Exploring Gestural Interaction with a Cushion Interface for Smart Home Control
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866912061981720576 |
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| author | Suzuki, Yuri Kato, Kaho Furui, Naomi Sakamoto, Daisuke Sugiura, Yuta |
| author_facet | Suzuki, Yuri Kato, Kaho Furui, Naomi Sakamoto, Daisuke Sugiura, Yuta |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04730 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Gestural Interaction with a Cushion Interface for Smart Home Control Suzuki, Yuri Kato, Kaho Furui, Naomi Sakamoto, Daisuke Sugiura, Yuta Human-Computer Interaction 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. |
| title | Exploring Gestural Interaction with a Cushion Interface for Smart Home Control |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.04730 |