Static Versus Dynamic Decoding Algorithms in a Non-Invasive Body-Machine Interface

In this study, we consider a non-invasive body-machine interface that captures body motions still available to people with spinal cord injury (SCI) and maps them into a set of signals for controlling a computer user interface while engaging in a sustained level of mobility and exercise. We compare t...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering Vol. 25; no. 7; pp. 893 - 905
Main Authors: Seanez-Gonzalez, Ismael, Pierella, Camilla, Farshchiansadegh, Ali, Thorp, Elias B., Abdollahi, Farnaz, Pedersen, Jessica P., Mussa-Ivaldi, Ferdinando A.
Format: Journal Article
Language:English
Published: United States IEEE 01.07.2017
Subjects:
Actigraphy - methods
Adult
Algorithms
Assistive devices
Body–machine interface (BMI)
cervical spinal cord injury
Computers
Female
handicapped aids
Heuristic algorithms
Humans
Kalman filters
Kinematics
Male
Man-Machine Systems
Middle Aged
Movement
Pattern Recognition, Automated - methods
Principal Component Analysis
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Spinal Cord Injuries - physiopathology
tetraplegia
Unsupervised Machine Learning
wheelchair control
Wheelchairs
ISSN:1534-4320, 1558-0210
Online Access:Get full text
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Summary:In this study, we consider a non-invasive body-machine interface that captures body motions still available to people with spinal cord injury (SCI) and maps them into a set of signals for controlling a computer user interface while engaging in a sustained level of mobility and exercise. We compare the effectiveness of two decoding algorithms that transform a high-dimensional body-signal vector into a lower dimensional control vector on six subjects with high-level SCI and eight controls. One algorithm is based on a static map from current body signals to the current value of the control vector set through principal component analysis (PCA), the other on dynamic mapping a segment of body signals to the value and the temporal derivatives of the control vector set through a Kalman filter. SCI and control participants performed straighter and smoother cursor movements with the Kalman algorithm during center-out reaching, but their movements were faster and more precise when using PCA. All participants were able to use the BMI's continuous, two-dimensional control to type on a virtual keyboard and play pong, and performance with both algorithms was comparable. However, seven of eight control participants preferred PCA as their method of virtual wheelchair control. The unsupervised PCA algorithm was easier to train and seemed sufficient to achieve a higher degree of learnability and perceived ease of use.
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ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2016.2640360