Smatable: A Vibration-Based Sensing Method for Making Ordinary Tables Touch-Interfaces

Abstract

In recent years, the equipment that makes up smart homes is required not only to be functional, but also to be integrated with the design and aesthetics of the living space. Among them, interfaces that directly touch the human eye and hands are the key to maintaining design, but there were many issues in terms of integration with design and aesthetics of living spaces. In this paper, we propose an interface system that operates existing furniture by touching it as a new interface that integrates beautifully into the living space. The proposed system detects user operations with four small vibration sensors attached to hidden locations of existing furniture and uses deep learning to learn the vibrations when a person touches the furniture. Using this method, thick materials difficult to achieve with normal capacitive touch sensors can be utilized. In the experiment, a dining table was used as a representative piece of furniture, and the accuracy of detecting the direction in which three participants swiped in four directions on the table was verified. As a result of the experiment, the accuracy was confirmed by Leave-One-Person-Out-Cross-Validation using 3 sessions of swipe data for each individual for 3 participants, and the accuracy was 0.67. Furthermore, we verified the accuracy of a trained model created by adding only one session of evaluation target data to each learning dataset used in the Leave-One-Person-Out-Cross-Validation. As a result, the accuracy reached 0.90, achieving practical precision.

Keywords

• Touch interface
• operation recognition
• vibration sensor
• deep learning