The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
A Hand Gesture Recognition Framework and Wearable Gesture-Based Interaction Prototype for Mobile Devices
Abstract
An algorithmic framework is proposed to process acceleration and surface electromyographic (SEMG) signals for gesture recognition. It includes a novel segmentation scheme, a score-based sensor fusion scheme, and two new features. A Bayes linear classifier and an improved dynamic time-warping algorithm are utilized in the framework. In addition, a prototype system, including a wearable gesture sensing device (embedded with a three-axis accelerometer and four SEMG sensors) and an application program with the proposed algorithmic framework for a mobile phone, is developed to realize gesture-based real-time interaction. With the device worn on the forearm, the user is able to manipulate a mobile phone using 19 predefined gestures or even personalized ones. Results suggest that the developed prototype responded to each gesture instruction within 300 ms on the mobile phone, with the average accuracy of 95.0% in user-dependent testing and 89.6% in user-independent testing. Such performance during the interaction testing, along with positive user experience questionnaire feedback, demonstrates the utility of the framework.
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Cites
A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors
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Self-citationFast time series classification using numerosity reduction
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Enabling always-available input with muscle-computer interfaces
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Wearable Sensor-Based Hand Gesture and Daily Activity Recognition for Robot-Assisted Living
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EMG-based hand gesture recognition for realtime biosignal interfacing
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Cited by
Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., Zhao, H., Miao, X., Liu, R., & Fortino, G. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.006 · Cited by 671
Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation
Du, Y., Jin, W., Wei, W., Hu, Y., & Geng, W. (2017) · Sensors · doi:10.3390/s17030458 · Cited by 363
Song, X., van de Ven, S. S., Chen, S., Kang, P., Gao, Q., Jia, J., & Shull, P. B. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.811950 · Cited by 47
Metadata
- Authors
- Lu, Z., Chen, X., Li, Q., Zhang, X., & Zhou, P.
- Venue
- IEEE Transactions on Human-Machine Systems
- Published
- 2014-04
- Volume
- 44
- Issue
- 2
- Pages
- 293-299
- ISSN
- 2168-2291 (ISSN-L)
- 2168-2305
- OpenAlex
- W2087455143
- Cited by
- 294
- References
- 20
- FWCI
- 50.18
- Type
- journal article
- Language
- en
- Open access
- closed
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- Lu, Z., Chen, X., Li, Q., Zhang, X., & Zhou, P. (2014). A Hand Gesture Recognition Framework and Wearable Gesture-Based Interaction Prototype for Mobile Devices. IEEE Transactions on Human-Machine Systems. https://doi.org/10.1109/thms.2014.2302794
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