Yongquan Hu
Hui-Shyong Yeo
Mingyue Yuan
Don Samitha Elvitigala
Wen Hu
Aaron Quigley
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The primary focus of this research is the discreet and subtle everyday contact interactions between mobile phones and their surrounding surfaces. Such interactions are anticipated to facilitate mobile context awareness, encompassing aspects such as dispensing medication updates, intelligently switching modes (e.g., silent mode), or initiating commands (e.g., deactivating an alarm). We introduce MicroCam, a contact-based sensing system that employs smartphone IMU data to detect the routine state of phone placement and utilizes a built-in microscope camera to capture intricate surface details. In particular, a natural dataset is collected to acquire authentic surface textures in situ for training and testing. Moreover, we optimize the deep neural network component of the algorithm, based on continual learning, to accurately discriminate between object categories (e.g., tables) and material constituents (e.g., wood). Experimental results highlight the superior accuracy, robustness and generalization of the proposed method. Lastly, we conducted a comprehensive discussion centered on our prototype, encompassing topics such as system performance and potential applications and scenarios.
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Supplemental movie, appendix, image and software files for, MicroCam: Leveraging Smartphone Microscope Camera for Context-Aware Contact Surface Sensing
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- MicroCam: Leveraging Smartphone Microscope Camera for Context-Aware Contact Surface Sensing
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