Machine Learning Based Caloric Expenditure Prediction for Personalized Exercise Soundtracks

In today's world, with a growing awareness of maintaining a healthy lifestyle, exercise plays an important role. The expanding market of smart wearables and the inclusion of music into workouts, utilises the potential of technology to transform exercise. The domains of music and exercise cohesively have the potential to enhance exercise experience. However, there is a gap that exists between the two domains, with an absence of a platform that effectively combines both to yield a powerful system. This study proposes a novel machine learning solution that aims to bridge this gap, by utilising its user's real-time smartwatch data and music preferences to dynamically regulate the pace of exercise of the user through personalised music playlists. The proposed solution also provides a diet-centric chatbot based on Natural Language Processing, thus resulting in a wholistic health solution. This innovative approach aims to elevate user motivation, improve performance, and ultimately make exercise more enjoyable.