New self-driven sensing array breaks through the diagnostic barrier of minor concussion

Statistics show that various sports and life accidents represented by skiing, boxing and rugby cause about 42 million people worldwide with minor concussions every year. However, minor concussions usually do not result in organic lesions, so computed tomography (CT) and magnetic resonance imaging (MRI) play a limited role in diagnosis, and the patient’s self-symptom description is the main source of information for the diagnosis of minor concussion. The lack of objective evaluation criteria and portable monitoring technology for the diagnosis of minor concussion has become the main problem in clinical diagnosis and treatment.

Based on this, the team of Academician Wang Zhonglin and Chen Baodong of the Beijing Institute of Nanoenergy and Systems, Chinese Academy of Sciences proposed a strategy to achieve real-time monitoring of head impact through a flexible curved sensing array composed of 3D printed multi-angle nanogenerators (TENG). This shows good prospects in the fields of personalized medicine, smart sports and aerospace. Recently, related papers were published in Science Advances.

Illustration of a self-driving sensing array application. Photo courtesy of interviewee

Nanogenerators show the advantages of sensing

Minor concussions occur frequently and may be accompanied by long-term cognitive, emotional, and physical sequelae. And the energy transfer of different types of impacts to the head will cause the brain to be cut, compressed, rotated and torn in the skull, resulting in different concussions. Therefore, it is necessary to objectively and accurately assess the degree and type of concussion.

Nanogenerators work using the coupling of friction, the electrical effects of pressure, and electrostatic induction. Therefore, the change of impact (friction) force can be judged from the change in the electrical efficiency of the circuit. Nanogenerators feature self-driven sensing, high sensitivity, and material diversity, making them ideal for providing active monitoring of static and dynamic pressure.

“Based on previous research, the team designed a wearable sensing array for position tracking and level evaluation of head impacts.” Chen Baodong told China Science News, “The array consists of 32 frictional nanogenerator units. Compared to other bulky and complex cabling solutions, the device is lighter, more flexible, and more portable. ”

This sensing array structure design not only helps the sensor to show the best performance, but also enables the friction nanogenerator to have the ability to move at multiple angles. In the experiment, the researchers used friction nanogenerators to collect mechanical energy from different directions and identify shear, rotational and compressive forces. After 30,000 duty cycles, it was found that the overall sensitivity decreased by only 2%.

“In addition, the sensor has ultra-high sensitivity and an ultra-wide pressure bandwidth.” Lulu, the first author of the paper and a doctoral student at the Beijing Institute of Nanoenergy and Systems, Chinese Academy of Sciences, explained, “Ultra-high sensitivity and ultra-wide pressure bandwidth are important performance indicators to measure the sensor’s perception ability and test range, our flexible sensing array can work normally in the range of external pressure (impact force) of 0~200 kilopascals (Kpa), if the voltage is used as the sensing signal, it can accurately distinguish pressure changes with an average sensitivity of 0.214 V/Kpa.” ”

Using 6 sensing metrics (stability, uniformity, linearity, repeatability, sensitivity, and hysteresis) to evaluate the sensor’s overall performance, the researchers found that friction nanogenerators can convert forces (compression, rotation, or shear) from multiple directions into electrical signals without an external power source. With a response time of 30 milliseconds and a minimum resolution of 1.415 kPa, it exhibits excellent sensing capabilities over a wide range of 0 to 200 kPa. In addition, the array can be visually mapped of head impacts through a wireless Bluetooth warning system. With the help of machine learning algorithms, it also shows an assessment of damage levels (with 98% accuracy).

“These data outperform other triboelectric and piezoelectric pressure sensors reported in the current literature.” “By collecting standardized data, we hope to build a big data platform to delve into the direct and indirect effects between head impacts and minor concussions in the future,” Chen said. ”

3D printing expands the application space

Although nanogenerators have obvious advantages in sensing, as an emerging sensor, there is still a lack of standard production processes, and manual products in the laboratory have become a major challenge for the large-scale application of this sensor. Advances in 3D printing technology not only make it possible to standardize the production of sensors produced by various materials (such as conductors, semiconductors and biomaterials), but also adapt to irregular body structures. Combined with 3D printing technology, it not only simplifies the processing of nanogenerators and reduces costs, but also allows integration into various application scenarios.

The researchers made the electrode wires into flexible printed circuit boards and provided upper and lower copper shields to reduce or minimize the effects of crosstalk. After applying pressure, an array of preloaded contact points can be imaged as letters “L”, “X” and “N” by rainbow color maps. By successfully demonstrating the pressure distribution of planar arrays, we are one step closer to the goal of curved sensing.

“The complex curvature of the head dictates that the sensing device must have precise surface geometry and stability. Therefore, we used reverse engineering, combined with 3D scanning to create a head point cloud, and 3D printed an ergonomic soft array that approximates the contour of a human head. Chen Baodong added, “3D printing provides a viable pathway for the commercialization of nanogenerators and shows good prospects in personalized medicine, intelligent sports and aerospace.” ”

Smart sports and wearable healthcare opportunities

To more accurately assess the degree of head impact, the team used deep convolutional neural networks (DCNNs) to analyze and identify multidimensional and massive amounts of data.

“DCNN can predict one or more response variables, with significant success in model prediction. The trained 6-fold cross-validation confusion matrix and prediction result confusion matrix show that the trained model has 100% classification accuracy for injury levels and 98% prediction accuracy for prediction sets. Lulu said.

Based on this, the team proposed a head-impact remote sensing system (HIRS) consisting of a friction nanogenerator sensing array, a data processing module, and a mobile terminal. In addition, a topologically optimized support structure was added to work with friction nanogenerator sensing arrays to create smart helmets better suited to reduce the effects of minor concussions. The results show that the head impact remote sensing system can quickly identify the injury area and provide accurate and intuitive recommendations before the clinical diagnosis of minor concussion, which helps to avoid delays in diagnosis and treatment.

“Using self-driving sensing arrays and machine learning methods to build DCNNs as decision-making models, and then creating a head-impact remote sensing system to provide predictive diagnostic reference in practical applications.” For example, in the case of an athlete’s injury, coaches and medical staff can judge whether a match needs to be terminated by the conclusions displayed on their smartphones. Chen Baodong said, “In addition, the breakthrough of fully 3D printed sensor technology makes the system suitable for pressure sensing equipment with various structures and different scenarios.” Therefore, this friction nanogenerator sensing array is expected to be widely used in the field of smart sports and wearable medicine. (Source: Zhang Shuanghu, China Science News)

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