Chen Wenqiang, Wang Ziqi, Quan Pengrui, Peng Zhencan, Lin Shupei, Srivastava Mani, Matusik Wojciech, Stankovic John
Massachusetts Institute of Technology USA.
University of California, Los Angeles USA.
Proc ACM Symp User Interface Softw Tech. 2023 Oct;2023. doi: 10.1145/3586183.3606794. Epub 2023 Oct 29.
Wearable devices like smartwatches and smart wristbands have gained substantial popularity in recent years. However, their small interfaces create inconvenience and limit computing functionality. To fill this gap, we propose ViWatch, which enables robust finger interactions under deployment variations, and relies on a single IMU sensor that is ubiquitous in COTS smartwatches. To this end, we design an unsupervised Siamese adversarial learning method. We built a real-time system on commodity smartwatches and tested it with over one hundred volunteers. Results show that the system accuracy is about 97% over a week. In addition, it is resistant to deployment variations such as different hand shapes, finger activity strengths, and smartwatch positions on the wrist. We also developed a number of mobile applications using our interactive system and conducted a user study where all participants preferred our un-supervised approach to supervised calibration. The demonstration of ViWatch is shown at https://youtu.be/N5-ggvy2qfI.
近年来，智能手表和智能手环等可穿戴设备大受欢迎。然而，它们的小界面带来不便并限制了计算功能。为了填补这一空白，我们提出了ViWatch，它能在不同的部署条件下实现强大的手指交互，并且仅依赖于消费级智能手表中普遍存在的单个惯性测量单元（IMU）传感器。为此，我们设计了一种无监督的暹罗对抗学习方法。我们在商用智能手表上构建了一个实时系统，并对一百多名志愿者进行了测试。结果表明，该系统在一周内的准确率约为97%。此外，它能抵抗不同的部署条件，如不同的手型、手指活动强度以及智能手表在手腕上的位置。我们还使用我们的交互系统开发了一些移动应用程序，并进行了一项用户研究，所有参与者都更喜欢我们的无监督方法而非有监督的校准方法。ViWatch的演示视频见https://youtu.be/N5-ggvy2qfI 。
