Battery-Free, Wireless, Cuff-Type, Multimodal Physical Sensor for Continuous Temperature and Strain Monitoring of Nerve.

Peripheral nerve injuries cause various disabilities related to loss of motor and sensory functions. The treatment of these injuries typically requires surgical operations for improving functional recovery of the nerve. However, capabilities for continuous nerve monitoring remain a challenge. Herein, a battery-free, wireless, cuff-type, implantable, multimodal physical sensing platform for continuous in vivo monitoring of temperature and strain from the injured nerve is introduced. The thin, soft temperature, and strain sensors wrapped around the nerve exhibit good sensitivity, excellent stability, high linearity, and minimum hysteresis in relevant ranges. In particular, the strain sensor integrated with circuits for temperature compensation provides reliable, accurate strain monitoring with negligible temperature dependence. The system enables power harvesting and data communication to wireless, multiple implanted devices wrapped around the nerve. Experimental evaluations, verified by numerical simulations, with animal tests, demonstrate the feasibility and stability of the sensor system, which has great potential for continuous in vivo nerve monitoring from an early stage to complete regeneration.

Battery-Free, Wireless, Ionic Liquid Sensor Arrays to Monitor Pressure and Temperature of Patients in Bed and Wheelchair.

Repositioning is a common guideline for the prevention of pressure injuries of bedridden or wheelchair patients. However, frequent repositioning could deteriorate the quality of patient's life and induce secondary injuries. This paper introduces a method for continuous multi-site monitoring of pressure and temperature distribution from strategically deployed sensor arrays at skin interfaces via battery-free, wireless ionic liquid pressure sensors. The wirelessly delivered power enables stable operation of the ionic liquid pressure sensor, which shows enhanced sensitivity, negligible hysteresis, high linearity and cyclic stability over relevant pressure range. The experimental investigations of the wireless devices, verified by numerical simulation of the key responses, support capabilities for real-time, continuous, long-term monitoring of the pressure and temperature distribution from multiple sensor arrays. Clinical trials on two hemiplegic patients confined on bed or wheelchair integrated with the system demonstrate the feasibility of sensor arrays for a decrease in pressure and temperature distribution under minimal repositioning.

Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information.

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.

Battery-free, wireless soft sensors for continuous multi-site measurements of pressure and temperature from patients at risk for pressure injuries.

Capabilities for continuous monitoring of pressures and temperatures at critical skin interfaces can help to guide care strategies that minimize the potential for pressure injuries in hospitalized patients or in individuals confined to the bed. This paper introduces a soft, skin-mountable class of sensor system for this purpose. The design includes a pressure-responsive element based on membrane deflection and a battery-free, wireless mode of operation capable of multi-site measurements at strategic locations across the body. Such devices yield continuous, simultaneous readings of pressure and temperature in a sequential readout scheme from a pair of primary antennas mounted under the bedding and connected to a wireless reader and a multiplexer located at the bedside. Experimental evaluation of the sensor and the complete system includes benchtop measurements and numerical simulations of the key features. Clinical trials involving two hemiplegic patients and a tetraplegic patient demonstrate the feasibility, functionality and long-term stability of this technology in operating hospital settings.