A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning Techniques
The use of mobile devices, especially smartphones, has become popular in recent years. There is an increasing need for cross-device interaction techniques that seamlessly integrate mobile devices and large display devices together. This paper develops a novel cross-device cursor position system that...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/5/1665 |
| _version_ | 1797416852817707008 |
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| author | Juechen Yang Jun Kong Chunying Zhao |
| author_facet | Juechen Yang Jun Kong Chunying Zhao |
| author_sort | Juechen Yang |
| collection | DOAJ |
| description | The use of mobile devices, especially smartphones, has become popular in recent years. There is an increasing need for cross-device interaction techniques that seamlessly integrate mobile devices and large display devices together. This paper develops a novel cross-device cursor position system that maps a mobile device’s movement on a flat surface to a cursor’s movement on a large display. The system allows a user to directly manipulate objects on a large display device through a mobile device and supports seamless cross-device data sharing without physical distance restrictions. To achieve this, we utilize sound localization to initialize the mobile device position as the starting location of a cursor on the large screen. Then, the mobile device’s movement is detected through an accelerometer and is accordingly translated to the cursor’s movement on the large display using machine learning models. In total, 63 features and 10 classifiers were employed to construct the machine learning models for movement detection. The evaluation results have demonstrated that three classifiers, in particular, gradient boosting, linear discriminant analysis (LDA), and naïve Bayes, are suitable for detecting the movement of a mobile device. |
| first_indexed | 2024-03-09T06:10:09Z |
| format | Article |
| id | doaj.art-2b10c11240904dd0aa7964032aaad299 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T06:10:09Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2b10c11240904dd0aa7964032aaad2992023-12-03T11:58:43ZengMDPI AGSensors1424-82202021-02-01215166510.3390/s21051665A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning TechniquesJuechen Yang0Jun Kong1Chunying Zhao2Department of Computer Science, North Dakota State University, Fargo, ND 58105, USADepartment of Computer Science, North Dakota State University, Fargo, ND 58105, USASchool of Computer Sciences, Western Illinois University, Macomb, IL 61455, USAThe use of mobile devices, especially smartphones, has become popular in recent years. There is an increasing need for cross-device interaction techniques that seamlessly integrate mobile devices and large display devices together. This paper develops a novel cross-device cursor position system that maps a mobile device’s movement on a flat surface to a cursor’s movement on a large display. The system allows a user to directly manipulate objects on a large display device through a mobile device and supports seamless cross-device data sharing without physical distance restrictions. To achieve this, we utilize sound localization to initialize the mobile device position as the starting location of a cursor on the large screen. Then, the mobile device’s movement is detected through an accelerometer and is accordingly translated to the cursor’s movement on the large display using machine learning models. In total, 63 features and 10 classifiers were employed to construct the machine learning models for movement detection. The evaluation results have demonstrated that three classifiers, in particular, gradient boosting, linear discriminant analysis (LDA), and naïve Bayes, are suitable for detecting the movement of a mobile device.https://www.mdpi.com/1424-8220/21/5/1665cross-device interactionmotion detectiongestural interactionlarge displaymobile device |
| spellingShingle | Juechen Yang Jun Kong Chunying Zhao A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning Techniques Sensors cross-device interaction motion detection gestural interaction large display mobile device |
| title | A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning Techniques |
| title_full | A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning Techniques |
| title_fullStr | A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning Techniques |
| title_full_unstemmed | A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning Techniques |
| title_short | A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning Techniques |
| title_sort | smartphone based cursor position system in cross device interaction using machine learning techniques |
| topic | cross-device interaction motion detection gestural interaction large display mobile device |
| url | https://www.mdpi.com/1424-8220/21/5/1665 |
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