ENGINEERING TECHNOLOGY

Edible material sensors are used in smart wearable products Zhang Hulin’s team at Taiyuan University of Technology has made new progress in the field of automotive sensing


Recently, Professor Zhang Hulin’s research group at the School of Information and Computer Science of Taiyuan University of Technology has made important research progress in the field of auto-driving sensing, and developed a baby intelligent monitoring system based on edible frictional electric hydrogel sensor network. For the first time, the study uses edible materials to manufacture pressure sensor chips, and develops a multi-channel data acquisition system, a proprietary deep learning algorithm and a mobile phone visualization program. The results were published in the top international journal Advanced Functional Materials (Impact Factor 19.924) under the title “Deep Learning Assisted Body Area Triboelectric Hydrogel Sensor Network for Infant Care”. Professor Zhang Hulin of Taiyuan University of Technology and Assistant Professor Chen Jun of the University of California, Los Angeles (UCLA) are co-corresponding authors, and doctoral student Guo Rui is the first author.

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Zhang Hulin and the research team in the laboratory. Courtesy of respondents

Monitor the infant’s stress around the clock

Infants and young children in the crawling period need to be accompanied by guardians continuously, and the camera tracking system developed on the market can only play an auxiliary effect, and cannot monitor the risk of infant bumping in real time.

Continuous monitoring of the biomechanical stresses on the baby’s body is essential to avoid injury to the baby.

Since 2019, Professor Zhang Hulin of Taiyuan University of Technology has begun to guide doctoral students to develop a flexible edible friction power generation hydrogel sensor to monitor the biomechanical pressure on the surface of the baby’s body. “We attached sensors to baby clothing and skin, and 11 pressure sensors covered key areas such as chest, hands, knees, feet, neck, back, wrists and hips. We pre-collect signals for specific movement patterns, such as turning over, holding babies, patting backs and clapping, and develop deep learning algorithms based on these signal characteristics can quickly and accurately identify infant movement patterns,” zhang hulin said. “Each flexible sensor features a high signal-to-noise ratio of 23.1 dB, high sensitivity of 0.28 V kPa-1, and fast response characteristics of 50 ms.”

The energy of the pulse signal sent by the sensor comes from tribological electricity, and the collection and transmission of the signal are automatically completed synchronously, which has the characteristics of passive drive and all-weather monitoring. However, pulse signals are extremely susceptible to interference, such as noise, friction between clothing and skin, and the footsteps of guardians, which can produce a large number of interference signals. In order to enhance the ability to accurately identify complex motion signals, Zhang Hulin’s team analyzed the coupling relationship between the applied force and electrical output through deep learning algorithms, analyzed the infant’s movement patterns, estimated the risk of infant falls, and achieved nearly 100% accurate identification of specific infant movement patterns, effectively avoiding the interference of noise signals.

In 2012, Academician Wang Zhonglin first invented the friction nano-generator, which converts mechanical energy into electrical energy in the nanoscale range, which is the smallest generator in the world. As a joint doctoral student of Academician Wang Zhonglin, Professor Zhang Hulin expanded the application scenarios of wearable clothing in this field.

New strategies for baby care

11 sensors, multi-channel data collection system, supporting deep learning algorithms and mobile app visualization programs… This self-powered body sensor network system can be directly attached to skin or clothing, and the sensitive unit can be large or small, but provides 24/7 security.

Speaking of the biggest achievement of this achievement, Professor Zhang Hulin said that the sensor uses a sandwich design, including gelatin, agar hydrogel and seaweed, all of which are edible materials, which is also the first edible material sensor developed in the field of nano-generators.

The outermost gelatin is an excellent alternative to traditional flexible friction power generation materials, rich in polyhydroxy structure, with high negative electron affinity, strong adhesion, and can be easily attached to the skin. The intermediate agar hydrogel acts as an electrode layer, and the seaweed is sandwiched between the agar hydrogel and gelatin to protect the gelatin from water erosion. The use of common food sources essentially eliminates the risks and consequences of accidental infant ingestion. This flexible biocompatible sensor has five performance indicators such as good adhesion, sensitivity, biocompatibility, robustness and edibleness, which is very suitable for intelligent monitoring of babies.

The sensor processed by lithography technology can be less than 100 microns in diameter, and the multi-channel data collection system has a minimum area of less than 2 square centimeters, which can be easily sewn into clothes. Supporting deep learning algorithm to the sensor to transmit the massive data analysis, there is no danger when there is no warning, if the pressure applied on the sensor exceeds the safety threshold, the pressure curve changes steeply, the App program can provide real-time early warning and one-click monitoring interaction.

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Professor Zhang Hulin and students in the laboratory. Courtesy of respondents

Sensor networks with multiple advantages such as no external power supply, safe and edible materials, and instant alarms offer promising strategies for reliable baby care in the IoT era. “In the next step, in addition to the pressure sensor, we can also add a temperature sensor, the 11 channels can also continue to be expanded, the mobile phone visualization program can also evolve, and we will also try to apply this result to the smart clothing of the elderly and the disabled.” Zhang Hulin imagined future application scenarios. (Source: China Science Daily Li Qingbo)

Related paper information:https://doi.org/10.1002/adfm.202204803



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