Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network
نویسندگان
چکیده
منابع مشابه
scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network
today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...
Relation Classification via Convolutional Deep Neural Network
The state-of-the-art methods used for relation classification are primarily based on statistical machine learning, and their performance strongly depends on the quality of the extracted features. The extracted features are often derived from the output of pre-existing natural language processing (NLP) systems, which leads to the propagation of the errors in the existing tools and hinders the pe...
متن کاملThe Effects of Visuomotor Training Using Pablo System on Hand Grip Strength and Wrist Movements in Adults and Elderly
Objectives: The primary study objective was to assess the effects of visuomotor training on grip strength and wrist movements in adults and the elderly to be efficiently used in rehabilitation. The secondary objective was to compare the post-training changes between the two groups. Methods: This was a pre-test-post-test quasi-experimental study, including healthy individuals aged 25-44 (adults...
متن کاملEMG-based wrist gesture recognition using a convolutional neural network
Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...
متن کاملSilent speech recognition from articulatory movements using deep neural network
Laryngectomee patients lose their ability to produce speech sounds and suffer in their daily communication. There are currently limited communication options for these patients. Silent speech interfaces (SSIs), which recognize speech from articulatory information (i.e., without using audio information), have potential to assist the oral communication of persons with laryngectomy or other speech...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Bioengineering and Biotechnology
سال: 2021
ISSN: 2296-4185
DOI: 10.3389/fbioe.2021.548357