Real-time rowing simulator with multimodal feedback

Zitzewitz, Joachim von; Wolf, Peter; Novakovic, Vladimir; Wellner, Mathias; Rauter, Georg; Brunschweiler, Andreas; Riener, Robert

doi:10.1002/jst.65

Cited by 39 publications

(9 citation statements)

References 16 publications

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“…During each experiment, the participant was seated in an actual (trimmed) rowing boat, set up in the middle of a CAVE (Cave Automated Virtual Environment; von Zitzewitz et al, 2008). Reflective markers were attached to a trimmed sweep rowing oar to track its movement with an optoelectrical motion tracking system (Qualisys, Gothenburg, Sweden).…”

Section: Apparatus and Taskmentioning

confidence: 99%

Terminal Feedback Outperforms Concurrent Visual, Auditory, and Haptic Feedback in Learning a Complex Rowing-Type Task

Sigrist

Rauter

Riener

et al. 2013

Journal of Motor Behavior

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Augmented feedback, provided by coaches or displays, is a well-established strategy to accelerate motor learning. Frequent terminal feedback and concurrent feedback have been shown to be detrimental for simple motor task learning but supportive for complex motor task learning. However, conclusions on optimal feedback strategies have been mainly drawn from studies on artificial laboratory tasks with visual feedback only. Therefore, the authors compared the effectiveness of learning a complex, 3-dimensional rowing-type task with either concurrent visual, auditory, or haptic feedback to self-controlled terminal visual feedback. Results revealed that terminal visual feedback was most effective because it emphasized the internalization of task-relevant aspects. In contrast, concurrent feedback fostered the correction of task-irrelevant errors, which hindered learning. The concurrent visual and haptic feedback group performed much better during training with the feedback than in nonfeedback trials. Auditory feedback based on sonification of the movement error was not practical for training the 3-dimensional movement for most participants. Concurrent multimodal feedback in combination with terminal feedback may be most effective, especially if the feedback strategy is adapted to individual preferences and skill level. ABSTRACT. Augmented feedback, provided by coaches or displays, is a well-established strategy to accelerate motor learning. Frequent terminal feedback and concurrent feedback have been shown to be detrimental for simple motor task learning but supportive for complex motor task learning. However, conclusions on optimal feedback strategies have been mainly drawn from studies on artificial laboratory tasks with visual feedback only. Therefore, the authors compared the effectiveness of learning a complex, 3-dimensional rowing-type task with either concurrent visual, auditory, or haptic feedback to self-controlled terminal visual feedback. Results revealed that terminal visual feedback was most effective because it emphasized the internalization of task-relevant aspects. In contrast, concurrent feedback fostered the correction of taskirrelevant errors, which hindered learning. The concurrent visual and haptic feedback group performed much better during training with the feedback than in nonfeedback trials. Auditory feedback based on sonification of the movement error was not practical for training the 3-dimensional movement for most participants. Concurrent multimodal feedback in combination with terminal feedback may be most effective, especially if the feedback strategy is adapted to individual preferences and skill level.

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Section: Apparatus and Taskmentioning

confidence: 99%

Terminal Feedback Outperforms Concurrent Visual, Auditory, and Haptic Feedback in Learning a Complex Rowing-Type Task

Sigrist

Rauter

Riener

et al. 2013

Journal of Motor Behavior

Self Cite

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show abstract

“…Also the motor drives (S700, Danaher, Pennsylvania, USA) are addressed via EtherCAT. Additional sensors needed inside the CAVE for specific applications, e.g., a rowing simulator, 28 are digitized by further terminals connected to the EtherCAT net.…”

Section: Signal Processingmentioning

confidence: 99%

A reconfigurable, tendon-based haptic interface for research into human-environment interactions

et al. 2012

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(2013). A reconfigurable, tendon-based haptic interface for research into human-environment interactions. Robotica, 31(03) SUMMARYHuman reaction to external stimuli can be investigated in a comprehensive way by using a versatile virtual-reality setup involving multiple display technologies. It is apparent that versatility remains a main challenge when human reactions are examined through the use of haptic interfaces as the interfaces must be able to cope with the entire range of diverse movements and forces/torques a human subject produces. To address the versatility challenge, we have developed a large-scale reconfigurable tendon-based haptic interface which can be adapted to a large variety of task dynamics and is integrated into a Cave Automatic Virtual Environment (CAVE). To prove the versatility of the haptic interface, two tasks, incorporating once the force and once the velocity extrema of a human subject's extremities, were implemented: a simulator with 3-DOF highly dynamic force feedback and a 3-DOF setup optimized to perform dynamic movements. In addition, a 6-DOF platform capable of lifting a human subject off the ground was realized. For these three applications, a position controller was implemented, adapted to each task, and tested. In the controller tests with highly different, taskspecific trajectories, the three robot configurations fulfilled the demands on the application-specific accuracy which illustrates and confirms the versatility of the developed haptic interface.

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“…The SPRINT mechanical platform is the physical core of the system because it allows subjects to experience rowing with kinematics and dynamics similar to natural rowing, supporting both sweep and sculling styles. An alternative design for a rowing simulator is presented in [7]. The mechanics has been designed and tuned by using known force profiles from literature and then by integrating them with data acquisition on the Concept2 system.…”

Section: System Design and Evolutionmentioning

confidence: 99%