Abstract
Most of the reported hand gesture recognition algorithms require high computational resources, i.e., fast MCU frequency and significant memory, which are highly inapplicable to the cost-effectiveness of consumer electronics products. This paper proposes a hand gesture recognition algorithm running on an interactive wristband, with computational resource requirements as low as Flash < 5 KB, RAM < 1 KB. Firstly, we calculated the three-axis linear acceleration by fusing accelerometer and gyroscope data with a complementary filter. Then, by recording the order of acceleration vectors crossing axes in the world coordinate frame, we defined a new feature code named axis-crossing code. Finally, we set templates for eight hand gestures to recognize new samples. We compared this algorithm’s performance with the widely used dynamic time warping (DTW) algorithm and recurrent neural network (BiLSTM and GRU). The results show that the accuracies of the proposed algorithm and RNNs are higher than DTW and that the time cost of the proposed algorithm is much less than those of DTW and RNNs. The average recognition accuracy is 99.8% on the collected dataset and 97.1% in the actual user-independent case. In general, the proposed algorithm is suitable and competitive in consumer electronics. This work has been volume-produced and patent-granted.
Highlights
Most hand gesture recognition (HGR) studies are based on inertial sensors, especially Micro-ElectroMechanical System (MEMS) sensors
This paper will train a bidirectional-long short-term memory (BiLSTM)-recurrent neural network (RNN) and a gate recurrent unit (GRU)-RNN as two typical examples of deep learning to compare them with the proposed algorithm
This paper proposed and implemented a novel gesture recognition method based on axis-crossing code that can recognize eight gestures
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. This paper introduces a novel gesture recognition algorithm for a wrist band to interact with intelligent speakers. It neither adopts DTW nor other classic machine learning classifiers and deep learning methods. We introduce a template matching method based on acceleration axis-crossing code and achieved high accuracy in eight gestures both in user-dependent and userindependent cases. The rest of the paper is organized as follows: Section 2 introduces some related work about hand gesture recognition algorithms using accelerometers and gyroscopes.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
