Recording, modelling and understanding tactile interactions is important in the study of human behaviour and in the development of applications in healthcare and robotics. However, such studies remain challenging because existing wearable sensory interfaces are limited in terms of performance, flexibility, scalability and cost. Here, we report a textile-based tactile learning platform that can be used to record, monitor and learn human–environment interactions. The tactile textiles are created via digital machine knitting of inexpensive piezoresistive fibres, and can conform to arbitrary three-dimensional geometries. To ensure that our system is robust against variations in individual sensors, we use machine learning techniques for sensing correction and calibration. Using the platform, we capture diverse human–environment interactions (more than a million tactile frames) and show that the artificial-intelligence-powered sensing textiles can classify humans’ sitting poses, motions and other interactions with the environment. We also show that the platform can recover dynamic whole-body poses, reveal environmental spatial information and discover biomechanical signatures.

记录、建模和理解触觉交互对于人类行为研究以及医疗保健和机器人技术应用的开发非常重要。然而，此类研究仍然具有挑战性，因为现有的可穿戴感官接口在性能、灵活性、可扩展性和成本方面受到限制。在这里，我们报告了一个基于纺织品的触觉学习平台，可用于记录、监控和学习人与环境的交互。触觉纺织品是通过数字机器编织廉价的压阻纤维制成的，并且可以符合任意三维几何形状。为了确保我们的系统对单个传感器的变化具有鲁棒性，我们使用机器学习技术进行传感校正和校准。使用平台，我们捕捉到不同的人与环境交互（超过一百万个触觉帧），并表明人工智能驱动的传感纺织品可以对人类的坐姿、动作和其他与环境的交互进行分类。我们还表明，该平台可以恢复动态全身姿势，揭示环境空间信息并发现生物力学特征。