Body Pose Prediction Based on Motion Sensor Data and Recurrent Neural Network

Mixed reality environments give better chances to provide constant help to the people in need. Applied there artificial intelligence models will provide ad hoc monitoring measures, which may be the best chance to protect life in dangerous conditions. In this article, we present our research on mixed reality system developed to detect symptoms of unusual poses at work, home, or other environments. Recurrent neural network is using sensor readings to evaluate the situation by the minimum necessary number of body sensors working as safe indicators. Research results show that the developed system is working with very high accuracy of 99.89% using just two body sensors working in a separate mode. The system can work without any special infrastructure or development in various environments to help workers and elder people in dangerous situations.

Automated summary

Mixed reality environments offer better opportunities for providing constant assistance to those in need. Utilizing artificial intelligence models and recurrent neural networks, the system can detect symptoms of unusual poses with high accuracy of 99.89% using minimal body sensors, helping to protect life in dangerous conditions. This system can operate without the need for special infrastructure or development, providing support for workers and elderly individuals in various environments.