A Comparison between the Efficacy of Task-Based Vs. Data-Based sEMG Sonification Designs
Abstract
Historically, many sonification designs that have been used
for data analysis purposes have been based on data
characteristics and have not been explicitly based on the
listener’s task. These sonification designs have often been
described as annoying, confusing, or fatiguing. In the absence
of a generally accepted theoretical framework for sonification
design, there is a need for improvements in sonification
design as well as a need for empirical evaluation of taskbased
sonification designs. This research focuses on surface
electromyography (sEMG) sonification and two sEMG data
analysis tasks: determining which of two muscles contracts
first and which of two muscles exhibits a higher exertion
level. Both of these tasks were analyzed using a task analysis
technique known as GOMS (Goals, Operators, Methods,
Selection Rules) and two sonification designs were created
based on the results of these task analyses. Two Data-based
sEMG sonification designs were then taken from the sEMG
sonification literature, and the four designs (2 Task-based and
2 Data-based) were empirically compared. Significant effects
of sonification design on listener performance were found,
with listeners scoring more accurately using the Task-based
sonification designs. Based on these results, we argue for
wider application of task analysis methods to sonification
design and for the inclusion of task analysis methods into a
generally accepted theoretical framework for sonification
design.