@conference{358acfcf791740b7bce037882fee0d6e,
title = "LSTM Classification of Functional Grasps Using sEMG Data from Low-Cost Wearable Sensor",
abstract = "Modelling human grasping and transferring this data to an anthropomorphic robotic hand to endow it with human like grasping capabilities is a complex task. In this paper the use of surface electromyography (sEMG) for classification of functional grasps associated with everyday life is carried out using a low-cost wearable sensor in conjunction with state-of-the-art recurrent neural networks. The results produced through these experiments demonstrate the potential for sEMG to be used as an effective medium for transferring human demonstration to a robotic system.",
keywords = "Recurrent Neural Network, Pattern Classification, Grasping, Muscles, Robot Sensing Systems, Task Analysis, Training, grasping, LSTM, signal classification, recurrent neural networks, sEMG, wearable sensors",
author = "Christopher Millar and N Siddique and Emmett Kerr",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 7th Annual International Conference on Control, Automation and Robotics, ICCAR ; Conference date: 23-04-2021 Through 26-04-2021",
year = "2021",
month = jun,
day = "25",
doi = "10.1109/ICCAR52225.2021.9463477",
language = "English",
pages = "213--222",
url = "http://iccar.org/index.html",
}