The effectiveness of robotic training depends on motor task characteristics

Marchal–Crespo, Laura; Rappo, Nicole; Riener, Robert

doi:10.1007/s00221-017-5099-9

Cited by 27 publications

(35 citation statements)

References 55 publications

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“…Although previous research has shown that motivation can be modulated through robotic assistance (Duarte and Reinkensmeyer, 2015;Marchal-Crespo et al, 2017b;Bernardoni et al, 2019), we did not find significant differences between training strategies in any subscale of the Intrinsic Motivation Inventory. The lack of significant differences could be due to the generally high overall interest/enjoyment and effort/importance (i.e., ceiling effect) and the high variability observed across participants.…”

Section: No Differences In Motivation Across Training Strategiescontrasting

confidence: 99%

Promoting Motor Variability During Robotic Assistance Enhances Motor Learning of Dynamic Tasks

2021

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Despite recent advances in robot-assisted training, the benefits of haptic guidance on motor (re)learning are still limited. While haptic guidance may increase task performance during training, it may also decrease participants' effort and interfere with the perception of the environment dynamics, hindering somatosensory information crucial for motor learning. Importantly, haptic guidance limits motor variability, a factor considered essential for learning. We propose that Model Predictive Controllers (MPC) might be good alternatives to haptic guidance since they minimize the assisting forces and promote motor variability during training. We conducted a study with 40 healthy participants to investigate the effectiveness of MPCs on learning a dynamic task. The task consisted of swinging a virtual pendulum to hit incoming targets with the pendulum ball. The environment was haptically rendered using a Delta robot. We designed two MPCs: the first MPC—end-effector MPC—applied the optimal assisting forces on the end-effector. A second MPC—ball MPC—applied its forces on the virtual pendulum ball to further reduce the assisting forces. The participants' performance during training and learning at short- and long-term retention tests were compared to a control group who trained without assistance, and a group that trained with conventional haptic guidance. We hypothesized that the end-effector MPC would promote motor variability and minimize the assisting forces during training, and thus, promote learning. Moreover, we hypothesized that the ball MPC would enhance the performance and motivation during training but limit the motor variability and sense of agency (i.e., the feeling of having control over their movements), and therefore, limit learning. We found that the MPCs reduce the assisting forces compared to haptic guidance. Training with the end-effector MPC increases the movement variability and does not hinder the pendulum swing variability during training, ultimately enhancing the learning of the task dynamics compared to the other groups. Finally, we observed that increases in the sense of agency seemed to be associated with learning when training with the end-effector MPC. In conclusion, training with MPCs enhances motor learning of tasks with complex dynamics and are promising strategies to improve robotic training outcomes in neurological patients.

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Section: No Differences In Motivation Across Training Strategiescontrasting

confidence: 99%

Promoting Motor Variability During Robotic Assistance Enhances Motor Learning of Dynamic Tasks

2021

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“…Here, only a brief summary is given for completeness. Similar haptic disturbance (Marchal-Crespo et al, 2017a) and error modulating controllers (Marchal-Crespo et al, 2017c) were developed to perform motor learning experiments with ARMin, a 7 degree-of-freedom (DoF) robotic exoskeleton for upper limb rehabilitation. The haptic controllers employed in the present experiment, however, were developed in joint coordinates and were not based on the end-effector trajectories.…”

Section: Methodsmentioning

confidence: 99%

“…We employed six statements (Table 1) from the well-established Intrinsic Motivation Inventory (IMI, Ryan, 1982). The IMI has been successfully employed in several motor learning experiments to assess intrinsic motivation (Duarte and Reinkensmeyer, 2015; Marchal-Crespo et al, 2017c). The full questionnaire assesses seven motivational subscales (with a total of 45 questions).…”

Section: Methodsmentioning

confidence: 99%

Haptic Error Modulation Outperforms Visual Error Amplification When Learning a Modified Gait Pattern

et al. 2019

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Robotic algorithms that augment movement errors have been proposed as promising training strategies to enhance motor learning and neurorehabilitation. However, most research effort has focused on rehabilitation of upper limbs, probably because large movement errors are especially dangerous during gait training, as they might result in stumbling and falling. Furthermore, systematic large movement errors might limit the participants’ motivation during training. In this study, we investigated the effect of training with novel error modulating strategies, which guarantee a safe training environment, on motivation and learning of a modified asymmetric gait pattern. Thirty healthy young participants walked in the exoskeletal robotic system Lokomat while performing a foot target-tracking task, which required an increased hip and knee flexion in the dominant leg. Learning the asymmetric gait pattern with three different strategies was evaluated: (i) No disturbance: no robot disturbance/guidance was applied, (ii) haptic error amplification: unsafe and discouraging large errors were limited with haptic guidance, while haptic error amplification enhanced awareness of small errors relevant for learning, and (iii) visual error amplification: visually observed errors were amplified in a virtual reality environment. We also evaluated whether increasing the movement variability during training by adding randomly varying haptic disturbances on top of the other training strategies further enhances learning. We analyzed participants’ motor performance and self-reported intrinsic motivation before, during and after training. We found that training with the novel haptic error amplification strategy did not hamper motor adaptation and enhanced transfer of the practiced asymmetric gait pattern to free walking. Training with visual error amplification, on the other hand, increased errors during training and hampered motor learning. Participants who trained with visual error amplification also reported a reduced perceived competence. Adding haptic disturbance increased the movement variability during training, but did not have a significant effect on motor adaptation, probably because training with haptic disturbance on top of visual and haptic error amplification decreased the participants’ feelings of competence. The proposed novel haptic error modulating controller that amplifies small task-relevant errors while limiting large errors outperformed visual error augmentation and might provide a promising framework to improve robotic gait training outcomes in neurological patients.

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“…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. However, in a previous study of ours, using a timing-based pinball-like task (identical to the one of the current study), we found that both EA and HG training techniques were beneficial to improving subjects’ timing performance.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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To promote motor learning, robotic devices have been used to improve subjects’ performance by guiding desired movements (haptic guidance—HG) or by artificially increasing movement errors to foster a more rapid learning (error amplification—EA). To better understand the neurophysiological basis of motor learning, a few studies have evaluated brain regions activated during EA/HG, but none has compared both approaches. The goal of this study was to investigate using fMRI which brain networks were activated during a single training session of HG/EA in healthy adults learning to play a computerized pinball-like timing task. Subjects had to trigger a robotic device by flexing their wrist at the correct timing to activate a virtual flipper and hit a falling ball towards randomly positioned targets. During training with HG/EA, subjects’ timing errors were decreased/increased, respectively, by the robotic device to delay or accelerate their wrist movement. The results showed that at the beginning of the training period with HG/EA, an error-detection network, including cerebellum and angular gyrus, was activated, consistent with subjects recognizing discrepancies between their intended actions and the actual movement timing. At the end of the training period, an error-detection network was still present for EA, while a memory consolidation/automatization network (caudate head and parahippocampal gyrus) was activated for HG. The results indicate that training movement with various kinds of robotic input relies on different brain networks. Better understanding the neurophysiological underpinnings of brain processes during HG/EA could prove useful for optimizing rehabilitative movement training for people with different patterns of brain damage.

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The effectiveness of robotic training depends on motor task characteristics

Cited by 27 publications

References 55 publications

Promoting Motor Variability During Robotic Assistance Enhances Motor Learning of Dynamic Tasks

Promoting Motor Variability During Robotic Assistance Enhances Motor Learning of Dynamic Tasks

Haptic Error Modulation Outperforms Visual Error Amplification When Learning a Modified Gait Pattern

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

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