Abstract
There is an increasing interest in employing immersive virtual reality or augmented reality and wearable technology to provide real-time motor performance feedback during rehabilitative arm exercises. Biofeedback systems have been shown to improve motor error, fluidity and speed whilst increasing patient engagement and motivation to persevere. Preliminary research on using sound to provide performance feedback has shown that it can provide spatio-temporal information in a motivating and engaging way. This research presents a proof-of-concept auditory biofeedback system that provides error-corrective sonification of the arms spatial orientation and acceleration throughout a reaching task in order for users to learn and follow a novel trajectory. Evaluation Method: Seven healthy participants (three males, four females) from a healthcare background completed the reaching task whilst using the auditory biofeedback system, both blindfolded and with full vision. Using a System Usability Scale (SUS) study, a quantitative score on the system’s usability was calculated. Results: The mean SUS score was 74.64 (standard deviation = 12.28), indicating that the prototype provides an above average usability score (avg. across 5000 surveys = 68). This research concludes that further investigation into the concept within a clinical environment as a tool for upper arm stroke rehabilitation is recommended.
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References
Bangor, A., Kortum, P., & Miller, J. (2009). Determining what individual SUS scores mean: Adding an adjective rating scale. Journal of Usability Studies, 4(3), 114–123. Retrieved from http://uxpajournal.org/wp-content/uploads/pdf/JUS_Bangor_May2009.pdf.
Brooke, J. (2013). SUS: A retrospective. Journal of Usability Studies, 8(2), 29–40. Retrieved from http://uxpajournal.org/wp-content/uploads/pdf/JUS_Brooke_February_2013.pdf.
Carr, J., & Shepard, R. (2010). Neurological rehabilitation (1st ed.). Churchill Livingstone Elsevier.
Cirstea, M., & Levin, M. (2007). Improvement of arm movement patterns and endpoint control depends on type of feedback during practice in stroke survivors. Neurorehabilitation and Neural Repair, 21(5), 398–411. https://doi.org/10.1177/1545968306298414.
Dailly, A., Sigrist, R., Kim, Y., Wolf, P., Erckens, H., Cerny, J., et al. (2012). Can simple error sonification in combination with music help improve accuracy in upper limb movements? In 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). https://doi.org/10.1109/biorob.2012.6290908.
Danna, J., Fontaine, M., Paz-Villagrán, V., Gondre, C., Thoret, E., Aramaki, M., et al. (2015). The effect of real-time auditory feedback on learning new characters. Human Movement Science, 43, 216–228. https://doi.org/10.1016/j.humov.2014.12.002.
Danzl, M., Etter, N., Andreatta, R., & Kitzman, P. (2012). Facilitating neurorehabilitation through principles of engagement. Journal of Allied Health. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/22544406.
Faraway, J. (2001). Modeling hand trajectories during reaching motions. Department of Statistics University of Michigan.
Fujii, S., Lulic, T., & Chen, J. (2016). More feedback is better than less: Learning a novel upper limb joint coordination pattern with augmented auditory feedback. Frontiers in Neuroscience, 10. https://doi.org/10.3389/fnins.2016.00251.
Herman, T., Hunt, A., & Neuhoff, J. (2011). The Sonification handbook. Bielefeld.
Heunis, C. (2016). Design, construction and analysis of an alternative stroke rehabilitation device based on the principles of neuroplasticity (master of engineering). Stellenbosch University.
Huang, H., Ingalls, T., Olson, L., Ganley, K., Rikakis, T., & He, J. (2005). Interactive multimodal biofeedback for task-oriented neural rehabilitation. In 2005 IEEE engineering in medicine and biology 27th annual conference. Engineering in Medicine and Biology Society. Retrieved from https://ieeexplore.ieee.org/document/1616988/
Huang, H., Wolf, S., & He, J. (2006). Recent developments in biofeedback for neuromotor rehabilitation. Journal of Neuroengineering and Rehabilitation, 3(1), 11. https://doi.org/10.1186/1743-0003-3-11.
