Improving Haptic Response for Contextual Human Robot Interaction
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 devi...
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| Published in | Sensors (Basel, Switzerland) Vol. 22; no. 5; p. 2040 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
MDPI AG
05.03.2022
MDPI |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s22052040 |
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| 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. |
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| AbstractList | For haptic applications, 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 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 prediction time and reduce the robot time 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. Experimental results in this study revealed that eye-gaze-based prediction significantly improved detection time by 37% and the robot time to reach the target by 27%. Further analysis provided more insight on the effect of eye-gaze window and the hand threshold on the device response for the experimental task. 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. 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.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. |
| Author | Mugisha, Stanley Chablat, Damien Zoppi, Matteo Guda, Vamsi Krisha Chevallereau, Christine Molfino, Rezia |
| AuthorAffiliation | 2 CNRS, LS2N, UMR 6004, 1 Rue de la Noë, 44321 Nantes, France; vamsikrishna.guda@ls2n.fr (V.K.G.); christine.chevallereau@ls2n.fr (C.C.); damien.chablat@cnrs.fr (D.C.) 1 Dipartimento di Ingegneria Meccanica, Energetica, Gestionale e dei Trasporti, University of Genova, Via All’Opera Pia, 15, 16145 Genova, Italy; matteo.zoppi@unige.it (M.Z.); rezia.molfino@unige.it (R.M.) |
| AuthorAffiliation_xml | – name: 2 CNRS, LS2N, UMR 6004, 1 Rue de la Noë, 44321 Nantes, France; vamsikrishna.guda@ls2n.fr (V.K.G.); christine.chevallereau@ls2n.fr (C.C.); damien.chablat@cnrs.fr (D.C.) – name: 1 Dipartimento di Ingegneria Meccanica, Energetica, Gestionale e dei Trasporti, University of Genova, Via All’Opera Pia, 15, 16145 Genova, Italy; matteo.zoppi@unige.it (M.Z.); rezia.molfino@unige.it (R.M.) |
| Author_xml | – sequence: 1 givenname: Stanley orcidid: 0000-0002-0046-6850 surname: Mugisha fullname: Mugisha, Stanley – sequence: 2 givenname: Vamsi Krisha surname: Guda fullname: Guda, Vamsi Krisha – sequence: 3 givenname: Christine surname: Chevallereau fullname: Chevallereau, Christine – sequence: 4 givenname: Matteo surname: Zoppi fullname: Zoppi, Matteo – sequence: 5 givenname: Rezia surname: Molfino fullname: Molfino, Rezia – sequence: 6 givenname: Damien orcidid: 0000-0001-7847-6162 surname: Chablat fullname: Chablat, Damien |
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| Cites_doi | 10.1109/THMS.2014.2303083 10.1016/j.heliyon.2018.e00526 10.1080/01621459.1972.10481232 10.1145/3290605.3300589 10.1016/j.brainresbull.2010.02.009 10.1145/3313831.3376476 10.1109/IROS40897.2019.8968559 10.1109/ICAR53236.2021.9659320 10.1109/TVCG.2020.2973060 10.1145/3379337.3415870 10.1007/978-1-4613-8122-8 10.1007/s002210100745 10.1109/IROS40897.2019.8968192 10.1145/3282894.3282898 10.1109/TSMC.2015.2468675 10.1145/3313831.3376286 10.1109/TASE.2016.2624279 10.1162/pres.1997.6.1.118 10.1109/SSCI.2017.8285207 10.1109/IROS.2017.8206572 10.1109/HRI.2016.7451747 10.1145/3003715.3005461 10.1145/3334480.3382924 10.1109/WHC.2011.5945483 10.1109/ACCESS.2020.3045994 10.1111/cgf.12603 10.1109/IROS40897.2019.8967927 10.1007/978-1-4471-6392-3 10.1145/3025453.3025753 10.1109/ROMAN.2011.6005258 10.1109/LRA.2020.3005892 10.3390/robotics10020054 10.1145/3173574.3173865 10.1109/ICRA.2011.5980248 10.1115/DETC2021-71518 10.1145/2839462.2839484 10.1109/VRAIS.1996.490509 10.3414/ME14-01-0125 10.1109/TCDS.2019.2959071 10.1145/3313831.3376523 10.1007/BF00229627 10.2307/2281326 |
