Comparison of haptic guidance and error amplification robotic trainings for the learning of a timing-based motor task by healthy seniors

Abstract

With age, a decline in the temporal aspect of movement is observed such as a longer movement execution time and a decreased timing accuracy. Robotic training can represent an interesting approach to help improve movement timing among the elderly. Two types of robotic training—haptic guidance (HG; demonstrating the correct movement for a better movement planning and improved execution of movement) and error amplification (EA; exaggerating movement errors to have a more rapid and complete learning) have been positively used in young healthy subjects to boost timing accuracy. For healthy seniors, only HG training has been used so far where significant and positive timing gains have been obtained. The goal of the study was to evaluate and compare the impact of both HG and EA robotic trainings on the improvement of seniors’ movement timing. Thirty-two healthy seniors (mean age 68 ± 4 years) learned to play a pinball-like game by triggering a one-degree-of-freedom hand robot at the proper time to make a flipper move and direct a falling ball toward a randomly positioned target. During HG and EA robotic trainings, the subjects’ timing errors were decreased and increased, respectively, based on the subjects’ timing errors in initiating a movement. Results showed that only HG training benefited learning, but the improvement did not generalize to untrained targets. Also, age had no influence on the efficacy of HG robotic training, meaning that the oldest subjects did not benefit more from HG training than the younger senior subjects. Using HG to teach the correct timing of movement seems to be a good strategy to improve motor learning for the elderly as for younger people. However, more studies are needed to assess the long-term impact of HG robotic training on improvement in movement timing.

Neural circuits activated by error amplification and haptic guidance training techniques during performance of a timing-based motor task by healthy individuals

“…The present work recruited only young healthy subjects, limiting the generalization of our findings to older individuals. Indeed, two of our previous studies, using a similar timing task, found different behavioral changes in young (Milot et al 2010 ) and elder participants (Bouchard et al 2015 ) whereas young subjects’ timing performance improved with both HG and EA, but only HG training was beneficial in elderly people. Thus, it is possible that the patterns of brain activation during HG and EA training conditions would also differ between younger and older subjects.…”

“…Consistent with these two learning paradigms, i.e., trial-and-error and learning by imitation, robotic devices have been developed to either artificially amplify subjects’ movement errors (error amplification), using force fields (Emken and Reinkensmeyer 2005 ; Israely and Carmeli 2016 ; Patton and Mussa-Ivaldi 2004 ; Patton et al 2006 ), visual distortions (Abdollahi et al 2014 ) or timing-error amplifications (Bouchard et al 2015 , 2016 ; Milot et al 2010 ), or to guide or demonstrate a correct movement (haptic guidance) to help promote greater learning (Bouchard et al 2015 , 2016 ; Carel et al 2000 ; Ciccarelli et al 2005 ; Estevez et al 2014 ; Jaeger et al 2014 ; Loubinoux et al 2001 ; Marchal-Crespo et al 2013 ; Milot et al 2010 ; Radovanovic et al 2002 ). Studies have shown that error amplification (Abdollahi et al 2014 ; Bouchard et al 2015 ; Emken and Reinkensmeyer 2005 ; Patton and Mussa-Ivaldi 2004 ) or haptic guidance (Bouchard et al 2015 , 2016 ; Marchal-Crespo et al 2013 ) training techniques allow improvement in subjects’ performance of the learned task. In healthy individuals, direct comparison of the effectiveness of these two techniques on promoting learning showed that for various upper or lower limb tasks, EA training using either force fields (Marchal-Crespo et al 2014 , 2017b ) or visual distortions (van Asseldonk et al 2009 ) led to higher learning rate than HG training.…”

It Pays to Go Off-Track: Practicing with Error-Augmenting Haptic Feedback Facilitates Learning of a Curve-Tracing Task

“…This is in contrast to one of Wei et al (2005) hypothesized benefits of artificial error augmentation. Nonetheless, Milot et al (2010) demonstrated that error-amplification for timing in a pinball-like game is beneficial for initially skilled, young adult learners but not for older adults (Bouchard et al, 2015). In addition, if we look outside the realm of upper-extremity movements, studies have also shown some benefits of error augmentation in comparison to no robot assistance for a simple locomotor task, particularly for initially less skilled learners (Marchal-Crespo et al, 2014b).…”

Variations in Static Force Control and Motor Unit Behavior with Error Amplification Feedback in the Elderly

“…Visual EA is thought to inflate response conflicts and facilitate attentional focus on the motor task via error-monitoring networks (Calhoun et al, 2002 ). The EA feedback has been shown to improve point-to-point visuomotor tasks for healthy elderly (Bouchard et al, 2015 ) and neurological patients (Abdollahi et al, 2014 ), as well as a continuous static force-tracking task for young adults (Hwang et al, 2017 ). For young adults during static isometric contraction, visual EA could better stabilize force output with the enhanced complexity of force fluctuations, corollary to a larger coefficient of variation for inter-spike intervals and a greater motor unit coherence at 13–35 Hz (Hwang et al, 2017 ).…”