Human–Robot Interaction

Sheridan, Thomas B.

doi:10.1177/0018720816644364

Cited by 509 publications

(134 citation statements)

References 22 publications

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“…Despite impressive advances in the recent decade, current robotics engineering is still distant from developing 297 robotic systems able to assist human individudals socially, especially during postural activities and balance 298 exercises (Sheridan, 2016). In the both rIPT conditions of the current study, the dynamics of the robotic system 299 were not independent but in one way or another a direct consequence of CR's movements.…”

Section: Robotic Control 205mentioning

confidence: 79%

Robotic light touch assists human balance control during maximum forward reaching

Johannsen

Potwar²,

Saveriano

et al. 2019

Preprint

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Ten healthy adult individuals performed maximum forward reaching (MFR) without visual feedback while 56 standing upright. We evaluated their control of reaching behaviour and of body balance during IPT provided by 57 either another human individual or by a robotic system in two alternative control modes (reactive vs predictive). 58 Results 59Changes in reaching behaviour under the robotic IPT, such as lower speed and straighter direction were linked 60 to reduced body sway. MFR of the contact receiver was influenced by the robotic control mode such as that a 61 predictive mode reduced movement variability and increased postural stability to a greater extend in comparison 62 to human IPT. The effects of the reactive robotic system, however, more closely resembled the effects of IPT 63 provided by human contact provider. 64 Conclusion 65The robotic IPT system was as supportive as human IPT. Robotic IPT seemed to afford more specific 66 adjustments, such as trading reduced speed for increased accuracy, to meet the intrinsic demands and constraints 67 of the robotic system. Possibly, IPT provided by a human contact provider reflected reactive interpersonal 68 postural coordination more similar to the robotic system's follower mode. 69 70 Keywords: interpersonal light touch, robotic assistance, body balance, forward reaching 71 72 73 Précis (<50 words) 74Interpersonal touch support by a robotic system was evaluated against support provided a human partner during 75 maximum forward reaching. 76Human contact receivers showed comparable benefits in their reaching postural performance between the 77 support conditions. 78Coordination with the robotic system, nevertheless, afforded specific adaptations in the reaching behaviour. 79We tested 10 healthy young adults ( average age=28.5, SD 3.35 years, 3 females and 7 males) as contact 126 receivers (CR) performing a maximum forward reaching (MFR) task. Participants were not affected by any 127 neurological or orthopedic indications. Participants were recruited as an opportunity sample from students of the 128 university. The study was approved by the ethical committee of the medical faculty of the TU Munich and all 129 participants gave written informed consent. 130 131 Equipment and experimental procedure 132One healthy adult, male contact provider (CP) applied the IPT to the wrists of the contact receivers (CR). The 133 CR stood blindfolded on a force plate (Bertec 4060, Columbus, OH, USA; 500 Hz) in bipedal stance performing 134 the MFR task. CR was always instructed to reach as far forward as possible by bending the torso but not the 135 knees. Before the start of a trial, CR was instructed to stand in a relaxed manner, the right arm extended at 136 shoulder height to reach horizontally above a height-adjusted table. After the start of a trial, CR was instructed 137 to remain static for at least 5 seconds (baseline) until an auditory signal cued the start of the MFR task (Fig. 1a). 138During IPT, CP stood facing orthogonally to CR in bipedal stance between CR's force plat...

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Section: Robotic Control 205mentioning

confidence: 79%

Robotic light touch assists human balance control during maximum forward reaching

Johannsen

Potwar²,

Saveriano

et al. 2019

Preprint

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“…RIFs on Trust were modeled, accordingly; namely Automation Etiquette and AW. Sheridan and Parasuraman (2005) identify influences affecting performance in human machine interaction. One influence highlighted and hence integrated in the present model (as a node) is Automation Etiquette.…”

Section: Bayesian Belief Networkmentioning

confidence: 99%

Risk modeling of autonomous underwater vehicle operation focusing on the human operator

Thieme¹,

Utne²,

Schjølberg³

2015

Safety and Reliability of Complex Engineered Systems

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“…For instance, it has been outlined as a fundamental need for robots that consciously navigate in the presence of humans, in order to develop a "human-aware" navigation strategy [1]. Additionally, fueled by recently proposed specifications (ISO/TS 15066:2016-Robots and robotic devices-Collaborative robots), it will likely be essential in the near future for the realization of the "intimate collaboration" or the "close proximity interaction" paradigms described by [2,3], respectively. In particular, studies have shown recently that human motion prediction technologies have the potential to improve the efficiency of the team and the perception of safety and comfort by the user in different settings in which persons and robots share the same physical workspace [4,5].…”

Section: Introductionmentioning

confidence: 99%

Walking Turn Prediction from Upper Body Kinematics: A Systematic Review with Implications for Human-Robot Interaction

López

Álvarez

2019

Applied Sciences

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Prediction of walking turns allows to improve human factors such as comfort and perceived safety in human-robot interaction. The current state-of-the-art suggests that upper body kinematics can be used for that purpose and contains evidence about the reliability and the quantitative anticipation that can be expected from different variables. However, the experimental methodology has not been consistent throughout the different works and the related data has not always been given in an explicit form, with different studies containing partial, complementary or even contradictory results. In this paper, with the purpose of providing a uniform view of the topic that can trigger new developments in the field, we performed a systematic review of the relevant literature addressing three main questions: (i) Which upper body kinematic variables permit to anticipate a walking turn? (ii) How long in advance can we anticipate the turn from them? (iii) What is the expected contribution of walking turn prediction systems from upper body kinematics for human-robot interaction? We have found that head yaw was the most reliable kinematical variable from the upper body to predict walking turns about 200ms. Trunk roll anticipates walking turns by a similar amount of time, but with less reliability. Both approaches may benefit human-robot interaction in close proximity, helping the robot to exhibit appropriate proxemic behavior interacting at intimate, personal or social distances. From the point of view of safety, they have to be considered with caution. Trunk yaw is not valid to anticipate turns. Gaze Yaw seems to be the earliest predictor, although existing evidence is still inconclusive.

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Human–Robot Interaction

Cited by 509 publications

References 22 publications

Robotic light touch assists human balance control during maximum forward reaching

Robotic light touch assists human balance control during maximum forward reaching

Risk modeling of autonomous underwater vehicle operation focusing on the human operator

Walking Turn Prediction from Upper Body Kinematics: A Systematic Review with Implications for Human-Robot Interaction

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