Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep learning approach to control of prosthetic hands with
  electromyography signals   Natural muscles provide mobility in response to nerve impulses.
Electromyography (EMG) measures the electrical activity of muscles in response
to a nerve's stimulation. In the past few decades, EMG signals have been used
extensively in the identification of user intention to potentially control
assistive devices such as smart wheelchairs, exoskeletons, and prosthetic
devices. In the design of conventional assistive devices, developers optimize
multiple subsystems independently. Feature extraction and feature description
are essential subsystems of this approach. Therefore, researchers proposed
various hand-crafted features to interpret EMG signals. However, the
performance of conventional assistive devices is still unsatisfactory. In this
paper, we propose a deep learning approach to control prosthetic hands with raw
EMG signals. We use a novel deep convolutional neural network to eschew the
feature-engineering step. Removing the feature extraction and feature
description is an important step toward the paradigm of end-to-end
optimization. Fine-tuning and personalization are additional advantages of our
approach. The proposed approach is implemented in Python with TensorFlow deep
learning library, and it runs in real-time in general-purpose graphics
processing units of NVIDIA Jetson TX2 developer kit. Our results demonstrate
the ability of our system to predict fingers position from raw EMG signals. We
anticipate our EMG-based control system to be a starting point to design more
sophisticated prosthetic hands. For example, a pressure measurement unit can be
added to transfer the perception of the environment to the user. Furthermore,
our system can be modified for other prosthetic devices.

17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin