Sitting Posture Recognition in Real-Time Combined with Index Map and BLS

Sun, Wanting; Zhou, Ze; Liu, Hongjun

doi:10.1145/3319921.3319955

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…Zhu et al [24] proposed a spatiotemporal descriptor method to detect action events in complex scenes. Sun et al [25] proposed a real-time sitting posture recognition algorithm based on Index Graph and BLS model, and proposed a double threshold cascade algorithm for the case of too many frames in the video. Jansen et al [26] designed a disposable stretch skin sensor, which could be used for body position monitoring, rehabilitation feedback and detailed motion monitoring in the process of exercise and fitness.…”

Section: Posture Recognitionmentioning

confidence: 99%

“…Moreover, some models were complicated or required an amount of computation [9], [31], [33], [34]. And what's more, most did not explore the posture characteristics from static and dynamic perspectives [1], [9], [10], [21]- [23], [25], [30], [31], [33]- [36], [39]- [41]. In order to reduce the complexity of posture recognition, the skeleton model was simplified, and characteristic consistent distance values were generated for the static and dynamic posture recognitions.…”

Section: Posture Recognitionmentioning

confidence: 99%

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Posture Recognition Technology Based on Kinect

Chu

Xin

2020

IEICE Trans. Inf. & Syst.

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Aiming at the complexity of posture recognition with Kinect, a method of posture recognition using distance characteristics is proposed. Firstly, depth image data was collected by Kinect, and threedimensional coordinate information of 20 skeleton joints was obtained. Secondly, according to the contribution of joints to posture expression, 60 dimensional Kinect skeleton joint data was transformed into a vector of 24-dimensional distance characteristics which were normalized according to the human body structure. Thirdly, a static posture recognition method of the shortest distance and a dynamic posture recognition method of the minimum accumulative distance with dynamic time warping (DTW) were proposed. The experimental results showed that the recognition rates of static postures, non-cross-subject dynamic postures and cross-subject dynamic postures were 95.9%, 93.6% and 89.8% respectively. Finally, posture selection, Kinect placement, and comparisons with literatures were discussed, which provides a reference for Kinect based posture recognition technology and interaction design.

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Section: Posture Recognitionmentioning

confidence: 99%

Section: Posture Recognitionmentioning

confidence: 99%

Posture Recognition Technology Based on Kinect

Chu

Xin

2020

IEICE Trans. Inf. & Syst.

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“…Afterwards the postures are classified into 15 different categories. A real time posture recognition system is proposed in [14], where a Kinect camera is used to collect point cloud data. Afterwards, the frames are classified into 8 different categories.…”

Section: Introductionmentioning

confidence: 99%

Automated Quantification of Occupant Posture and Shoulder Belt Fit Using Safety Specific Key Points

Hartleitner

Koppisetty

Bohman

2022

IEEE Open J. Intell. Transp. Syst.

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Virtual evaluation of automotive safety with variation in occupant posture and shoulder belt fit is gaining importance, and there is a need of methods facilitating analysis of occupant postures in driving studies. This study is aimed to develop an AI-based computer vision method to automatically quantify occupant posture and shoulder belt position over time in a car. Traceable defined key points on the occupant were related with the shoulder belt and quantified over time in real 3D coordinates by predefined key measurements, utilising the underlying spatial information of a Intel RealSense 3D Camera. The key points are defined as traceable key points relevant to relate the occupant to the vehicle environment and to estimate shoulder belt position. Key point prediction results suggest an average deviation of around 1cm per coordinate, which enable a reliable spatial categorisation of the respective tracked occupant by analysing the key measurements. This method providing continuous information of the occupant position and belt fit will be useful to identify common occupant postures as well as more extreme postures, to be used for expanding variations in postures for vehicle safety assessments.

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Abnormal sitting posture recognition based on multi-scale spatiotemporal features of skeleton graph

Yang

et al. 2023

Engineering Applications of Artificial Intelligence

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Sitting Posture Recognition in Real-Time Combined with Index Map and BLS

Cited by 8 publications

References 18 publications

Posture Recognition Technology Based on Kinect

Posture Recognition Technology Based on Kinect

Automated Quantification of Occupant Posture and Shoulder Belt Fit Using Safety Specific Key Points

Abnormal sitting posture recognition based on multi-scale spatiotemporal features of skeleton graph

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