Upper Body Posture Recognition Using Inertial Sensors and Recurrent Neural Networks

Resumen

In this study, a wearable system that can recognize human posture was developed. By using long short-term memory-based recurrent neural network (LSTM-RNN) architecture, this system was able to classify posture with data measured by using an inertial measurement unit (IMU). Our results can serve as a reference for future developments of wearable systems in order to correct human posture and mitigate risks of spinal deformity.