Estimation for Human Motion Posture and Health Using Improved Deep Learning and Nano Biosensor

Abstract

To improve the technical level of human motion posture and health estimation, a human motion posture and health estimation algorithm based on Nano biosensor and improved deep learning is proposed. First, we use Nano biological acceleration sensor and Nano biological angular velocity sensor to obtain human motion posture and health data. Second, after the fusion processing of human motion posture and health data, we use the motion posture coordinate system conversion unit and the physiological information recognition unit to convert the coordinate system of human motion angular velocity and acceleration data and recognize the physiological information of blood pressure and heart rhythm. Finally, the convolution neural networks (CNN) in deep learning is improved to obtain the deformable CNN. The transformed angular velocity, physiological information recognition results and other human posture data are input into the deformable CNN, and the human posture estimation results are output. Experiments show that proposed algorithm can accurately obtain human posture data, can quickly and accurately estimate human posture, and has a good application effect. It has important application value in identity recognition and sports performance analysis.