Journal ArticleDOI
A Hand Gesture Recognition Framework and Wearable Gesture-Based Interaction Prototype for Mobile Devices
TLDR
An algorithmic framework is proposed to process acceleration and surface electromyographic (SEMG) signals for gesture recognition, which includes a novel segmentation scheme, a score-based sensor fusion scheme, and two new features.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.read more
Citations
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Journal ArticleDOI
Gesture recognition by instantaneous surface EMG images.
TL;DR: It is presented that the patterns inside the instantaneous values of high-density sEMG enables gesture recognition to be performed merely with s EMG signals at a specific instant.
Journal ArticleDOI
Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation
TL;DR: A benchmark database of HD-sEMG recordings of hand gestures performed by 23 participants is presented, and a deep-learning-based domain adaptation framework is proposed to enhance sEMG-based inter-session gesture recognition.
Proceedings ArticleDOI
Serendipity: Finger Gesture Recognition using an Off-the-Shelf Smartwatch
TL;DR: Serendipity is the first to explore the feasibility of using solely motion sensors on everyday wearable devices to detect fine-grained gestures, and has the potential to be applied to cross-device interactions, or as a tool for research in fields involving finger and hand motion.
Journal ArticleDOI
Feasibility of Wrist-Worn, Real-Time Hand, and Surface Gesture Recognition via sEMG and IMU Sensing
TL;DR: The design and validation of a real-time gesture recognition wristband based on surface electromyography and inertial measurement unit sensing fusion is presented, which can recognize 8 air gestures and 4 surface gestures with 2 distinct force levels.
Journal ArticleDOI
Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges
TL;DR: In this article , a comprehensive survey of the most important aspects of multi-sensor applications for human activity recognition, including those recently added to the field for unsupervised learning and transfer learning, is presented.
References
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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