Towards more efficient robotic gait training: A novel controller to modulate movement errors

Rudt, Simon; Moos, Marco; Seppey, Solange; Riener, Robert; Marchal–Crespo, Laura

doi:10.1109/biorob.2016.7523738

Cited by 12 publications

(20 citation statements)

References 23 publications

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“…Additionally, maybe the controller parameters that describe the random disturbance forces' directionality, frequency and magnitude were not appropriate to enhance motor learning of these specific tasks. We selected the parameters based on previous research on motor learning of locomotor tasks (Marchal-Crespo et al, 2014b;Rüdt et al, 2016). However, maybe complex tasks performed with the upper limbs require totally different values.…”

Section: Discussionmentioning

confidence: 99%

“…We made the negative proportional gain k p a function of the subject's ongoing perpendicular error in order to limit dangerous and discouraging large errors with haptic guidance, while augmenting awareness of task relevant errors by means of error amplification (Rüdt et al, 2016). The used equation corresponds to the superposition of two elementary sigmoid functions:…”

Section: Perpendicular Error Amplificationmentioning

confidence: 99%

“…At every time step, a random number rand in the range from zero to one is drawn from a uniform distribution [rand~Unif(0,1)]. The probability for this random number rand to be smaller than a constant C∈ [0,1] is equal to C (Rüdt et al, 2016).…”

Section: Random Disturbing Forcesmentioning

confidence: 99%

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Experimental Evaluation of a Mixed Controller That Amplifies Spatial Errors and Reduces Timing Errors

et al. 2017

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Research on motor learning suggests that training with haptic guidance enhances learning of the timing components of motor tasks, whereas error amplification is better for learning the spatial components. We present a novel mixed guidance controller that combines haptic guidance and error amplification to simultaneously promote learning of the timing and spatial components of complex motor tasks. The controller is realized using a force field around the desired position. This force field has a stable manifold tangential to the trajectory that guides subjects in velocity-related aspects. The force field has an unstable manifold perpendicular to the trajectory, which amplifies the perpendicular (spatial) error. We also designed a controller that applies randomly varying, unpredictable disturbing forces to enhance the subjects' active participation by pushing them away from their "comfort zone." We conducted an experiment with thirty-two healthy subjects to evaluate the impact of four different training strategies on motor skill learning and selfreported motivation: (i) No haptics, (ii) mixed guidance, (iii) perpendicular error amplification and tangential haptic guidance provided in sequential order, and (iv) randomly varying disturbing forces. Subjects trained two motor tasks using ARMin IV, a robotic exoskeleton for upper limb rehabilitation: follow circles with an ellipsoidal speed profile, and move along a 3D line following a complex speed profile. Mixed guidance showed no detectable learning advantages over the other groups. Results suggest that the effectiveness of the training strategies depends on the subjects' initial skill level. Mixed guidance seemed to benefit subjects who performed the circle task with smaller errors during baseline (i.e., initially more skilled subjects), while training with no haptics was more beneficial for subjects who created larger errors (i.e., less skilled subjects). Therefore, perhaps the high functional difficulty of the tasks limited the potential benefit of mixed guidance. Adding random disturbing forces during training reduced the learning effect size compared to no haptics. The unanticipated forces also decreased the subjects' feelings of competence while did not increase their effort and interest. Further studies with mildly affected neurologically patients employing easier tasks need to be performed in order to evaluate the applicability of our approaches in rehabilitation.

