Evidence map›Paper›PMID 35271188›Full record

ArticleSensors (Basel, Switzerland)2022

Improving Haptic Response for Contextual Human Robot Interaction.

Stanley Mugisha, Vamsi Krisha Guda, Christine Chevallereau, Matteo Zoppi, Rezia Molfino, Damien Chablat

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Stanley MugishaDipartimento di Ingegneria Meccanica, Energetica, Gestionale e dei Trasporti, University of Genova, Via All'Opera Pia, 15, 16145 Genova, Italy.ORCID 0000-0002-0046-6850
Vamsi Krisha GudaCNRS, LS2N, UMR 6004, 1 Rue de la Noë, 44321 Nantes, France.
Christine ChevallereauCNRS, LS2N, UMR 6004, 1 Rue de la Noë, 44321 Nantes, France.
Matteo ZoppiDipartimento di Ingegneria Meccanica, Energetica, Gestionale e dei Trasporti, University of Genova, Via All'Opera Pia, 15, 16145 Genova, Italy.
Rezia MolfinoDipartimento di Ingegneria Meccanica, Energetica, Gestionale e dei Trasporti, University of Genova, Via All'Opera Pia, 15, 16145 Genova, Italy.
Damien ChablatCNRS, LS2N, UMR 6004, 1 Rue de la Noë, 44321 Nantes, France.ORCID 0000-0001-7847-6162

Funding

LobbyBot project ANR-17-CE33regione liguria ARGE17-992/10/1
6 · The paper itself

Abstract

For haptic interaction, a user in a virtual environment needs to interact with proxies attached to a robot. The device must be at the exact location defined in the virtual environment in time. However, due to device limitations, delays are always unavoidable. One of the solutions to improve the device response is to infer human intended motion and move the robot at the earliest time possible to the desired goal. This paper presents an experimental study to improve the prediction time and reduce the robot time taken to reach the desired position. We developed motion strategies based on the hand motion and eye-gaze direction to determine the point of user interaction in a virtual environment. To assess the performance of the strategies, we conducted a subject-based experiment using an exergame for reach and grab tasks designed for upper limb rehabilitation training. The experimental results in this study revealed that eye-gaze-based prediction significantly improved the detection time by 37% and the robot time taken to reach the target by 27%. Further analysis provided more insight on the effect of the eye-gaze window and the hand threshold on the device response for the experimental task.

Indexed as

RoboticsHandHaptic TechnologyHumansMotivationUpper Extremityeye–gaze trackinghaptic deviceshuman–robot interactionresponse timevirtual reality

Identifiers

PMID35271188
PMCPMC8914947

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.