The complex wiring, bulky data collection devices, and difficulty in fast and on-site data interpretation significantly limit the practical application of flexible strain sensors as wearable devices. To tackle these challenges, this work develops an artificial intelligence-assisted, wireless, flexible, and wearable mechanoluminescent strain sensor system (AIFWMLS) by integration of deep learning neural network-based color data processing system (CDPS) with a sandwich-structured flexible mechanoluminescent sensor (SFLC) film. The SFLC film shows remarkable and robust mechanoluminescent performance with a simple structure for easy fabrication. The CDPS system can rapidly and accurately extract and interpret the color of the SFLC film to strain values with auto-correction of errors caused by the varying color temperature, which significantly improves the accuracy of the predicted strain. A smart glove mechanoluminescent sensor system demonstrates the great potential of the AIFWMLS system in human gesture recognition. Moreover, the versatile SFLC film can also serve as a encryption device. The integration of deep learning neural network-based artificial intelligence and SFLC film provides a promising strategy to break the “color to strain value” bottleneck that hinders the practical application of flexible colorimetric strain sensors, which could promote the development of wearable and flexible strain sensors from laboratory research to consumer markets.

复杂的布线、笨重的数据收集设备以及快速和现场数据解释的困难极大地限制了柔性应变传感器作为可穿戴设备的实际应用。为了应对这些挑战，这项工作通过将基于深度学习神经网络的颜色数据处理系统 （CDPS） 与三明治结构的柔性机械发光传感器 （SFLC） 薄膜集成，开发了一种人工智能辅助、无线、柔性和可穿戴的机械发光应变传感器系统 （AIFWMLS）。SFLC 薄膜具有卓越而坚固的机械发光性能，结构简单，易于制造。CDPS 系统可以快速准确地提取和解释 SFLC 薄膜的颜色与应变值，并自动校正由色温变化引起的误差，从而显着提高预测应变的准确性。智能手套机械发光传感器系统展示了 AIFWMLS 系统在人体手势识别方面的巨大潜力。此外，多功能 SFLC 胶片还可以用作加密设备。基于深度学习神经网络的人工智能与 SFLC 薄膜的集成为打破阻碍柔性比色应变传感器实际应用的“颜色到应变值”瓶颈提供了一种有前途的策略，这将促进可穿戴和柔性应变传感器从实验室研究向消费市场的发展。