Human standing posture recognition based on CNN and pressure floor

Article type: Research Article

Authors: Li, Guan^{a; b} | Liu, Zhifeng^{a; *} | Cai, Ligang^a | Yan, Jun^a

Affiliations: [a] College of ME, Beijing University of Technology, Beijing 230031, China | [b] North China Institute of Science and Technology, Hebei 065201, China

Correspondence: [*] Corresponding author: Zhifeng Liu, College of ME, Beijing University of Technology, Beijing 230031, China. E-mail: [email protected].

Abstract: The goal of this study was to recognize human standing postures in human-robot collaborations such that the robot can serve the human operator better. An intelligent sensing floor was developed based on a thin-film pressure sensor and a human standing posture dataset was obtained by transforming the pressure data into a pressure image. A human standing posture recognition method based on an improved convolutional neural network is proposed. The results of the experiments demonstrate that a convolutional neural network can be used in the field of pressure images. The proposed method returned a recognition rate of 96.6%. Compared to the traditional neural network, the improved convolutional neural network model has better performance. The study results are expected to be used in standing posture monitoring to provide additional data for a robot in a human-robot collaboration system.

Keywords: Human posture recognition, pressure floor, CNN, HRC

DOI: 10.3233/JCM-193924

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 20, no. 2, pp. 489-498, 2020