3,568 research outputs found
Haptics: Science, Technology, Applications
This open access book constitutes the proceedings of the 13th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2022, held in Hamburg, Germany, in May 2022. The 36 regular papers included in this book were carefully reviewed and selected from 129 submissions. They were organized in topical sections as follows: haptic science; haptic technology; and haptic applications
Haptics: Science, Technology, Applications
This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility
A survey of dextrous manipulation
technical reportThe development of mechanical end effectors capable of dextrous manipulation is a rapidly growing and quite successful field of research. It has in some sense put the focus on control issues, in particular, how to control these remarkably humanlike manipulators to perform the deft movement that we take for granted in the human hand. The kinematic and control issues surrounding manipulation research are clouded by more basic concerns such as: what is the goal of a manipulation system, is the anthropomorphic or functional design methodology appropriate, and to what degree does the control of the manipulator depend on other sensory systems. This paper examines the potential of creating a general purpose, anthropomorphically motivated, dextrous manipulation system. The discussion will focus on features of the human hand that permit its general usefulness as a manipulator. A survey of machinery designed to emulate these capabilities is presented. Finally, the tasks of grasping and manipulation are examined from the control standpoint to suggest a control paradigm which is descriptive, yet flexible and computationally efficient1
Spatial-Temporal Characteristics of Multisensory Integration
abstract: We experience spatial separation and temporal asynchrony between visual and
haptic information in many virtual-reality, augmented-reality, or teleoperation systems.
Three studies were conducted to examine the spatial and temporal characteristic of
multisensory integration. Participants interacted with virtual springs using both visual and
haptic senses, and their perception of stiffness and ability to differentiate stiffness were
measured. The results revealed that a constant visual delay increased the perceived stiffness,
while a variable visual delay made participants depend more on the haptic sensations in
stiffness perception. We also found that participants judged stiffness stiffer when they
interact with virtual springs at faster speeds, and interaction speed was positively correlated
with stiffness overestimation. In addition, it has been found that participants could learn an
association between visual and haptic inputs despite the fact that they were spatially
separated, resulting in the improvement of typing performance. These results show the
limitations of Maximum-Likelihood Estimation model, suggesting that a Bayesian
inference model should be used.Dissertation/ThesisDoctoral Dissertation Human Systems Engineering 201
Enhancing the use of Haptic Devices in Education and Entertainment
This research was part of the two-years Horizon 2020 European Project "weDRAW". The aim of the project was that "specific sensory systems have specific roles to learn specific concepts". This work explores the use of the haptic modality, stimulated by the means of force-feedback devices, to convey abstract concepts inside virtual reality. After a review of the current use of haptic devices in education, available haptic software and game engines, we focus on the implementation of an haptic plugin for game engines (HPGE, based on state of the art rendering library CHAI3D) and its evaluation in human perception experiments and multisensory integration
Wearable Vibrotactile Haptic Device for Stiffness Discrimination during Virtual Interactions
In this paper, we discuss the development of cost effective, wireless, and wearable vibrotactile haptic device for stiffness perception during an interaction with virtual objects. Our experimental setup consists of haptic device with five vibrotactile actuators, virtual
reality environment tailored in Unity 3D integrating the Oculus Rift Head Mounted Display (HMD) and the Leap Motion controller. The virtual environment is able to capture touch inputs from users. Interaction forces are then rendered at 500 Hz and fed back to the wearable setup stimulating fingertips with ERM vibrotactile actuators. Amplitude and frequency of vibrations are modulated proportionally to the interaction force to simulate the stiffness of a virtual object. A quantitative and qualitative study is done to compare the discrimination of stiffness on virtual linear spring in three sensory modalities: visual only feedback, tactile only feedback, and their combination. A common psychophysics method called the Two Alternative Forced Choice (2AFC) approach is used for quantitative analysis using Just Noticeable Difference (JND) and Weber Fractions (WF). According to the psychometric experiment result, average Weber fraction values of 0.39 for visual only feedback was improved to 0.25 by adding the tactile feedback
Active Haptic Exploration of Softness: Indentation Force Is Systematically Related to Prediction, Sensation and Motivation
