ArticlePloS one2023
Comparing end-effector position and joint angle feedback for online robotic limb tracking.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Psychometric evaluation of high-resolution electrotactile interface for conveying 3D spatial information.Scientific reports · 2024Article
- From rubber hands to neuroprosthetics: Neural correlates of embodiment.Neuroscience and biobehavioral reviews · 2023Review
- Integration of proprioception in upper limb prostheses through non-invasive strategies: a review.Journal of neuroengineering and rehabilitation · 2023Review
- Exploring crossmodal correspondences for future research in human movement augmentation.Frontiers in psychology · 2023Article
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Authors and funding
5 authors.
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Abstract
Somatosensation greatly increases the ability to control our natural body. This suggests that supplementing vision with haptic sensory feedback would also be helpful when a user aims at controlling a robotic arm proficiently. However, whether the position of the robot and its continuous update should be coded in a extrinsic or intrinsic reference frame is not known. Here we compared two different supplementary feedback contents concerning the status of a robotic limb in 2-DoFs configuration: one encoding the Cartesian coordinates of the end-effector of the robotic arm (i.e., Task-space feedback) and another and encoding the robot joints angles (i.e., Joint-space feedback). Feedback was delivered to blindfolded participants through vibrotactile stimulation applied on participants' leg. After a 1.5-hour training with both feedbacks, participants were significantly more accurate with Task compared to Joint-space feedback, as shown by lower position and aiming errors, albeit not faster (i.e., similar onset delay). However, learning index during training was significantly higher in Joint space feedback compared to Task-space feedback. These results suggest that Task-space feedback is probably more intuitive and more suited for activities which require short training sessions, while Joint space feedback showed potential for long-term improvement. We speculate that the latter, despite performing worse in the present work, might be ultimately more suited for applications requiring long training, such as the control of supernumerary robotic limbs for surgical robotics, heavy industrial manufacturing, or more generally, in the context of human movement augmentation.
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