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

A popular method to estimate proximal/distal temperature (T(PROX) and T(DIST)) consists in calculating a weighted average of nine wireless sensors placed on pre-defined skin locations. Specifically, T(PROX) is derived from five sensors placed on the infra-clavicular and mid-thigh area (left and right) and abdomen, and T(DIST) 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 T(PROX) and T(DIST) 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 T(PROX) and T(DIST) 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 25(th) and 75(th) 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 T(PROX), 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 T(PROX) and T(DIST) estimates based on the number of available sensors; ii) select the most appropriate subset of sensors, depending on goals and operational constraints.