Thermal evaluation of a chair with fans as an individually controlled system

Watanabe, Shinichi; Shimomura, Toshimichi; Miyazaki, Hisao

doi:10.1016/j.buildenv.2008.05.016

Cited by 72 publications

(34 citation statements)

References 10 publications

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“…The difference is that we can record behavior in a far less intrusive way than surveys. Personal Comfort System (PCS) such as a heated and cooled chair (hereinafter referred to as a PCS chair) provides local heating and/or cooling via embedded heating strips and fans ( Figure 1) [29][30][31]. With personally-owned thermal control devices such as PCS, we can trace the associated behavior back to individual occupants, creating a direct link to personal comfort.…”

mentioning

confidence: 99%

Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

Kim

Zhou

Schiavon

et al. 2018

Building and Environment

339

162

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mentioning

confidence: 99%

Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

Kim

Zhou

Schiavon

et al. 2018

Building and Environment

339

162

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“…It is obvious that the thermal properties (heat absorption and dissipation) of a cushion can play a vital role in the evaluation of sitting comfort [1][2][3][4]. However, thermal characteristics might also have a bearing on tissue viability and as a consequence the likelihood of skin ulcer formation [5].…”

Section: Introductionmentioning

confidence: 99%

Microenvironment temperature prediction between body and seat interface using autoregressive data-driven model

Liu

Lin

Luo

et al. 2015

Journal of Tissue Viability

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT AbstractThere is a need to develop a greater understanding of temperature at the skin-seat interface during prolonged seating from the perspectives of both industrial design (comfort/discomfort) and medical care (skin ulcer formation). Here we test the concept of predicting temperature at the seat surface and skin interface during prolonged sitting (such as required from wheelchair users). As caregivers are usually busy, such a method would give them warning ahead of a problem. This paper describes a data-driven model capable of predicting thermal changes and thus having the potential to provide an early warning (15-to 25-minute ahead prediction) of an impending temperature that may increase the risk for potential skin damages for those subject to enforced sitting and who have little or no sensory feedback from this area.Initially, the oscillations of the original signal are suppressed using the reconstruction strategy of empirical mode decomposition (EMD). Consequentially, the autoregressive data-driven model can be used to predict future thermal trends based on a shorter period of acquisition, which reduces the possibility of introducing human errors and artefacts associated with longer duration "enforced" sitting by volunteers. In this study, the method had a maximum predictive error of <0.4 o C when used to predict the temperature at the seat and skin interface 15 minutes ahead, but required 45 minutes data prior to give this accuracy. Although the 45 minutes front loading of data appears large (in proportion to the 15 minute prediction), a relative strength derives from the fact that the same algorithm could be used on the other 4 sitting datasets created by the same individual, suggesting that the period of 45 minutes required to train the algorithm is transferable to other data from the same individual. This approach might be developed (along with incorporation of other measures such as movement and humidity) into a system that can give caregivers prior warning to help avoid exacerbating the skin disorders of patients who suffer from low body insensitivity and disability requiring them to be immobile in seats for prolonged periods.

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“…They used the ASHRAE seven point thermal sensation scale, five point comfort scale and a two point acceptability scale. They concluded that the application of a ventilated chair improves local comfort of the occupants, also that occupants felt comfortable at 30C [39]. Figure 2.…”

Section: Previous Related Workmentioning

confidence: 99%

“…Watanabe et al (2009) suggests a user controlled system as a ventilated chair to improve occupant satisfaction with the thermal environment [39]. They provided an individually controlled ventilation system for the back and seat of the chair, as illustrated in Figure 2.…”

Section: Previous Related Workmentioning

confidence: 99%

“…Figure 2. Ventilated chair [39] Ventilated office chairs in hot climates were reported as successful to cool and to improve user comfort in experimental chambers [39][40][41], which is called ventilated [40] or cooled chair [41], as demonstrated in Figure 3. In this prototype, the air is drawn through the back and seat of the chair and it is released through the armrests so that the ventilation allows the user to cool down.…”

Section: Previous Related Workmentioning

confidence: 99%

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A user-controlled thermal chair for an open plan workplace: CFD and field studies of thermal comfort performance

et al. 2017

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Thermal evaluation of a chair with fans as an individually controlled system

Cited by 72 publications

References 10 publications

Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

Microenvironment temperature prediction between body and seat interface using autoregressive data-driven model

A user-controlled thermal chair for an open plan workplace: CFD and field studies of thermal comfort performance

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