Visual error amplification showed no benefit for non-naïve subjects in trunk-arm rowing

Nicolas Gerig, Ekin Basalp, Roland Sigrist, Robert Riener, Peter Wolf

Abstract


Motor learning is assumed to be a partly error driven process. Motor learning studies on simple movements have shown that skilled subjects benefit from training with error amplification. Findings of studies with simple movements do not necessarily transfer to complex sport movements. The goal of this work was to determine the benefit of visual error amplification for non-naïve subjects in learning a fast rowing movement.

We conducted a study comparing non-naïve subjects receiving a fading, visual feedback with visual error amplification against a control group receiving the same visual feedback without error amplification. Separate outcome metrics were applied for the domains of spatial and velocity magnitude errors. Besides error metrics, variability metrics were evaluated for both domains, such that they could be interpreted in quantitative relation to each other.

The implemented error amplification did not cause group differences in any variable. Subjects with or without error amplification reached similar absolute levels in error and variability. Possible reasons remain speculative. For implementing error amplification to the training of complex movements design decisions must be made for which an informative basis is missing, e.g. the error amplification gains.


Keywords


Motor Learning; Variability; Error Augmentation; Robot-Assisted Training; Augmented Feedback

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References


Abdollahi, F., Lazarro, E. D. C., Listenberger, M., Kenyon, R. V., Kovic, M., Bogey, R. A., Hedeker, D., et al. (2013). Error Augmentation Enhancing Arm Recovery in Individuals With Chronic Stroke A Randomized Crossover Design. Neurorehabilitation and neural repair, 1545968313498649.

Basalp, E., Gerig, N., Marchal-Crespo, L., Sigrist, R., Riener, R., & Wolf, P. (2016). Visual Augmentation of Spatiotemporal Errors in a Rowing Task. 11th joint dvs Conference on Motor Control & Learning, Biomechanics & Training, 2016, Germany. doi:http://dx.doi.org/10.3929/ethz-a-010799461

Bouchard, A. E., Corriveau, H., & Milot, M.-H. (2015). Comparison of haptic guidance and error amplification robotic trainings for the learning of a timing-based motor task by healthy seniors. Frontiers in systems neuroscience, 9.

Celik, O., Powell, D., & Malley, M. K. (2009). Impact of visual error augmentation methods on task performance and motor adaptation. Rehabilitation Robotics, 2009. ICORR 2009. IEEE International Conference on (pp. 793–798). IEEE.

Crespo, L. M., & Reinkensmeyer, D. J. (2008). Haptic guidance can enhance motor learning of a steering task. Journal of motor behavior, 40(6), 545–557.

Duarte, J. E., & Reinkensmeyer, D. J. (2015). Effects of robotically modulating kinematic variability on motor skill learning and motivation. Journal of neurophysiology, 113(7), 2682–2691.

Emken, J. L., Benitez, R., & Reinkensmeyer, D. J. (2007). Human-robot cooperative movement training: Learning a novel sensory motor transformation during walking with robotic assistance-as-needed. Journal of neuroengineering and rehabilitation, 4(1), 16. doi:http://dx.doi.org/10.1186/1743-0003-4-8

Emken, J. L., & Reinkensmeyer, D. J. (2005). Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(1), 33–39. doi:http://dx.doi.org/10.1109/TNSRE.2004.843173

Fisher, M. E., Huang, F. C., Klamroth-Marganska, V., Riener, R., & Patton, J. L. (2015). Haptic error fields for robotic training. World Haptics Conference (WHC), 2015 IEEE (pp. 434–439). doi:10.1109/WHC.2015.7177750

Giese, M. A., & Poggio, T. (2000). Morphable models for the analysis and synthesis of complex motion patterns. International Journal of Computer Vision, 38(1), 59–73.

Hasson, C. J., Zhang, Z., Abe, M. O., & Sternad, D. (2016). Neuromotor Noise Is Malleable by Amplifying Perceived Errors. PLOS Computational Biology, 12(8), 1–28. doi:10.1371/journal.pcbi.1005044

Heuer, H., & Lüttgen, J. (2015). Robot assistance of motor learning: A neuro-cognitive perspective. Neuroscience & Biobehavioral Reviews, 56, 222–240.