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| Keywords | haptic devices virtual reality response time human–robot interaction eye–gaze tracking Haptic devices Human robot interaction Eye gaze prediction |
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| References | ref_50 ref_13 ref_12 ref_51 ref_18 Callens (ref_34) 2020; 5 ref_17 Ruhland (ref_38) 2015; 34 ref_16 ref_15 Strauss (ref_19) 2020; 26 Guo (ref_11) 2021; 13 Alves (ref_14) 2016; 55 Ravichandar (ref_30) 2017; 14 Gruenbaum (ref_22) 1997; 6 ref_25 ref_24 ref_23 ref_21 ref_20 Lee (ref_3) 2016; 46 ref_29 Shapiro (ref_45) 1972; 67 ref_28 ref_27 ref_26 Zaraki (ref_10) 2014; 44 ref_36 Tukey (ref_47) 1952; 47 ref_35 ref_33 ref_31 Esfahlani (ref_39) 2018; 4 ref_37 Ernst (ref_4) 2011; 85 Pelz (ref_6) 2001; 139 ref_46 ref_44 ref_43 ref_42 ref_41 ref_40 ref_1 Li (ref_32) 2020; 8 ref_2 ref_49 ref_48 ref_9 ref_8 ref_5 Smeets (ref_7) 1996; 109 |
| References_xml | – volume: 44 start-page: 157 year: 2014 ident: ref_10 article-title: Designing and Evaluating a Social Gaze-Control System for a Humanoid Robot publication-title: IEEE Trans. Hum.-Mach. Syst. doi: 10.1109/THMS.2014.2303083 – volume: 4 start-page: e00526 year: 2018 ident: ref_39 article-title: ReHabgame: A non-immersive virtual reality rehabilitation system with applications in neuroscience publication-title: Heliyon doi: 10.1016/j.heliyon.2018.e00526 – ident: ref_49 – ident: ref_51 – volume: 67 start-page: 215 year: 1972 ident: ref_45 article-title: An approximate analysis of variance test for normality publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1972.10481232 – ident: ref_29 doi: 10.1145/3290605.3300589 – volume: 85 start-page: 245 year: 2011 ident: ref_4 article-title: Effects of visual-haptic asynchronies and loading-unloading movements on compliance perception publication-title: Brain Res. Bull. doi: 10.1016/j.brainresbull.2010.02.009 – ident: ref_25 doi: 10.1145/3313831.3376476 – ident: ref_31 doi: 10.1109/IROS40897.2019.8968559 – ident: ref_13 doi: 10.1109/ICAR53236.2021.9659320 – ident: ref_8 – volume: 26 start-page: 1955 year: 2020 ident: ref_19 article-title: A Steering Algorithm for Redirected Walking Using Reinforcement Learning publication-title: IEEE Trans. Vis. Comput. Graph. doi: 10.1109/TVCG.2020.2973060 – ident: ref_2 doi: 10.1145/3379337.3415870 – ident: ref_41 – ident: ref_46 doi: 10.1007/978-1-4613-8122-8 – volume: 139 start-page: 266 year: 2001 ident: ref_6 article-title: The coordination of eye, head, and hand movements in a natural task publication-title: Exp. Brain Res. doi: 10.1007/s002210100745 – ident: ref_17 – ident: ref_35 doi: 10.1109/IROS40897.2019.8968192 – ident: ref_28 doi: 10.1145/3282894.3282898 – volume: 46 start-page: 1098 year: 2016 ident: ref_3 article-title: Impact of Visual-Haptic Spatial Discrepancy on Targeting Performance publication-title: IEEE Trans. Syst. Man Cybern. Syst. doi: 10.1109/TSMC.2015.2468675 – ident: ref_20 – ident: ref_27 doi: 10.1145/3313831.3376286 – volume: 14 start-page: 855 year: 2017 ident: ref_30 article-title: Human Intention Inference Using Expectation-Maximization Algorithm With Online Model Learning publication-title: IEEE Trans. Autom. Sci. Eng. doi: 10.1109/TASE.2016.2624279 – volume: 6 start-page: 118 year: 1997 ident: ref_22 article-title: Implementation of Dynamic Robotic