Sengupta Debarun, Mastella Michele, Chicca Elisabetta, Kottapalli Ajay Giri Prakash
Department of Advanced Production Engineering (APE), Engineering and Technology Institute Groningen (ENTEG), University of Groningen, Groningen 9747 AG, The Netherlands.
Groningen Cognitive Systems and Materials Center (CogniGron), University of Groningen, Groningen 9747 AG, The Netherlands.
ACS Appl Electron Mater. 2022 Jan 25;4(1):308-315. doi: 10.1021/acsaelm.1c01010. Epub 2022 Jan 2.
During the past few decades, a significant amount of research effort has been dedicated toward developing skin-inspired sensors for real-time human motion monitoring and next-generation robotic devices. Although several flexible and wearable sensors have been developed in the past, the need of the hour is developing accurate, reliable, sophisticated, facile yet inexpensive flexible sensors coupled with neuromorphic systems or spiking neural networks to encode tactile information without the need for complex digital architectures, thus achieving true skin-like sensing with limited resources. In this work, we propose an approach entailing carbon nanofiber-polydimethylsiloxane composite-based piezoresistive sensors, coupled with spiking neural networks, to mimic skin-like sensing. The strain and pressure sensors have been combined with appropriately designed neural networks to encode analog voltages to spikes to recreate bioinspired tactile sensing and proprioception. To further validate the proprioceptive capability of the system, a gesture tracking smart glove, combined with a spiking neural network, was demonstrated. Wearable and flexible sensors with accompanying neural networks such as the ones proposed in this work will pave the way for a future generation of skin-mimetic sensors for advanced prosthetic devices, apparel integrable smart sensors for human motion monitoring, and human-machine interfaces.
在过去几十年里，大量的研究工作致力于开发受皮肤启发的传感器，用于实时人体运动监测和下一代机器人设备。尽管过去已经开发了几种柔性可穿戴传感器，但目前迫切需要开发出准确、可靠、精密、简便且廉价的柔性传感器，并与神经形态系统或脉冲神经网络相结合，以便在无需复杂数字架构的情况下对触觉信息进行编码，从而以有限的资源实现真正的类皮肤传感。在这项工作中，我们提出了一种方法，即采用基于碳纳米纤维 - 聚二甲基硅氧烷复合材料的压阻式传感器，并结合脉冲神经网络，来模拟类皮肤传感。应变和压力传感器已与经过适当设计的神经网络相结合，将模拟电压编码为脉冲，以重现受生物启发的触觉传感和本体感觉。为了进一步验证该系统的本体感觉能力，展示了一款结合脉冲神经网络的手势跟踪智能手套。像本文所提出的这种带有配套神经网络的可穿戴柔性传感器，将为下一代用于先进假肢设备的类皮肤传感器、用于人体运动监测的可集成到服装中的智能传感器以及人机接口铺平道路。