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Section: Discussionmentioning

confidence: 99%

Section: Perpendicular Error Amplificationmentioning

confidence: 99%

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Experimental Evaluation of a Mixed Controller That Amplifies Spatial Errors and Reduces Timing Errors

et al. 2017

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“…In particular, motor learning theories have been used as an important tool to investigate, improve and adapt neurorehabilitation exercises (Dietz and Ward 2015). There is increasing interest in using robotic devices to train new motor skills, since robots can physically interact with subjects in order to assist or resist during training of complex movements, such as multi-joint arm movements (Nef et al 2009;Klein et al 2010) and walking (Riener et al 2005;Rüdt et al 2016). There has been a progression in the development of robotic training strategies that specify how these robotic devices should interact with subjects in order to enhance motor learning and rehabilitation outcomes -see Marchal-Crespo and Reinkensmeyer (2009) for a review.…”

Section: Introductionmentioning

confidence: 99%

The effectiveness of robotic training depends on motor task characteristics

2017

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Previous research suggests that the effectiveness of robotic training depends on the motor task to be learned. However, it is still an open question which specific task's characteristics influence the efficacy of error-modulating training strategies. Motor tasks can be classified based on the time characteristics of the task, in particular the task's duration (discrete vs. continuous). Continuous tasks require movements without distinct beginning or end. Discrete tasks require fast movements that include well-defined postures at the beginning and the end. We developed two games, one that requires a continuous movement-a tracking task-and one that requires discrete movements-a fast reaching task. We conducted an experiment with thirty healthy subjects to evaluate the effectiveness of three error-modulating training strategies-no guidance, error amplification (i.e., repulsive forces proportional to errors) and haptic guidance-on self-reported motivation and learning of the continuous and discrete games. Training with error amplification resulted in better motor learning than haptic guidance, besides the fact that error amplification reduced subjects' interest/enjoyment and perceived competence during training. Only subjects trained with error amplification improved their performance after training the discrete game. In fact, subjects trained without guidance improved the performance in the continuous game significantly more than in the discrete game, probably because the continuous task required greater attentional levels. Error-amplifying training strategies have a great potential to provoke better motor learning in continuous and discrete tasks. However, their long-lasting negative effects on motivation might limit their applicability in intense neurorehabilitation programs.

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“…In this kind of therapy, healthy lower limb movement is supported by robotic devices, improving the rehabilitation process of patients with partial loss of walking ability (Fisher et al, 2011;Jezernik et al, 2003;Rudt et al, 2016;Saggini et al, 2016). Data can be used as expected values in a "patient-in-control" therapy, and as control elements for "personalized" gait therapy, since the data can reflect the characteristic patterns of the gait according to the age and weight of the individual.…”

Section: Discussionmentioning

confidence: 99%

Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks

Caparelli

Naves

2017

Res. Biomed. Eng.

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Introduction: Historically, assessing the quality of human gait has been a difficult process. Advanced studies can be conducted using modern 3D systems. However, due to their high cost, usage of these 3D systems is still restricted to research environments. 2D systems offer simpler and more affordable solutions. Methods: In this study, the gait of 40 volunteers walking on a treadmill was recorded in the sagittal plane, using a 2D motion capture system. The extracted joint angles data were used to create cyclograms. Sections of the cyclograms were used as inputs to artificial neural networks (ANNs), since they can represent the kinematic behavior of the lower body. This allowed for prediction of future states of the moving body. Results: The results indicate that ANNs can predict the future states of the gait with high accuracy. Both single point and section predictions were successfully performed. Pearson's correlation coefficient and matched-pairs t-test ensured that the results were statistically significant. Conclusion: The combined use of ANNs and simple, accessible hardware is of great value in clinical practice. The use of cyclograms facilitates the analysis, as several gait characteristics can be easily recognized by their geometric shape. The predictive model presented in this paper facilitates generation of data that can be used in robotic locomotion therapy as a control signal or feedback element, aiding in the rehabilitation process of patients with motor dysfunction. The system proposes an interesting tool that can be explored to increase rehabilitation possibilities, providing better quality of life to patients.

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Towards more efficient robotic gait training: A novel controller to modulate movement errors

Cited by 12 publications

References 23 publications

Experimental Evaluation of a Mixed Controller That Amplifies Spatial Errors and Reduces Timing Errors

Experimental Evaluation of a Mixed Controller That Amplifies Spatial Errors and Reduces Timing Errors

The effectiveness of robotic training depends on motor task characteristics

Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks

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