Better combination of thickness and hardness of mattress topper for supine sleeping posture: A physiological measurements evaluation

Yu-Chi, Lee; Chih-Yun, Lee; Wang, Mao-Jiun

doi:10.1016/j.ergon.2020.102979

Cited by 14 publications

(7 citation statements)

References 29 publications

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“…The deformation state and pressure dispersion of fabric subjected to curved surface pressure can be measured by using spherical compression. IFD 20 is the force required to produce the required indentation deformation of a specimen. It is included as a mandatory test for conventional padding materials – polyurethane flexible foam products, but can also be used for other materials such as springs or fiber mesh.…”

Section: Materials and Experimentsmentioning

confidence: 99%

Influence of spacer filament fineness on indentation characterizations of warp-knitted spacer fabrics for mattresses

Xia

Fang

et al. 2022

Journal of Engineered Fibers and Fabrics

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Mattress plays an important role in the quality of human sleep, warp-knitted spacer fabric (WKSF) is a rather important mattress material, this paper explored the indentation state of WKSFs of 20 mm thickness and 0.12, 0.15, 0.18, and 0.25 mm spacer filaments under the action of curved surface pressure by using spherical compression tests and finite element simulations, with the combination of three-dimensional and non-uniform pressure characteristics exerted on the mattress by the human body when lying down. Tests and simulations analysis would be shown in four aspects: firstly, when the spherical indenter acts on the WKSFs, the compressive stress, Indentation Force Deflection (IFD) and support factor (SF) of the WKSFs increase as the spacer filament fineness increases; secondly, the compression depth of WKSFs should be controlled within 40% when used as mattresses to obtain good compression comfort; thirdly, A warp-knitted spacer fabric with a 0.25 mm spacer filament fineness provides both comfort and pressure resistance and better meets the needs of most people for a spacer fabric for mattresses.

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Section: Materials and Experimentsmentioning

confidence: 99%

Influence of spacer filament fineness on indentation characterizations of warp-knitted spacer fabrics for mattresses

Xia

Fang

et al. 2022

Journal of Engineered Fibers and Fabrics

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“…In particular, in terms of human biomechanics 6 – 8 , the shoulders, back, and hips serve as the main support points of the body 6 , and their positions and corresponding pressure distributions contribute to the curvature of the spine and the overall stability of the sleep posture 7 , 8 . Sleep physiology studies 9 – 12 have shown that shoulder-hip pressures and spinal pressures can provide information about whether an individual maintains a sleep posture that reduces body stress and supports the natural curve of the spine 10 , 11 , which is critical for understanding sleep quality and preventing sleep-related disorders 12 . This has led to theoretical and practical research support for considering shoulder-hip eigenquantities and spine eigenquantities as key eigenquantities in sleep position recognition.…”

Section: Introductionmentioning

confidence: 99%

A dual fusion recognition model for sleep posture based on air mattress pressure detection

Li,

Zhou,

Zhou

2024

Sci Rep

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In order to solve the difficult portability problem of traditional non-invasive sleeping posture recognition algorithms arising from the production cost and computational cost, this paper proposes a sleeping posture recognition model focusing on human body structural feature extraction and integration of feature space and algorithms based on a specific air-spring mattress structure, called SPR-DE (SPR-DE is the Sleep Posture Recognition-Data Ensemble acronym form). The model combines SMR (SMR stands for Principle of Spearman Maximal Relevance) with horizontal and vertical division based on the barometric pressure signals in the human body’s backbone region to reconstruct the raw pressure data into strongly correlated non-image features of the sleep postures in different parts and directions and construct the feature set. Finally, the recognit-ion of the two sleep postures is accomplished using the AdaBoost-SVM integrated classifier. SPR-DE is compared with the base and integrated classifiers to verify its performance. The experimental results show that the amount of significant features helps the algorithm to classify different sleeping patterns more accurately, and the f1 score of the SPR-DE model determined by the comparison experiments is 0.998, and the accuracy can reach 99.9%. Compared with other models, the accuracy is improved by 2.9% ~ 7.7%, and the f1-score is improved by 0.029 ~ 0.076. Therefore, it is concluded that the SMR feature extraction strategy in the SPR-DE model and the AdaBoost-SVM can achieve high accuracy and strong robustness in the task of sleep posture recognition in a small area, low-density air-pressure mattress, taking into account the comfort of the mattress structural design and the sleep posture recognition, integrated with the mattress adaptive adjustment system.

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“…This necessitates the harmonious spinal alignment 11 , 12 , appropriate joint position 13 , along with the promotion of muscle relaxation throughout the body 14 . Researchers have conducted extensive biomechanical research on sleeping positions 5 , 6 , such as conducting subjective surveys on preferred sleeping positions among people; using X-ray techniques, analyze the differences in spinal alignment between sleeping position and standing position 4 , 15 ; evaluate the effect of different mattresses on sleep comfort using surface electromyography (sEMG) technology 16 – 20 . Research has shown that the differences in internal muscle and joint forces caused by distinct mattresses supports are one of the primary contributors affecting sleep comfort in the human body 21 – 24 .…”

Section: Introductionmentioning

confidence: 99%

A method for calculating vector forces at human-mattress interface during sleeping positions utilizing image registration

Gao,

Zhang,

Zou

et al. 2024

Sci Rep

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The vector forces at the human-mattress interface are not only crucial for understanding the distribution of vertical and shear forces exerted on the human body during sleep but also serves as a significant input for biomechanical models of sleeping positions, whose accuracy determines the credibility of predicting musculoskeletal system loads. In this study, we introduce a novel method for calculating the interface vector forces. By recording indentations after supine and lateral positions using a vacuum mattress and 3D scanner, we utilize image registration techniques to align body pressure distribution with the mattress deformation scanning images, thereby calculating the vector force values for each unit area (36.25 mm × 36.25 mm). This method was validated through five participants attendance from two perspectives, revealing that (1) the mean summation of the vertical force components is 98.67% ± 7.21% body weight, exhibiting good consistency, and mean ratio of horizontal component force to body weight is 2.18% ± 1.77%. (2) the predicted muscle activity using the vector forces as input to the sleep position model aligns with the measured muscle activity (%MVC), with correlation coefficient over 0.7. The proposed method contributes to the vector force distribution understanding and the analysis of musculoskeletal loads during sleep, providing valuable insights for mattress design and evaluation.

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Better combination of thickness and hardness of mattress topper for supine sleeping posture: A physiological measurements evaluation

Cited by 14 publications

References 29 publications

Influence of spacer filament fineness on indentation characterizations of warp-knitted spacer fabrics for mattresses

Influence of spacer filament fineness on indentation characterizations of warp-knitted spacer fabrics for mattresses

A dual fusion recognition model for sleep posture based on air mattress pressure detection

A method for calculating vector forces at human-mattress interface during sleeping positions utilizing image registration

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