Modelling Proper and Improper Sitting Posture of Computer Users Using Machine Vision for a Human–Computer Intelligent Interactive System during COVID-19

Estrada, Jheanel E.; Vea, Larry A.; Devaraj, Madhavi

doi:10.3390/app13095402

Cited by 1 publication

(1 citation statement)

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“…Kim et al used the skeletal posture estimated by the deep learning method Mediapipe as input and used the fast optimization method uDEAS (the univariate dynamic encoding algorithm for searches) to propose a 3D human posture estimation system to monitor whether elderly people living alone have fallen [13]. Estrada et al used key human body points detected by Mediapipe to further identify correct and incorrect sitting postures while working from home [14]. In addition, other deep models have also achieved remarkable results in key point detection.…”

Section: Introductionmentioning

confidence: 99%

Light-Adaptive Human Body Key Point Detection Algorithm Based on Multi-Source Information Fusion

Hu,

Zhang,

Wang

et al. 2024

Sensors

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The identification of key points in the human body is vital for sports rehabilitation, medical diagnosis, human–computer interaction, and related fields. Currently, depth cameras provide more precise depth information on these crucial points. However, human motion can lead to variations in the positions of these key points. While the Mediapipe algorithm demonstrates effective anti-shake capabilities for these points, its accuracy can be easily affected by changes in lighting conditions. To address these challenges, this study proposes an illumination-adaptive algorithm for detecting human key points through the fusion of multi-source information. By integrating key point data from the depth camera and Mediapipe, an illumination change model is established to simulate environmental lighting variations. Subsequently, the fitting function of the relationship between lighting conditions and adaptive weights is solved to achieve lighting adaptation for human key point detection. Experimental verification and similarity analysis with benchmark data yielded R2 results of 0.96 and 0.93, and cosine similarity results of 0.92 and 0.90. With a threshold range of 8, the joint accuracy rates for the two rehabilitation actions were found to be 89% and 88%. The experimental results demonstrate the stability of the proposed method in detecting key points in the human body under changing illumination conditions, its anti-shake ability for human movement, and its high detection accuracy. This method shows promise for applications in human–computer interaction, sports rehabilitation, and virtual reality.

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Section: Introductionmentioning

confidence: 99%