Wearable Fabric μBrain Enabling On-Garment Edge-Based Sensor Data Processing

Advances in artificial intelligence(AI) is an enabler for innovative wearable on-garment edge driven AI intelligence. Such AI technologies are leveraging bio-inspired methods to develop spiking neural networks (SNN) that mimic the workings of the human brain to produce higher performance edge based processing capabilities. Such advancements are driving the development and emergence of neuromorphic computing with SNN architectural designs. Taking inspiration from these this paper proposes and demonstrates a wearable μBrain multi-layer SNN smart garment, with an event-driven artificial SNN embedded into the garments fabric. The fabric based multilayer SNN is interchangeable and scalable through its fabric hidden layer and fabric patch design and can be interconnected with textile based analogue monitoring sensors. The prototype was tested to check the functionality of the individual and collective nodes of the SNN as well as evaluating the wearable μBrain for fault tolerance scenarios (ripping and fraying) proving the prototype remained intact following the disconnection of fabric node Patch 5 and 8. Two application demonstrators were identified and tested. The first application focused on the detection of touch sensation on a forearm using the wearable μBrain, here real world use cases were checked against the expected output. Fluctuations were encountered when voltage output readings were above and below the set threshold level of 2.3Volts. The second application interfaced the wearable μBrain to live neurons to demonstrate how textile pressure sensors on the surface can lead to stimulation of neurons, when validated we observed an 11 second timeframe where the brain slice was stimulated.