Zihan Yan
MIT Media Lab, Cambridge, Massachusetts, United States
Ground surfaces are often carefully designed and engineered with various textures to fit the functionalities of human environments and thus could contain rich context information for smart wearables. Ground surface detection could power a wide array of applications including activity recognition, mobile health, and context-aware computing, and potentially provide an additional channel of information for many existing kinesiology approaches such as gait analysis. To facilitate the detection of ground surfaces, we present LaserShoes, a texture-sensing-enabled system using laser speckle imaging that can be retrofitted to shoes. Our system captures videos of speckle patterns induced on ground surfaces and uses pre-processing to identify ideal images with clear speckle patterns collected when users' feet are in contact with ground surfaces. We demonstrated our technique with a ResNet-18 model and achieved real-time inference. We conducted an evaluation in different conditions and demonstrated results that verified the feasibility.
https://doi.org/10.1145/3544548.3581344
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)