Paper Info
Title
A comparative analysis of three non-invasive human-machine interfaces for the disabled.
Abstract
In the framework of rehabilitation robotics, a major role is played by the human-machine interface (HMI) used to gather the patients intent from biological signals, and convert them into control signals for the robotic artifact. Surprisingly, decades of research have not yet declared what the optimal HMI is in this context; in particular, the traditional approach based upon surface electromyography (sEMG) still yields unreliable results due to the inherent variability of the signal. To overcome this problem, the scientific community has recently been advocating the discovery, analysis, and usage of novel HMIs to supersede or augment sEMG; a comparative analysis of such HMIs is therefore a very desirable investigation. In this paper, we compare three such HMIs employed in the detection of finger forces, namely sEMG, ultrasound imaging, and pressure sensing. The comparison is performed along four main lines: the accuracy in the prediction, the stability over time, the wearability, and the cost. A psychophysical experiment involving ten intact subjects engaged in a simple finger-flexion task was set up. Our results show that, at least in this experiment, pressure sensing and sEMG yield comparably good prediction accuracies as opposed to ultrasound imaging; and that pressure sensing enjoys a much better stability than sEMG. Given that pressure sensors are as wearable as sEMG electrodes but way cheaper, we claim that this HMI could represent a valid alternative/augmentation to sEMG to control a multi-fingered hand prosthesis.
Year
DOI
Venue
2014
10.3389/fnbot.2014.00024
FRONTIERS IN NEUROROBOTICS
Keywords
Field
DocType
pressure sensing,machine learning,incremental learning,human-machine interaction,rehabilitation robotics,force control
Human–machine system,Computer science,Wearable computer,Incremental learning,Ultrasound imaging,Pressure sensor,Artificial intelligence,Hand prosthesis,Rehabilitation robotics,Machine learning,Human machine interaction
Journal
Volume
ISSN
Citations 
8
1662-5218
10
PageRank 
References 
Authors
0.68
11
2
Name
Order
Citations
PageRank
Vikram Ravindra1100.68
Claudio Castellini2565.26