“E-static Soft Sensors: A study on applying industrial manufacturing techniques to develop self-powered triboelectric textile sensors for high-accuracy activity recognition in smart homes”

E-static soft sensors is an investigation into manufacturing self-powered triboelectric textile sensors with regular materials (for e.g. wool, polyester and conductive yarns) for high-accuracy activity recognition in smart home applications. Triboelectricity is generated when materials such as human skin and synthetic fabrics repeatedly rub together.

This form of electricity is considered annoying or can even be dangerous.

However, when handled safely it can provide unique insights into our everyday interactions with textiles and present an environmentally-friendly solution for sensing different kinds of activities for smart home applications, such as fall detection for elderly care, assistance in indoor navigation or sports tracking and game design.Recent advancements in textile-based triboelectric nano generators(tTENG) have highlighted the rich potential of using this energy for powering low-power devices.

However, their sensing capabilities remain limited to a single repetitive movement.

Additionally, the current work in tTENG present one-off prototypes and are not at a stage where they can be manufactured on a larger scale.With an aim to tackle these challenges, the project develops an approach for constructing high-performance self-powered triboelectric sensors without the need for specialised materials using industrial production techniques of weaving, tufting, and embroidery.

These sensors will be evaluated and optimised for high accuracy self-powered activity and gesture recognition by applying advanced textile construction knowledge to optimise the structures and using AI models to fine tune the sensing analysis.

Additionally, their tactile and interactive properties would be evaluated in order to improve their suitability and acceptance of embedding them in everyday use-contexts.The results of the project, including the patterns files for the sensors would be documented online and shared openly for supporting reuse, adaption or commercialisation.