Paper Info
Title
A Bipolar Myoelectric Sensor-Enabled Human-Machine Interface Based On Spinal Module Activations
Abstract
The surface electromyography (sEMG) signal-based human-machine interface (HMI) has been widely used for various scenarios of physical human-robot interaction. However, current HMIs based on bipolar myoelectric sensors are hindered by the limitations of global sEMG features, which are prone to variability and delay. In this letter, we define a HMI that takes advantage of the underlying neural information of spinal module activations from bipolar sEMG signals, inspired by recent findings of neural codes. Firstly, the spinal module activations are identified by the spiking trains of the muscle synergies extracted from bipolar sEMG signals. Secondly, we extract the information encoded in both firing rates and spike timings of the spinal module activation in a population coding manner, which follows the information encoding principle of neurons. Thirdly, we map the series of spinal module activations into gait phases, locomotion modes, joint moment and human identity in order to experimentally reveal the physiological information contained in the spinal module activations. The contained information and the benefit of our design are demonstrated and experimentally explained by the presented results and comparisons with the traditionally used global sEMG features. The proposed bipolar myoelectric sensor-enabled human-machine interface could contribute to various scenarios of physical human-robot interaction.
Year
DOI
Venue
2021
10.1109/ICRA48506.2021.9561535
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
Keywords
DocType
ISSN
Intention recognition, wearable robotics, prosthetics and exoskeletons
Conference
1050-4729
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Chunzhi Yi102.03
Feng Jiang230437.75
Guangming Lu38014.93
Chifu Yang4127.65
Zhen Ding503.04
Jianfei Zhu600.68
Jie Liu7248.36