Sleep and Rhythms Projects

Expected accuracy of proximal and distal temperature estimated by wireless sensors, in relation to their number and position on the skin

Longato E, Garrido M, Saccardo D, Montesinos Guevara C, Mani A, Bolognesi M, Amodio P, Facchinetti A, Sparacino G, Montagnese S.


A popular method to estimate proximal/distal temperature (TPROX and TDIST) consists in calculating a weighted average of nine wireless sensors placed on pre-defined skin locations. Specifically, TPROX is derived from five sensors placed on the infra-clavicular and mid-thigh area (left and right) and abdomen, and TDIST from four sensors located on the hands and feet. In clinical practice, the loss/removal of one or more sensors is a common occurrence, but limited information is available on how this affects the accuracy of temperature estimates. The aim of this study was to determine the accuracy of temperature estimates in relation to number/position of sensors removed. Thirteen healthy subjects wore all nine sensors for 24 hours and reference TPROX and TDIST time-courses were calculated using all sensors. Then, all possible combinations of reduced subsets of sensors were simulated and suitable weights for each sensor calculated. The accuracy of TPROX and TDIST estimates resulting from the reduced subsets of sensors, compared to reference values, was assessed by the mean squared error, the mean absolute error (MAE), the cross-validation error and the 25th and 75th percentiles of the reconstruction error. Tables of the accuracy and sensor weights for all possible combinations of sensors are provided. For instance, in relation to TPROX, a subset of three sensors placed in any combination of three non-homologous areas (abdominal, right or left infra-clavicular, right or left mid-thigh) produced an error of 0.13°C MAE, while the loss/removal of the abdominal sensor resulted in an error of 0.25°C MAE, with the greater impact on the quality of the reconstruction. This information may help researchers/clinicians: i) evaluate the expected goodness of their TPROX and TDIST estimates based on the number of available sensors; ii) select the most appropriate subset of sensors, depending on goals and operational constraints.


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