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Abstract Individuals who are unable to walk independently spend most of the day in a wheelchair. This population is at high risk for developing pressure injuries caused by sitting. However, early diagnosis and prevention of these injuries still remain challenging. Herein, we introduce battery-free, wireless, multimodal sensors and a movable system for continuous measurement of pressure, temperature, and hydration at skin interfaces. The device design includes a crack-activated pressure sensor with nanoscale encapsulations for enhanced sensitivity, a temperature sensor for measuring skin temperature, and a galvanic skin response sensor for measuring skin hydration levels. The movable system enables power harvesting, and data communication to multiple wireless devices mounted at skin-cushion interfaces of wheelchair users over full body coverage. Experimental evaluations and numerical simulations of the devices, together with clinical trials for wheelchair patients, demonstrate the feasibility and stability of the sensor system for preventing pressure injuries caused by sitting.

In this paper, we mathematically analyze bit-error rate (BER) and diversity order of physical-layer network coding (PNC) scheme in the two-way relay channel (TWRC) under the assumption of multiple antennas at relay node. We assume the quadrature phase shift keying (QPSK) modulation for simple data transmission in IoT applications, and max–min transmit antenna selection (TAS) scheme in the broadcast (BC) phase for achieving full diversity gain. In particular, we obtain the upper bound of BER at the multiple access (MA) phase, while deriving the exact BER performance at the BC phase. To the best our knowledge, the mathematical analysis in this paper is the first analytical results in the literature. Through computer simulations, the analytical results are validated.

Uninterrupted monitoring of pressure and temperature at skin interfaces can help to minimize the potential for pressure injuries in hospitalized or bedridden patients. Here, the authors introduce a soft, skin-mountable sensor that can continuously provide readings via antennas mounted under bedding, and demonstrate the functionality and robustness of the devices on patients.

Surface ozone is one of six air pollutants designated as harmful by National Ambient Air Quality Standards because it can adversely impact human health and the environment. Thus, ozone forecasting is a critical task that can help people avoid dangerously high ozone concentrations. Conventional numerical approaches, as well as data-driven forecasting approaches, have been studied for ozone forecasting. Data-driven forecasting models, in particular, have gained momentum with the introduction of machine learning advancements. We consider planetary boundary layer (PBL) height as a new input feature for data-driven ozone forecasting models. PBL has been shown to impact ozone concentrations, making it an important factor in ozone forecasts. In this paper, we investigate the effectiveness of utilization of PBL height on the performance of surface ozone forecasts. We present both surface ozone forecasting models, based on multilayer perceptron (MLP) and bidirectional long short-term memory (LSTM) models. These two models forecast hourly ozone concentrations for an upcoming 24-h period using two types of input data, such as measurement data and PBL height. We consider the predicted values of PBL height obtained from the weather research and forecasting (WRF) model, since it is difficult to gather actual PBL measurements. We evaluate two ozone forecasting models in terms of index of agreement (IOA), mean absolute error (MAE), and root mean square error (RMSE). Results showed that the MLP-based and bidirectional LSTM-based models yielded lower MAE and RMSE when considering forecasted PBL height, but there was no significant changes in IOA when compared with models in which no forecasted PBL data were used. This result suggests that utilizing forecasted PBL height can improve the forecasting performance of data-driven prediction models for surface ozone concentrations.