Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction
Human hand gestures are a widely accepted form of real-time input for devices providing a human-machine interface. However, hand gestures have limitations in terms of effectively conveying the complexity and diversity of human intentions. This study attempted to address these limitations by proposin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/11/2562 |
| _version_ | 1797999670409035776 |
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| author | Hobeom Han Sang Won Yoon |
| author_facet | Hobeom Han Sang Won Yoon |
| author_sort | Hobeom Han |
| collection | DOAJ |
| description | Human hand gestures are a widely accepted form of real-time input for devices providing a human-machine interface. However, hand gestures have limitations in terms of effectively conveying the complexity and diversity of human intentions. This study attempted to address these limitations by proposing a multi-modal input device, based on the observation that each application program requires different user intentions (and demanding functions) and the machine already acknowledges the running application. When the running application changes, the same gesture now offers a new function required in the new application, and thus, we can greatly reduce the number and complexity of required hand gestures. As a simple wearable sensor, we employ one miniature wireless three-axis gyroscope, the data of which are processed by correlation analysis with normalized covariance for continuous gesture recognition. Recognition accuracy is improved by considering both gesture patterns and signal strength and by incorporating a learning mode. In our system, six unit hand gestures successfully provide most functions offered by multiple input devices. The characteristics of our approach are automatically adjusted by acknowledging the application programs or learning user preferences. In three application programs, the approach shows good accuracy (90−96%), which is very promising in terms of designing a unified solution. Furthermore, the accuracy reaches 100% as the users become more familiar with the system. |
| first_indexed | 2024-04-11T11:07:11Z |
| format | Article |
| id | doaj.art-3196c2ac44e84be0808052f580a79383 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:07:11Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3196c2ac44e84be0808052f580a793832022-12-22T04:28:14ZengMDPI AGSensors1424-82202019-06-011911256210.3390/s19112562s19112562Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine InteractionHobeom Han0Sang Won Yoon1Department of Automotive Engineering, Hanyang University, Seoul 04763, KoreaDepartment of Automotive Engineering, Hanyang University, Seoul 04763, KoreaHuman hand gestures are a widely accepted form of real-time input for devices providing a human-machine interface. However, hand gestures have limitations in terms of effectively conveying the complexity and diversity of human intentions. This study attempted to address these limitations by proposing a multi-modal input device, based on the observation that each application program requires different user intentions (and demanding functions) and the machine already acknowledges the running application. When the running application changes, the same gesture now offers a new function required in the new application, and thus, we can greatly reduce the number and complexity of required hand gestures. As a simple wearable sensor, we employ one miniature wireless three-axis gyroscope, the data of which are processed by correlation analysis with normalized covariance for continuous gesture recognition. Recognition accuracy is improved by considering both gesture patterns and signal strength and by incorporating a learning mode. In our system, six unit hand gestures successfully provide most functions offered by multiple input devices. The characteristics of our approach are automatically adjusted by acknowledging the application programs or learning user preferences. In three application programs, the approach shows good accuracy (90−96%), which is very promising in terms of designing a unified solution. Furthermore, the accuracy reaches 100% as the users become more familiar with the system.https://www.mdpi.com/1424-8220/19/11/2562hand gesturecontinuous gesture recognitiongyroscopemulti-modal input devicesunified wearable input devices |
| spellingShingle | Hobeom Han Sang Won Yoon Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction Sensors hand gesture continuous gesture recognition gyroscope multi-modal input devices unified wearable input devices |
| title | Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction |
| title_full | Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction |
| title_fullStr | Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction |
| title_full_unstemmed | Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction |
| title_short | Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction |
| title_sort | gyroscope based continuous human hand gesture recognition for multi modal wearable input device for human machine interaction |
| topic | hand gesture continuous gesture recognition gyroscope multi-modal input devices unified wearable input devices |
| url | https://www.mdpi.com/1424-8220/19/11/2562 |
| work_keys_str_mv | AT hobeomhan gyroscopebasedcontinuoushumanhandgesturerecognitionformultimodalwearableinputdeviceforhumanmachineinteraction AT sangwonyoon gyroscopebasedcontinuoushumanhandgesturerecognitionformultimodalwearableinputdeviceforhumanmachineinteraction |