Virtual Sleep Regulator. These systems provide personalized PA and sleep recommendations by analyzing user data from IoT devices. And the other one belongs to [PERSON] et al. [44], who developed an Explainable AI framework called ICARE, designed for context-aware health recommendations, particularly for improving sleep quality through PA. It uses data-mining algorithms to provide personalized suggestions. * Physical Activity, Physical Exercise, and Diet. Under this domain only one work is placed. This one was written by [PERSON] et al. [45], and presented \"SriHealth\", a mobile application designed for Sri Lankans, offering personalized meal plans, PA, and yoga schedules. Utilizing ML and natural language processing, the app tailors recommendations based on user preferences and health conditions. The system achieved high accuracy in providing these personalized health and wellness suggestions. * Physical Exercise. This domain includes four research projects. The first was made by [PERSON] et al. [49], and it talked about an interactive robot system designed to assist elderly individuals in performing daily physical activities at home called PHAROS, which uses advanced AI techniques, including deep learning, to monitor and recommend personalized PE based on the user's physical abilities and health status. The system aims to promote active aging and improve overall well-being. [PERSON] et al. [48] designed a RS to support a fitness assistance system using AI. The system employs ML algorithms, such as ANN and LR, to predict and suggest personalized workout routines. It aims to enhance user experience by providing tailored PE recommendations. In [PERSON] et al. [47], a RS that combines food and exercise ontologies to create personalized PE plans for individuals, particularly targeting overweight professionals. The system uses Python and ontological frameworks to tailor PE recommendations based on user-specific data, such as body measurements, exercise preferences, and medical history. And finally, [PERSON] et al. [46] developed an AI-based PE prescription system designed to guide individuals in adopting effective PE routines. The system uses data from an exercise community to recommend tailored PE modes and predict the impact on resting heart rate over one to three months. It achieves high accuracy in recommending exercise routines. * Physical Exercise and Diet. This domain contains seven investigations, starting with the one developed by [PERSON] et al. [56], which presented a Lifestyle RS based on the Framingham Heart Study and Clinical Decision Support System.