NESL Technical Report #: 2022-5-2251
Authors:
Abstract: Wearable devices like smartwatches and smart wristbands have gained substantial popularity in recent years. However, due to the limited size of the touch screens, smartwatches typically have a poor interactive experience for users. Recently, new technology has converted the human body into a virtual interface using finger activity-induced vibrations. However, these solutions fail to meet expectations during real-world deployments, e.g., system performance significantly degrades due to human-based variations, such as hand shapes, tapping forces, and device positions. To mitigate these human-based variations, we collected a dataset of 114 users, built a deep-learning model, and designed a novel Siamese domain adversarial training algorithm. In this way, we implement a robust system that works at accuracy (97%) across different hand shapes, finger activity strengths, and smartwatch positions on the wrist. We have posted a demo video on YouTube (https://youtu.be/N5-ggvy2qfI).
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Publication Forum: Proceedings of the 13th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2022)
Date: 2022-05-04
Place: Milan, Italy
Publisher: ACM/IEEE
NESL Document?: Yes
Document category: Demonstration
Primary Research Area: Pervasive Computing
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