Graphics for a Virtual Control Panel publication-title: Presence Teleoper. Virtual Environ. doi: 10.1162/pres.1997.6.1.118 – ident: ref_16 doi: 10.1109/SSCI.2017.8285207 – ident: ref_33 doi: 10.1109/IROS.2017.8206572 – ident: ref_43 doi: 10.1109/HRI.2016.7451747 – ident: ref_37 doi: 10.1145/3003715.3005461 – ident: ref_48 doi: 10.1145/3334480.3382924 – ident: ref_9 doi: 10.1109/WHC.2011.5945483 – volume: 8 start-page: 227690 year: 2020 ident: ref_32 article-title: Data Driven Models for Human Motion Prediction in Human-Robot Collaboration publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3045994 – ident: ref_40 – volume: 34 start-page: 299 year: 2015 ident: ref_38 article-title: A Review of Eye Gaze in Virtual Agents, Social Robotics and HCI: Behaviour Generation, User Interaction and Perception publication-title: Comput. Graph. Forum doi: 10.1111/cgf.12603 – ident: ref_15 doi: 10.1109/IROS40897.2019.8967927 – ident: ref_44 – ident: ref_21 – ident: ref_50 doi: 10.1007/978-1-4471-6392-3 – ident: ref_18 doi: 10.1145/3025453.3025753 – ident: ref_42 doi: 10.1109/ROMAN.2011.6005258 – volume: 5 start-page: 5151 year: 2020 ident: ref_34 article-title: A Framework for Recognition and Prediction of Human Motions in Human-Robot Collaboration Using Probabilistic Motion Models publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2020.3005892 – ident: ref_12 doi: 10.3390/robotics10020054 – ident: ref_24 doi: 10.1145/3173574.3173865 – ident: ref_36 doi: 10.1109/ICRA.2011.5980248 – ident: ref_5 doi: 10.1115/DETC2021-71518 – ident: ref_23 doi: 10.1145/2839462.2839484 – ident: ref_1 doi: 10.1109/VRAIS.1996.490509 – volume: 55 start-page: 79 year: 2016 ident: ref_14 article-title: Eye Gaze Correlates of Motor Impairment in VR Observation of Motor Actions publication-title: Methods Inf. Med. doi: 10.3414/ME14-01-0125 – volume: 13 start-page: 179 year: 2021 ident: ref_11 article-title: A Novel Robotic Guidance System With Eye-Gaze Tracking Control for Needle-Based Interventions publication-title: IEEE Trans. Cogn. Dev. Syst. doi: 10.1109/TCDS.2019.2959071 – ident: ref_26 doi: 10.1145/3313831.3376523 – volume: 109 start-page: 434 year: 1996 ident: ref_7 article-title: Goal-directed arm movements change eye-head coordination publication-title: Exp. Brain Res. doi: 10.1007/BF00229627 – volume: 47 start-page: 554 year: 1952 ident: ref_47 article-title: Statistical Methods for Natural Scientists, Medical Men, and Engineers publication-title: J. Am. Stat. Assoc. doi: 10.2307/2281326 |
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| Snippet | 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... For haptic applications, a user in a virtual environment needs to interact with proxies attached to a robot. The device must be at the exact location defined... |
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| StartPage | 2040 |
| SubjectTerms | Computer Science eye–gaze tracking Hand - physiology haptic devices Haptic Technology Haptics Humans human–robot interaction Motivation Neural networks Principal components analysis response time Robotics Robotics - methods Robots Upper Extremity Virtual reality |
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| Title | Improving Haptic Response for Contextual Human Robot Interaction |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35271188 https://www.proquest.com/docview/2637791174 https://www.proquest.com/docview/2638713400 https://hal.science/hal-03599147 https://pubmed.ncbi.nlm.nih.gov/PMC8914947 https://doaj.org/article/90f6eea31c054d20b12f49874a9ebde7 |
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