To deal with situations of accidential swalloing by the infant, the soft triboelectric agar hydrogel biocompatible sensors are even edible.
Design of a soft edible triboelectric hydrogel sensor for infant care. a) Illustration of the body area sensor network system for infant motion monitoring. b) Schematic diagram of the soft edible triboelectric hydrogel sensor. c) Photograph of a seaweed. d) Comprehensive performance profile of the soft edible triboelectric hydrogel sensor. e) A schematic diagram of deep learning assisted body area triboelectric hydrogel sensor network for real-time infant care system. (Reprinted with permission by Wiley-VCH Verlag)
With the assistance of deep learning algorithms, the sensor network is able to promptly measure the mechanical pressure applied to an infant body, and continuously track its motion patterns. Once mechanical pressure exceeds a certain threshold and an abnormal motion pattern is recognized, a customized user-friendly cellphone application, also developed by the researchers, provides real-time warning and one-click guardian interaction.
The hydrogel sensor consists of a sandwiched structure comprising three layers of gelatin, agar hydrogel, and seaweed (see figure b above). Gelatin in the outermost layer is a superb alternative to conventional soft triboelectric materials such as polydimethylsiloxane as it is rich in polyhydroxy structure for high negative electronic affinities. Also, it readily adheres to the skin given its strong adhesion upon exposure to water. Intermediate agar hydrogel acts as an electrode layer, and seaweed is sandwiched between the agar hydrogel and gelatin to protect the gelatin from water erosion.
The mechanism of action of the soft edible triboelectric hydrogel sensor is a combination of triboelectric effect and electrostatic induction that converts biomechanical energy into electricity.
The sensor holds an signal-to-noise ratio of up to 23.1 dB, a response time of 50 ms, and a decent sensitivity of 0.28 V kPa−1.
With the assistance of infant self-powered body area sensor network and deep learning algorithms, this sensor network could realize the accurate and rapid measurement of applied mechanical pressure and recognition of multiple motion patterns of infants.
A customized cell phone application enables wireless transmission of proposed motion signals to a mobile phone for real-time warning and one-click guardian interaction.
