Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review

Sigrist, Roland; Rauter, Georg; Riener, Robert; Wolf, Peter

doi:10.3758/s13423-012-0333-8

Cited by 965 publications

(811 citation statements)

References 284 publications

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“…Provision of concurrent augmented feedback during practice of a new motor skill can improve learning of that skill (Sigrist et al 2013). Concurrent augmented visual feedback has also been applied to re-learning balancing tasks among individuals with impaired balance due to neurologic injury (Shumway-Cook et al 1988;Winstein et al 1989;Sackley and Lincoln 1997;Walker et al 2000;Ioffe et al 2010;Sayenko et al 2010;Tsaklis et al 2012) or aging (Wolf et al 1997;Rose and Clark 2000;Lajoie 2004;Sihvonen et al 2004b;Hatzitaki et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Can augmented feedback facilitate learning a reactive balance task among older adults?

et al. 2016

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While concurrent augmented visual feedback of the centre of pressure (COP) or centre of gravity (COG) can improve quiet standing balance control, it is not known if such feedback improves reactive balance control. Additionally, it is not known if feedback of the COP or COG is superior. This study aimed to determine if: 1) concurrent augmented feedback can improve reactive balance control; and 2) feedback of the COP or COG is more effective. Forty-eight healthy older adults (60-75 years old) were randomly allocated to one of 3 groups: feedback of the COP, feedback of the COG, or No Feedback. The task was to maintain standing while experiencing 30 seconds of continuous pseudo-random perturbations delivered by a moving platform. Participants completed 25 trials with or without feedback (acquisition), immediately followed by 5 trials without feedback (immediate transfer); 5 trials without feedback were completed after a 24-hour delay (delayed transfer). The root mean square error (RMSE) of COP-COG, electrodermal level, and cocontraction index were compared between the groups and over time. All three groups reduced RMSE and co-contraction index from the start of the acquisition to the transfer tests, and there were no significant between-group differences in RMSE or co-contraction on the transfer tests. Therefore, all three groups learned the task equally well and improved balance was achieved with practice via a more efficient control strategy. The two feedback groups reduced electrodermal level with practice, but the No Feedback group did not, suggesting that feedback may help to reduce anxiety.

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Section: Introductionmentioning

confidence: 99%

Can augmented feedback facilitate learning a reactive balance task among older adults?

et al. 2016

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“…However, augmented feedback strategies should be designed with regards to the Guidance Hypothesis and to avoid problems with Slacking (Reinkensmeyer, Akoner, Ferris, & Gordon, 2009). Their efficacy is dependent on the skill level of the subject and task characteristics (MarchalCrespo et al, 2015;Sigrist et al, 2013). According to the Challenge Point Theory (Guadagnoli & Lee, 2004), optimal conditions for improvement are expected when the subject is challenged during training according to their skill level.…”

Section: Introductionmentioning

confidence: 99%

Automated Feedback Selection for Robot-Assisted Training

Gerig

Wolf

Sigrist

et al. 2017

International Journal of Computer Science in Sport

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Robot-assisted training can be enhanced by using augmented feedback to support trainees during learning. Efficacy of augmented feedback is assumed to be dependent on the trainee's skill level and task characteristics. Thus, selecting the most efficient augmented feedback for individual subjects over the course of training is challenging.We present a general concept to automate feedback selection based on predicted performance improvement. As proof of concept, we applied our concept to trunkarm rowing. Using existing data, the assumption that improvement is skill level dependent was verified and a predictive linear mixed model was obtained. We used this model to automatically select feedback for new trainees. The observed improvements were used to adapt the prediction model to the individual subject. The prediction model did not over-fit and generalized to new subjects with this adaptation.Mainly, feedback was selected that showed the highest baseline to retention learning in previous studies. By this replication of our former best results we demonstrate that a simple decision rule based on improvement prediction has the potential to reasonably select feedback, or to provide a comprehensible suggestion to a human supervisor. To our knowledge, this is the first time an automated feedback selection has been realized in motor learning.

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“…When these results are focused in the context of other similar research, some findings are supportive while some others are not. Studies that support the specific premise in which feedback is an important part of learning are numerous (for example, (Sigrist et al, 2013;Sigrist et al, 2011). However, when feedback types are considered, determining the most effective one, verbal or visual,etc., is of particular importance.…”

Section: Discussionmentioning

confidence: 99%

“…In the other study, Sigrist et al (Sigrist, Rauter, Riener, & Wolf, 2013) found that visual, auditory, haptic, and multimodal feedback are necessary for success.…”

Section: Introductionmentioning

confidence: 95%

Untitled

2017

JPES

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Background: The purpose of the present study is to examine the effects of visual, verbal and verbal-visual feedback on the success of serve and bump skills training in volleyball. Methods: The total participants of the study were 42 university students. All the participants attended the PE lectures during the 2012-2013 academic years in a form of 3 different classes which consisted of 14 students per each. Each class was assigned as an experimental group and received different feedback styles for bump and serve skills in volleyball. The feedback was as follows: verbal feedback (mean age= 12.28 ± 0.46; sport age=1.85 ± 0.62), visual feedback (12.14 ± 0.37; 1.66 ± 0.44) and verbal-visual feedback (12.21 ± 0.42; 1.91 ± 0.38). Considering the pre-test/ post-test design, the data collected from students' test scores in cognitive domain and video records were assessed and scored by experts related to the achievements in the psychomotor area. Descriptive statistics and T-Test analysis was used to investigate if there were any significant differences between pre-test and post-test scores of each experimental group. Also, an ANOVA Test was conducted to assess the significant differences between groups. Results: Results showed that there were meaningful differences between the levels of success in verbal, visual and verbal-visual feedback of pre-test and post-test scores of each group in terms of cognitive domain [F(39,2)=26.87;p=0.001] and [F(39,2)=7.807;p=0.001]. For each type of evaluation, all feedback styles increased the success levels of bumps and serve skills of the participants. Conclusions: Consequently, the findings of the present work suggested that the use of verbal-visual feedback styles that were given by teachers' instructions and video records can positively affect the learning process of bump and serve skills in volleyball.

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Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review

Cited by 965 publications

References 284 publications

Can augmented feedback facilitate learning a reactive balance task among older adults?

Can augmented feedback facilitate learning a reactive balance task among older adults?

Automated Feedback Selection for Robot-Assisted Training

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