Merlin is a Personal Fitness Trainer app which can help you to reach your fitness goals by providing personalized training advice and feedback in real time. It is designed to help you learn proper form and technique for various exercises and even allows you to customize and tailor your workout plans to your individual goals and needs.
Developing a pose recognition system that can accurately detect and analyze user movements in real-time presents a significant challenge. Providing instant feedback or results to users based on their performance requires efficient algorithms and processing capabilities. Ensuring low latency and seamless communication between the user's device and the server is crucial for real-time feedback delivery.
Maintaining accuracy even in different backgrounds and surroundings is a technical challenge. Variations in lighting conditions, cluttered backgrounds, and diverse user environments can impact the accuracy of pose detection algorithms.
Supporting multiple platforms requires developing a pose recognition library that can seamlessly integrate with different operating systems and device types. Ensuring consistent performance and functionality across various platforms while optimizing resource usage and user experience poses a technical challenge.
Integrating wearables like smartwatches to gather live data such as calorie burn and heart rate adds complexity to the development process. It should ensure seamless data synchronization between the pose recognition system and wearables, handling different data formats, and accurately processing and analyzing the live data.
To ensure real-time data transmission, the project utilized socket communication. This approach allowed for a reliable and efficient connection between the user's device and the server, enabling a seamless exchange of real-time data.
A proprietary algorithm was developed to accurately analyze pose data and evaluate specific conditions such as the positioning of hands and the required angle of leg separation. This custom algorithm enabled precise pose recognition, ensuring that users' exercise form was assessed correctly.
A voice-based feedback system was implemented to provide users with real-time guidance during their exercises. By leveraging speech synthesis technology, the system could convert feedback messages into audible instructions. This approach allowed users to receive feedback and instructions without the need to visually divert their attention, enabling a seamless workout experience.
Users are provided with initial voice-based instructions and exercise demos to assist them in performing the exercises correctly. These instructions are delivered at the beginning of each exercise routine, offering a demonstration of the proper technique and guidance on the correct form. By incorporating these initial voice-based messages, the project aimed to enhance users' understanding of the exercises and improve their overall performance.