Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA, 02139, USA.
LightSpeed Studios, 12777 W Jefferson Boulevard, Los Angeles, CA, 90066, USA.
Nat Commun. 2024 Jan 29;15(1):868. doi: 10.1038/s41467-024-45059-8.
Human-machine interfaces for capturing, conveying, and sharing tactile information across time and space hold immense potential for healthcare, augmented and virtual reality, human-robot collaboration, and skill development. To realize this potential, such interfaces should be wearable, unobtrusive, and scalable regarding both resolution and body coverage. Taking a step towards this vision, we present a textile-based wearable human-machine interface with integrated tactile sensors and vibrotactile haptic actuators that are digitally designed and rapidly fabricated. We leverage a digital embroidery machine to seamlessly embed piezoresistive force sensors and arrays of vibrotactile actuators into textiles in a customizable, scalable, and modular manner. We use this process to create gloves that can record, reproduce, and transfer tactile interactions. User studies investigate how people perceive the sensations reproduced by our gloves with integrated vibrotactile haptic actuators. To improve the effectiveness of tactile interaction transfer, we develop a machine-learning pipeline that adaptively models how each individual user reacts to haptic sensations and then optimizes haptic feedback parameters. Our interface showcases adaptive tactile interaction transfer through the implementation of three end-to-end systems: alleviating tactile occlusion, guiding people to perform physical skills, and enabling responsive robot teleoperation.
用于在时间和空间上捕获、传递和共享触觉信息的人机接口在医疗保健、增强和虚拟现实、人机协作和技能发展方面具有巨大的潜力。为了实现这一潜力，此类接口应该是可穿戴的、不引人注目的，并且在分辨率和身体覆盖范围方面具有可扩展性。为了朝着这一愿景迈出一步，我们提出了一种基于纺织品的可穿戴人机接口，该接口具有集成的触觉传感器和振动触觉执行器，这些传感器和执行器经过数字设计并快速制造。我们利用数字刺绣机以可定制、可扩展和模块化的方式将压阻式力传感器和振动触觉执行器阵列无缝嵌入纺织品中。我们使用该工艺来制作可以记录、再现和传递触觉交互的手套。用户研究调查了人们如何感知我们的手套与集成的振动触觉执行器再现的感觉。为了提高触觉交互传输的有效性，我们开发了一个机器学习管道，该管道自适应地对每个用户对触觉感觉的反应进行建模，然后优化触觉反馈参数。我们的接口通过实施三个端到端系统展示了自适应触觉交互传输：减轻触觉遮挡、指导人们执行物理技能以及实现响应式机器人遥操作。