Active finger movements play a crucial role in natural haptic perception. For the perception of different haptic properties people use different well-chosen movement schemes (Lederman and Klatzky, 1987). The haptic property of softness is stereotypically judged by repeatedly pressing one’s finger against an objects’ surface, actively indenting the object. It has been shown that people adjust the peak indentation forces of their pressing movements to the expected stimulus’ softness in order to improve perception (Kaim and Drewing, 2011). Here, we aim to clarify the mechanisms underlying such adjustments. We disentangle how people modulate executed peak indentation forces depending on predictive vs. sensory signals to softness, and investigate the influence of the participants’ motivational state on movement adjustments. In Experiment 1, participants performed a two alternative forced-choice (2AFC) softness discrimination task for stimulus pairs from one of four softness categories. We manipulated the predictability of the softness category. Either all stimuli of the same category were presented in a blocked fashion, which allowed predicting the softness category of the upcoming pair (predictive signals high), or stimuli from different categories were randomly intermixed, which made prediction impossible (predictive signals low). Sensory signals to softness category of the two stimuli in a pair are gathered during exploration. We contrasted the first indentation (sensory signals low) and last indentation (sensory signals high) in order to examine the effect of sensory signals. The results demonstrate that participants systematically apply lower forces when softer objects (as compared to harder objects) are indicated by predictive signals. Notably, sensory signals seemed to be not as relevant as predictive signals. However, in Experiment 2, we manipulated participant motivation by introducing rewards for good performance, and showed that the use of sensory information for movement adjustments can be fostered by high motivation. Overall, the present study demonstrates that exploratory movements are adjusted to the actual perceptual situation and that in the process of fine-tuning, closed- and open-loop mechanisms interact, with varying contributions depending on the observer’s motivation
Haptics: Science, Technology, Applications
This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility
Understanding motor control in humans to improve rehabilitation robots
Recent reviews highlighted the limited results of robotic rehabilitation and the low quality of evidences in this field. Despite the worldwide presence of several robotic infrastructures, there is still a lack of knowledge about the capabilities of robotic training effect on the neural control of movement. To fill this gap, a step back to motor neuroscience is needed: the understanding how the brain works in the generation of movements, how it adapts to changes and how it acquires new motor skills is fundamental. This is the rationale behind my PhD project and the contents of this thesis: all the studies included in fact examined changes in motor control due to different destabilizing conditions, ranging from external perturbations, to self-generated disturbances, to pathological conditions. Data on healthy and impaired adults have been collected and quantitative and objective information about kinematics, dynamics, performance and learning were obtained for the investigation of motor control and skill learning. Results on subjects with cervical dystonia show how important assessment is: possibly adequate treatments are missing because the physiological and pathological mechanisms underlying sensorimotor control are not routinely addressed in clinical practice. These results showed how sensory function is crucial for motor control. The relevance of proprioception in motor control and learning is evident also in a second study. This study, performed on healthy subjects, showed that stiffness control is associated with worse robustness to external perturbations and worse learning, which can be attributed to the lower sensitiveness while moving or co-activating. On the other hand, we found that the combination of higher reliance on proprioception with \u201cdisturbance training\u201d is able to lead to a better learning and better robustness. This is in line with recent findings showing that variability may facilitate learning and thus can be exploited for sensorimotor recovery. Based on these results, in a third study, we asked participants to use the more robust and efficient strategy in order to investigate the control policies used to reject disturbances. We found that control is non-linear and we associated this non-linearity with intermittent control. As the name says, intermittent control is characterized by open loop intervals, in which movements are not actively controlled. We exploited the intermittent control paradigm for other two modeling studies. In these studies we have shown how robust is this model, evaluating it in two complex situations, the coordination of two joints for postural balance and the coordination of two different balancing tasks. It is an intriguing issue, to be addressed in future studies, to consider how learning affects intermittency and how this can be exploited to enhance learning or recovery. The approach, that can exploit the results of this thesis, is the computational neurorehabilitation, which mathematically models the mechanisms underlying the rehabilitation process, with the aim of optimizing the individual treatment of patients. Integrating models of sensorimotor control during robotic neurorehabilitation, might lead to robots that are fully adaptable to the level of impairment of the patient and able to change their behavior accordingly to the patient\u2019s intention. This is one of the goals for the development of rehabilitation robotics and in particular of Wristbot, our robot for wrist rehabilitation: combining proper assessment and training protocols, based on motor control paradigms, will maximize robotic rehabilitation effects
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