A Deep Learning Approach for Evaluating the Efficacy and Accuracy of PoseNet for Posture Detection

Abstract This paper investigates the posture identification capabilities of a deep learning framework known as PoseNet across different platforms, including ml5.js and JavaScript. The primary objective is to assess the accuracy and effectiveness of PoseNet's performance in identifying and interpreting human poses across different scenarios. Combining the adaptability and accessibility of JavaScript with PoseNet to develop web-based posture detection applications that are intuitive to users is the subject of this research. A series of comprehensive experiments were conducted, employing a varied dataset to evaluate the performance of the model across various environments. PoseNet has the potential to be a valuable tool for real-time applications due to its constant and dependable ability to identify poses, as demonstrated by our research. The research offers various perspectives on the pragmatic challenges associated with the implementation of deep learning models in digital environments. Additionally, the implementation challenges and limitations are assessed. The findings provide a substantial contribution to the expanding domain of accessible machine learning by emphasizing the feasibility and efficacy of using frameworks based on JavaScript to accomplish intricate assignments such as posture detection.