Inseer, Inc. is in the business of protecting people. The company wants to prevent injuries to workers, boosting productivity and keeping employees safe. Their approach uses an AI-powered ergonomics solution to proactively address issues before an injury occurs.

Recently, the company patented their human pose estimation technology using a complex and dynamic multi-headed convolutional attention. The machine learning-based solution tracks and analyzes human motion in real-time, providing unprecedented precision in generating 3D skeletal representations.

The apparatus works by processing a series of image frames that capture the movement of a subject. Using a trained limb segment machine-learning model, it identifies and maps the interrelations between different joints and limbs across these frames. This process results in a highly accurate temporal joints profile, which can be used to create detailed 3D models of human movement.

One of the key advancements in this invention is its ability to handle complex motion data from multiple angles simultaneously, thanks to its integration with multiple sensors. By combining data from these varied perspectives, the system generates a comprehensive and coherent representation of human motion, even in dynamic or non-staged environments.

This technology addresses the limitations of current 3D pose estimation systems, which often struggle with high computational demands and poor adaptability to real-world scenarios. Inseer’s approach simplifies the process, reducing noise and improving the smoothness of the 3D representations. This makes it an ideal tool for applications in areas such as sports analysis, physical therapy, virtual reality, and advanced human-computer interaction.

By pushing the boundaries of what’s possible with AI and machine learning, Inseer’s invention opens up new possibilities for understanding and utilizing human motion data with a level of accuracy and efficiency that was previously unattainable.

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