Marchal-Crespo, L., Lopez-Oloriz, J., Jaeger, L., & Riener, R. (2014). Optimizing learning of a locomotor task: amplifying errors as needed. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 5304–5307). IEEE.

Marchal–Crespo, L., Schneider, J., Jaeger, L., & Riener, R. (2014). Learning a locomotor task: with or without errors? Journal of neuroengineering and rehabilitation, 11(1), 1.

Marchal-Crespo, L., Wolf, P., Gerig, N., Rauter, G., Jaeger, L., Vallery, H., & Riener, R. (2015). The role of skill level and motor task characteristics on the effectiveness of robotic training: first results. Rehabilitation Robotics (ICORR), 2015 IEEE International Conference on (pp. 151–156). IEEE.

Milot, M. H., Marchal-Crespo, L., Green, C. S., Cramer, S. C., & Reinkensmeyer, D. J. (2010). Comparison of error-amplification and haptic-guidance training techniques for learning of a timing-based motor task by healthy individuals. Experimental brain research, 201(2), 119–131. doi:10.1007/s00221-009-2014-z

Parmar, P. N., & Patton, J. L. (2015). Optimal gain schedules for visuomotor skill training using error-augmented feedback. Robotics and Automation (ICRA), 2015 IEEE International Conference on (pp. 3809–3813). IEEE.

Patton, J. L., Wei, Y. J., Bajaj, P., & Scheidt, R. A. (2013). Visuomotor Learning Enhanced by Augmenting Instantaneous Trajectory Error Feedback during Reaching. PLoS ONE, 8(1), e46466. doi:10.1371/journal.pone.0046466

Rauter, G., Sigrist, R., Koch, C., Crivelli, F., Raai, M. van, Riener, R., & Wolf, P. (2013). Transfer of Complex Skill Learning from Virtual to Real Rowing. PLoS ONE, 8(12), 1–18. doi:10.1371/journal.pone.0082145

Rauter, G., Sigrist, R., Marchal-Crespo, L., Vallery, H., Riener, R., & Wolf, P. (2011). Assistance or challenge? Filling a gap in user-cooperative control. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3068–3073). San Francisco, California. doi:10.1109/IROS.2011.6094832

Rauter, G., Sigrist, R., Riener, R., & Wolf, P. (2015). Learning of temporal and spatial movement aspects: A comparison of four types of haptic control and concurrent visual feedback.

Rauter, G., Zitzewitz, J. von, Duschau-Wicke, A., Vallery, H., & Riener, R. (2010). A tendon based parallel robot applied to motor learning in sports. 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), 2010 (pp. 82–87). Tokyo, Japan. doi:10.1109/BIOROB.2010.5627788

Rozario, S. V., Housman, S., Kovic, M., Kenyon, R. V., & Patton, J. L. (2009). Therapist-mediated post-stroke rehabilitation using haptic/graphic error augmentation. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE (pp. 1151–1156). IEEE.

Sharp, I., Huang, F., & Patton, J. (2011). Visual error augmentation enhances learning in three dimensions. J Neuroeng Rehabil, 8, 1–6.

Sherwood, D. E. (1988). Effect of bandwidth knowledge of results on movement consistency. Perceptual and Motor Skills, 66(2), 535–542.

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, 1–17.

Sigrist, R., Rauter, G., Riener, R., & Wolf, P. (2013). Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review. Psychonomic Bulletin & Review, 20(1), 21–53. doi:http://dx.doi.org/10.3758/s13423-012-0333-8

Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., & Keogh, E. (2003). Indexing multi-dimensional time-series with support for multiple distance measures. Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’03 (pp. 216–225). Washington, D.C.: ACM. doi:10.1145/956750.956777

Wang, F., Barkana, D. E., & Sarkar, N. (2010). Impact of visual error augmentation when integrated with assist-as-needed training method in robot-assisted rehabilitation. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 18(5), 571–579.

Wei, Y., Bajaj, P., Scheidt, R., & Patton, J. (2005). Visual error augmentation for enhancing motor learning and rehabilitative relearning. 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005. (pp. 505–510). IEEE.

Wei, Y., Patton, J., Bajaj, P., & Scheidt, R. (2005). A real-time haptic/graphic demonstration of how error augmentation can enhance learning. Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on (pp. 4406–4411). IEEE.




DOI (PDF): https://doi.org/10.15203/CISS_2018.013

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