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7 September 2022 Optimizing sitting recognition models using preprocessing methods and improved ResNet
Zeen He, Sicong Cheng, Hongbin Zheng
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Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 1232912 (2022) https://doi.org/10.1117/12.2646764
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China
Abstract
Social progress has driven industrial transformation and upgrading, and numerous people from physical labor to mental labor so that the proportion of sedentary people is increasing year by year. Poor sitting posture and sedentary are important causes of sub-health today. The premise of correcting poor sitting posture is to identify human sitting posture using realtime monitoring devices such as smart chair. Therefore, it is of great significance to study a sitting posture recognition method. In this article, we propose a data pre-processing method and an improved ResNet for the recognition of sitting posture. Using Cross-mean Filtering and Extreme Value Processing Functions to preprocess data can effectively filter out data noise and reduce the probability of algorithm misclassification. Experiments show that it is easy to analyze the user's sitting posture using this method, and the probability of physiological left-right tilt being misclassified as poor sitting posture is reduced, which means that the improved method proposed in this article has obvious advantages in recognizing sitting posture.
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Zeen He, Sicong Cheng, and Hongbin Zheng "Optimizing sitting recognition models using preprocessing methods and improved ResNet", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 1232912 (7 September 2022); https://doi.org/10.1117/12.2646764
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KEYWORDS
Sensors
Data modeling
Detection and tracking algorithms
Data processing
Image processing
Machine vision
Statistical analysis
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