Exploring Gestural Interaction with a Cushion Interface for Smart Home Control

TL;DR

This research designed user-defined gestures using cushion and developed gesture recognition system that was implemented using Convolutional Neural Networks and conducted an experiment to measure recognition accuracy.

Abstract

In this research, we aim to realize cushion interface for operating smart home. We designed user-defined gestures using cushion and developed gesture recognition system. We asked some users to make gestures using cushions for operating home appliances and determined user-defined gesture sets. We developed two methods for gesture identification. The First, We inserted sensor modules consisting of photo reflective sensors and acceleration sensor inside a cushion. The second, we embedded the acceleration sensor arrays in the cushion cover. Gesture recognizer was implemented using Convolutional Neural Networks (CNN). To evaluate our method, We conducted an experiment to measure recognition accuracy. Results showed that an average accuracy was 94.8% when training for each user, and an average accuracy of 91.3% when testing with a user that did not exist in the training data set.

Figures (10)

• Figure 1: Cushion experiment
• Figure 2: Types of gestures
• Figure 3: Principle of the build-in sensor Sugiura
• Figure 4: Sensor module (left), components (middle), and sensor module arrangement (right)
• Figure 5: Principle of the cover-type sensor