Sitting posture recognition by body pressure distribution and airbag regulation strategy based on seat comfort evaluation

Jiang, Yongxiang; Jingle, Duan; Deng, Sanpeng; Qi, Yuming; Wang, Peng; Wang, Zijing; Tianjiang, Zhang

doi:10.1049/joe.2018.9143

Cited by 20 publications

(12 citation statements)

References 12 publications

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“…All parameters have significant differences for different cushions except the contact area. In Ebe and Griffin (2001) and Yongxiang et al (2019) studies, the contact area also did not have a significant effect. Different shapes of cushions may affect the contact area more directly.…”

Section: Discussionmentioning

confidence: 80%

“…Among the many parameters in the sitting pressure distribution, the average pressure, peak pressure, contact area, and pressure gradient are more used in the comfort evaluation of the cushion ( Jackson et al, 2009 ; Smardzewski et al, 2014 ; Guo et al, 2016 ; Yongxiang et al, 2019 ). Zemp et al (2016) and Liu et al (2018) removed the repeated sitting pressure distribution parameters through correlation analysis and also screened out these four parameters as evaluation indicators.…”

Section: Introductionmentioning

confidence: 99%

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Comfort Evaluation of Slow-Recovery Ejection Seat Cushions Based on Sitting Pressure Distribution

Jiayi

Zhou

Wang

et al. 2021

Front. Bioeng. Biotechnol.

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Sitting discomfort not only affects the health of pilots carrying out long-endurance missions but also affects operational performance. The experimental objects included four ejection seat cushions: N1 was a fast-recovery foam as the comparison group, and the experimental groups were slow-recovery foams with different indentation force deflection (IFD), named N2 (hard), N3 (mid), and N4 (soft). The sitting comfort of 20 participants was tested on the four cushions by using subjective rating and sitting pressure distribution analysis. The results showed that compared with fast-recovery cushion N3 and N4 slow-recovery cushions have lower contact pressure and more uniform pressure distribution. Slow-recovery cushions that were too soft or too hard would reduce the comfort. No matter from the subjective rating or the analysis of the contact pressure data, the N3 cushion with a thickness of 3 cm and 65% IFD of 280 N had the highest comfort. In addition, the seat pressure distribution (SPD%) has a significant correlation with the subjective rating (p = 0.019, R = −0.98), which is more suitable for evaluating the comfort of the cushions. However, the slow-recovery cushions would show a decrease in support after a period of sitting, while the fast-recovery cushion could always maintain constant support.

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Section: Discussionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Comfort Evaluation of Slow-Recovery Ejection Seat Cushions Based on Sitting Pressure Distribution

Jiayi

Zhou

Wang

et al. 2021

Front. Bioeng. Biotechnol.

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“…Human posture detection is an active research area. There have been many studies to explored models to distinguish human lying and sitting postures [ 23 , 24 , 25 ]. In this section, we discuss the previous studies on lying posture tracking while using wearable accelerometer sensors.…”

Section: Background and Related Studiesmentioning

confidence: 99%

Pervasive Lying Posture Tracking

Alinia

Samadani

Milošević

et al. 2020

Sensors

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Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

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“…This method may be uncomfortable and low accurate. Experimental results of [ 16 ] shows that the overall accuracy of this method is 85.9%, and the accuracy rate reaches 83.33% in [ 17 ]. As an exploration and improvement, we study the method of sitting posture recognition based on the flexible array pressure sensor for real-time and accurate sitting posture recognition.…”

Section: Introductionmentioning

confidence: 99%

Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System

Cai

Zhao

Zhang

et al. 2021

Sensors

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As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of patients with spinal disease would be beneficial for their recovery. Accordingly, this paper designs and implements a sitting posture recognition system from a flexible array pressure sensor, which is used to acquire pressure distribution map of sitting hips in a real-time manner. Moreover, an improved self-organizing map-based classification algorithm for six kinds of sitting posture recognition is proposed to identify whether the current sitting posture is appropriate. The extensive experimental results verify that the performance of ISOM-based sitting posture recognition algorithm (ISOM-SPR) in short outperforms that of four kinds of traditional algorithms including decision tree-based (DT), K-means-based (KM), back propagation neural network-based (BP), self-organizing map-based (SOM) sitting posture recognition algorithms. Finally, it is proven that the proposed system based on ISOM-SPR algorithm has good robustness and high accuracy.

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Sitting posture recognition by body pressure distribution and airbag regulation strategy based on seat comfort evaluation

Cited by 20 publications

References 12 publications

Comfort Evaluation of Slow-Recovery Ejection Seat Cushions Based on Sitting Pressure Distribution

Comfort Evaluation of Slow-Recovery Ejection Seat Cushions Based on Sitting Pressure Distribution

Pervasive Lying Posture Tracking

Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System

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