https://doi.org/10.1145/3498361.3538931
In this work, we propose FabToy, a plush toy instrumented with a 24-sensor array of fabric-based pressure sensors located beneath the surface of the toy to have dense spatial sensing coverage while maintaining the natural feel of fabric and softness of the toy. We optimize both the hardware and software pipeline to reduce overall power consumption while achieving high accuracy in detecting a wide range of interactions at different regions of the toy. Our con- tributions include a) sensor array fabrication to maximize coverage and dynamic range, b) data acquisition and triggering methods to minimize the cost of sampling a large number of channels, and c) neural network models with early exit to optimize power consumed for computation when processing locally and autoencoder-based channel aggregation to optimize power consumed for communi- cation when processing remotely. We demonstrate that we can achieve high accuracy of more than 83% for robustly detecting and localizing complex human interactions such as swiping, patting, holding, and tickling in different regions of the toy.