Towards a systematic approach to real-time sonification design for surface electromyography Academic Article uri icon

abstract

  • © 2016 Elsevier B.V. Surface electromyography (sEMG) is a technique for measuring the electrical activity of muscles and is often used as a biofeedback tool. However, challenges associated with the typically visual display of sEMG data have motivated researchers to find non-visual ways of displaying sEMG data, and parameter-mapping sonification has been explored in order to present sEMG data acoustically. Parameter-mapping sonification is a technique that involves mapping values in a data set to acoustic properties of sound. Sonification of EMG data has shown potential for identifying musculoskeletal disease and improving athletic and exercise performance. However, many sonification designs to date have not been systematically evaluated and there have been few quantitative approaches to objective comparisons of sonification paradigms. In this study, we performed a quantitative comparison of different sonification designs in order to test our hypothesis that different sonification designs may be better suited to different tasks. Thirty-six participants (ages 18–31, 14 male) who volunteered to listen to the sEMG sonifications created for this study were asked to identify two different features of the data: muscle activation time and muscle exertion level. Their responses were analyzed in order to determine the effect of sonification design on listener performance. Results indicated that having the sonifications spatialized resulted in the best performance for both tasks. However, different sonification designs resulted in the best performance for the muscle activation time estimation task (Pitch and Loudness mapped redundantly) and the muscle exertion level estimation task (Pitch, Loudness, and Attack mapped redundantly). Further, for the time estimation task, the use of the Attack mapping appeared to reliably inhibit performance. These findings strongly suggest that sonification designs for sEMG need to be designed differently based on the task the user is performing.

author list (cited authors)

  • Peres, S. C., Verona, D., Nisar, T., & Ritchey, P.

citation count

  • 3

publication date

  • April 2017