Graphene- and Graphite-Based Polyorganosiloxane Composite Ligaments for Sensory Feedback in Upper-Limb Prosthetics

The hand is a sophisticated enabler of many of our regular daily activities. When it is lost, e.g. due to disease or trauma, it has a significant effect on the amputee’s quality of life. Prosthesis users regularly report the desire for more sophisticated prosthesis technologies that provide sensory feedback to the body, and are more intuitive to use. This would lessen the requirement for visual feedback, for instance to determine if enough pressure has been applied to lift an object. Sensory feedback requires sensors that can respond to different stimulation in real-time. Graphene-based composites have many interesting electrical, mechanical and thermal properties, and have a conductivity that changes with applied pressure, movement (e.g. grasping) and temperature stimulation. We have developed a working proof of concept for a low-cost sensory feedback system using graphene-based composites and commercial-off-the-shelf technology. Prototype graphene- and graphite-based composite sensors, with electrical resistance ∼ 50MΩ and ∼ 10–100 kΩ respectively, were fabricated using a polyorganosiloxane matrix, and eight ligaments were mounted onto a reduced scale hand with four moving fingers each with three phalanges. Signals from pressure and movement stimuli show characteristic peaks at the start and end of the stimulation, which are not present in temperature stimulation. After stimulation, the signal from each sensor was digitized at ∼ 3 Hz and characterised by a bespoke processing, peak-finding and classifying algorithm running on a Raspberry Pi to provide real-time electrotactile stimulation ≤ 10 mA to the body. When identifying between different stimuli combinations, the algorithm has an accuracy score ∼ 95%. In this paper, we outline the synthesis of prototype graphene- and graphite-polyorganosiloxane composite sensors, and discuss the classifiying algorithm used to discriminate between different combinations of stimuli. We present initial results from our upper-limb prosthesis demonstrator, and outline further developments such as introducing the magnitude of the stimulation into the classifying algorithm, and the direct scalable chemical synthesis of other graphene- and graphite-composite sensors.