Making Vibration-based On-body Interaction Robust
2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)(2022)
摘要
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 which 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).
更多查看译文
关键词
On-body Interaction,Vibration Sensing,Domain Adaptation,Un-supervised Adversarial Training
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要