Improving Posture Screening Using Generative AI and Reinforcement Learning

The problem

The iPlena-Omdena project aims to leverage Generative AI and Reinforcement Learning to facilitate posture screening and correction. The project seeks to make the process quick, easy, and accessible to all individuals. In today’s fast-paced world, we often juggle multiple responsibilities and spend a considerable amount of time sitting down, inadvertently ignoring posture-related issues until they develop into chronic back pain caused by postural abnormalities. The development of poor posture habits can occur subconsciously, as a result of prolonged periods spent slouching in a chair, hunching over a screen, or carrying heavy bags, among other things. These activities may also be regular occurrences in a work setting. Unfortunately, these habits can lead to long-term postural issues such as back discomfort, spinal dysfunction, joint degeneration, rounded shoulders, and so on.

The project goals

At present, iPlena possesses a vast image dataset and has created the minimum viable product (MVP) of the app, which includes the pose detection model, the recommendation algorithm, and the augmented reality (AR) spine model.

Project Scope:

• Use photogrammetry to enhance pose detection in diverse lighting conditions.
• Optimize the AR model by incorporating videos instead of pictures to improve input quality.
• Introduce continuous learning into pose detection and exercise recommendation models using Reinforcement Learning to enhance interactivity.
• Develop a posture prediction system utilizing a Generative Adversarial Network.

**More details will be shared once the project is joined.

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