Electromyography Signal-Based Hand Gesture Recognition for Human-Robot Interface
Dr.Lakshmi
, Viknesh K.S , Yogul Kishore.S , Sanjay.B , Veniyan.U
Human-Robot interaction, Electromyography, Ensemble learning, Hand motion
The development of hand gesture recognition in numerous scientific fields has proven to offer major advantages in enhancing the need for Robotics and human contact. The change of big data and machine learning as intelligent statistical knowledge techniques has transformed data science and brought about a new era. simpler to reliably categorize Electromyography (EMG) signals are used to measure hand movements. Unfortunately, the labor-intensive process of gathering and labeling the enormous dataset demands a lengthy implementation period. As a result, a special method Electromyography (EMG) signals are used to animate the hands the detection of hand gestures based on EMG was created. Without taking into account the hand motion sequence, it is possible to automatically categorize Employing ensemble learning, determine the type of obtained EMG data. The growing requirement for human-robot interaction has been significantly increased by the development of hand gesture recognition in several scientific domains. A new era of data science has begun with the emergence of big data and machine learning as sophisticated statistical knowledge approaches, making it easier than ever to categorize hand motions using electromyography (EMG) signals. Unfortunately, a lengthy implementation period is necessary due to the time- consuming procedure of gathering and labeling the massive dataset. In order to combine the benefits of depth perception training using the detection of hand gestures based on EMG, a unique technique was developed. It is feasible to automatically classify the collected EMG data using ensemble learning without considering the hand motion sequence.
"Electromyography Signal-Based Hand Gesture Recognition for Human-Robot Interface", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.360-365, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001056.pdf
Volume 11
Issue 2,
February-2024
Pages : 360-365
Paper Reg. ID: TIJER_151127
Published Paper Id: TIJERB001056
Downloads: 000288
Research Area: Science and Technology
Country: Thiruvallur, TamilNadu, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
