Abstract
This paper reports the uncertainty analysis of the temperature–resistance (TR) data of the newly developed temperature sensing fabric (TSF), which is a double-layer knitted structure fabricated on an electronic flat-bed knitting machine, made of polyester as a basal yarn, and embedded with fine metallic wire as sensing element. The measurement principle of the TSF is identical to temperature resistance detector (RTD); that is, change in resistance due to change in temperature. The regression uncertainty (uncertainty within repeats) and repeatability uncertainty (uncertainty among repeats) were estimated by analysing more than 300 TR experimental repeats of 50 TSF samples. The experiments were performed under dynamic heating and cooling environments on a purpose-built test rig within the temperature range of 20–50 °C. The continuous experimental data was recorded through LabVIEW-based graphical user interface. The result showed that temperature and resistance values were not only repeatable but reproducible, with only minor variations. The regression uncertainty was found to be less than ±0.3 °C; the TSF sample made of Ni and W wires showed regression uncertainty of <±0.13 °C in comparison to Cu-based TSF samples (>±0.18 °C). The cooling TR data showed considerably reduced values (±0.07 °C) of uncertainty in comparison with the heating TR data (±0.24 °C). The repeatability uncertainty was found to be less than ±0.5 °C. By increasing the number of samples and repeats, the uncertainties may be reduced further. The TSF could be used for continuous measurement of the temperature profile on the surface of the human body.
Highlights
In standard medical settings, the vital signs of the human body include body temperature, blood pressure, respiration, and electrocardiography (ECG) signals [1]
This article estimates the regression uncertainty in the experimental data acquired on a test rig; this will be helpful for the individual calibration of each temperature sensing fabric (TSF) sample
This article presents the analysis of uncertainties within experimental repeats the TR relationship of theofTSF
Summary
The vital signs of the human body include body temperature, blood pressure, respiration, and electrocardiography (ECG) signals [1]. Body temperature is the most measured vital sign and can be used for diagnostic purposes and for management of the disease process [2,3,4]. A temperature sensing fabric (TSF) has been developed in order to measure the human body temperature in nonclinical environments [5,6,7,8,9]. This article estimates the regression uncertainty in the experimental data (of TSF) acquired on a test rig; this will be helpful for the individual calibration of each TSF sample. A smart shirt a next-to-skin integrated with sensors is being used in uncertainty in is the experimental garment data (of TSF)
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