Krakauer, J., & Mazzoni, P. (2011). Human sensorimotor learning: Adaptation, skill, and beyond. Current Opinion in Neurobiology, 21(4), 636–644. https://doi.org/10.1016/j.conb.2011.06.012.
Laver, K., George, S., Thomas, S., Deutsch, J., & Crotty, M. (2015). Virtual reality for stroke rehabilitation. Cochrane Database of Systematic Reviews. https://doi.org/10.1002/14651858.cd008349.pub3.
Lewthwaite, R., & Wulf, G. (2012). Motor learning through a motivational lens. In N. Hodges & M. Williams (Eds.), Skill acquisition in sport: Research, theory and practice (2nd ed.). Routledge.
Merians, A., Jack, D., Burdca, G., Adamovich, S., Recce, M., & Poizner, H. et al. (2002). Virtual reality-augmented rehabilitation for patients following stroke. Physical Therapy Issue 9, 82(9), p898. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/12201804
Phelan, I., Arden, M., Garcia, C., & Roast, C. (2015). Exploring virtual reality and prosthetic training. 2015 IEEE Virtual Reality (VR). https://doi.org/10.1109/vr.2015.7223441.
Rama Murthy, S., & Mani, M. (2013). Discerning rejection of technology. SAGE Open, 3(2), 215824401348524. https://doi.org/10.1177/2158244013485248.
Salmoni, A., Schmidt, R., & Walter, C. (1984). Knowledge of results and motor learning: A review and critical reappraisal. Psychological Bulletin, 95(3), 355–386. https://doi.org/10.1037/0033-2909.95.3.355.
Sathiyanarayanan, M., & Rajan, S. (2016). MYO Armband for physiotherapy healthcare: A case study using gesture recognition application. 2016 8th International Conference on Communication Systems and Networks (COMSNETS). https://doi.org/10.1109/comsnets.2016.7439933.
Sauro, J. (2011). Measuring usability with the system usability scale (SUS). Retrieved from https://measuringu.com/sus/
Scholz, D., Rhode, S., Großbach, M., Rollnik, J., & Altenmüller, E. (2015). Moving with music for stroke rehabilitation: A sonification feasibility study. Annals of the New York Academy of Sciences, 1337(1), 69–76. doi:https://doi.org/10.1111/nyas.12691
Sigrist, R., Rauter, G., Marchal-Crespo, L., Riener, R., & Wolf, P. (2014). Sonification and haptic feedback in addition to visual feedback enhances complex motor task learning. Experimental Brain Research, 233(3), 909–925. https://doi.org/10.1007/s00221-014-4167-7.
Singer, B., & Garcia-Vega, J. (2017). The Fugl-Meyer upper extremity scale. Journal of Physiotherapy, 63(1), 53. https://doi.org/10.1016/j.jphys.2016.08.010.
Stern, B. (2017). Inside Myo | Myo armband teardown | Adafruit learning system. Learn.adafruit.com . Retrieved 19 June 2017, from https://learn.adafruit.com/myo-armband-teardown/inside-myo
Stroke Association. (2017). State of the nation, 2017. Stroke Association. Retrieved from https://www.stroke.org.uk/sites/default/files/state_of_the_nation_2017_final_1.pdf
Thalmic Labs. (2014). Myo SDK Manual. Thalmic Labs. Retrieved from https://developer.thalmic.com/docs/api_reference/platform/index.html
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Hall, S., Wild, F., Scheper, T.o. (2019). Real-Time Auditory Biofeedback System for Learning a Novel Arm Trajectory: A Usability Study. In: Buchem, I., Klamma, R., Wild, F. (eds) Perspectives on Wearable Enhanced Learning (WELL). Springer, Cham. https://doi.org/10.1007/978-3-319-64301-4_18
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