Abstract
Recent advances in myoelectric controlled techniques have made the surface electromyogram (sEMG)-based sensing armband a promising candidate for acquiring bioelectric signals in a simple and convenient way. However, inevitable electrode shift as a non-negligible defect commonly causes a trained classifier requiring continuous recalibrations. In this study, a novel hand gesture prediction is firstly proposed; it is robust to electrode shift with arbitrary angle. Unlike real-time recognition which outputs target gestures only after the termination of hand motions, our proposed advanced prediction can provide the same results, even before the completion of signal collection. Moreover, by combining interpolated peak location and preset synchronous gesture, the developed simplified rapid electrode shift detection and correction at random rather than previous fixed angles are realized. Experimental results demonstrate that it is possible to achieve both electrode shift detection with high precision and gesture prediction with high accuracy. This study provides a new insight into electrode shift robustness which brings gesture prediction a step closer to practical applications.
Highlights
Hand gesture serves as an auxiliary enhancer of reinforcing information delivery in human conversations, and as a primary method for transferring instructions with human–computer interaction devices [1]
We firstly report a hand gesture prediction model with an emphasis on estimating electrode shift effects to provide a new insight into wearing-independence based on an surface electromyogram (sEMG) armband
We propose a specific gesture based on a signal synchronization cansynchronization activate minimum muscular as problem, we propose a specific gesture method based on a that signal method
Summary
Hand gesture serves as an auxiliary enhancer of reinforcing information delivery in human conversations, and as a primary method for transferring instructions with human–computer interaction devices [1]. The fast growing characteristic of surface electromyogram (sEMG) has made it a promising candidate for hand motion detection, recognition or even prediction (as bio-signal sensing technique is). While the sEMG signal cannot be solely utilized in discriminating dynamic or spatial hand gestures [4], it has particular advantages of non-invasive sensing and decoding fine muscular activity and directly. There are usually classification errors due to an electrode shift and when the wearing position may deviate from that of previous use [8]. Recent advances in wearable sEMG sensors (e.g., sEMG armband) have facilitated the process of bioelectrical signal acquisition, whereas electrode position should be consistent in case of reducing the classification accuracy [9]. The position-dependent properties of Sensors 2020, 20, 1113; doi:10.3390/s20041113 www.mdpi.com/journal/sensors
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.
