Towards Haptic Images: A Survey on Touchscreen-Based Surface Haptics

Costes, Antoine; Danieau, Fabien; Argelaguet, Ferran; Guillotel, Philippe; Lécuyer, Anatole

doi:10.1109/toh.2020.2984754

Cited by 21 publications

(12 citation statements)

References 45 publications

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“…The average classification accuracies with the 1, 3, and 5 min time scales were 44.44%, 35.92%, and 53.92%, respectively. Figure 10(e (1,3, and 5 min) of the same individual were analyzed. Using a t-test, the statistical differences between the task-performing and rest states were analyzed.…”

Section: Classification Accuracy Of Different Classifiers Withmentioning

confidence: 99%

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Comparison Analysis of Different Time‐Scale Heart Rate Variability Signals for Mental Workload Assessment in Human‐Robot Interaction

Shao

Wang

et al. 2021

Wireless Communications and Mobile Computing

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Excessive mental workload affects human health and may lead to accidents. This study is motivated by the need to assess mental workload in the process of human-robot interaction, in particular, when the robot performs a dangerous task. In this study, the use of heart rate variability (HRV) signals with different time scales in mental workload assessment was analyzed. A humanoid dual-arm robot that can perform dangerous work was used as a human-robot interaction object. Electrocardiogram (ECG) signals of six subjects were collected in two states: during the task and in a relaxed state. Multiple time-scale (1, 3, and 5 min) HRV signals were extracted from ECG signals. Then, we extracted the same linear and nonlinear features from the HRV signals at different time scales. The performance of machine learning algorithms using the different time-scale HRV signals obtained during the human-robot interaction was evaluated. The results show that for the per-subject case with a 3 min HRV signal length, the K -nearest neighbor classifier achieved the best mental workload classification performance. For the cross-subject case with a 5 min time-scale signal length, the gentle boost classifier achieved the best mental workload classification accuracy. This study provides a novel research idea for using HRV signals to measure mental workload during human-robot interaction.

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Section: Classification Accuracy Of Different Classifiers Withmentioning

confidence: 99%

“…Nowadays, robots, instead of humans, work in unstructured environments, expanding the scope of human work. Humans interact with robots through visual, tactile, and other feedback [1][2][3][4]. The robot can be operated remotely to complete a dangerous task; this operation can be challenging for humans.…”

Section: Introductionmentioning

confidence: 99%

Comparison Analysis of Different Time‐Scale Heart Rate Variability Signals for Mental Workload Assessment in Human‐Robot Interaction

Shao

Wang

et al. 2021

Wireless Communications and Mobile Computing

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“…There is high potential in using display-based interfaces, although these often come at the cost of usability and the need for relearning certain aspects of machine operation [6][7][8]. Other production branches like agriculture or logistics that went further in terms of digital transformation already use display-based interfaces on a large scale.…”

Section: Chances and Challenges Of Novel Concepts Of Machine Operationmentioning

confidence: 99%

“…Accordingly, touch operation might not guarantee good usability in specific situations. Despite the advancing technologies around touch interaction [7,8] the HMI concept, therefore, offers several operation modalities to the operator. In addition to touch inputs, the operator can use a control pad on the joystick to interact with the display interface.…”

Section: A Novel Human-machine Interface (Hmi) Concept For Excavator mentioning

confidence: 99%

UUX Evaluation of a Digitally Advanced Human–Machine Interface for Excavators

Lorenz

Helmert

Anders³

et al. 2020

MTI

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With the evaluation of a next-generation human–machine interface (HMI) concept for excavators, this study aims to discuss the HMI quality measurement based on usability and user experience (UUX) metrics. Regarding the digital transformation of construction sites, future work environments will have to be capable of presenting various complex visual data and enabling efficient and safe interactivity while working. The evaluated HMI focused on introducing a touch display-based interface, providing advanced operation functions and different interaction modalities. The assessment of UUX should show whether the novel HMI can be utilised to perform typical tasks (usability) and how it is accepted and assessed in terms of non-instrumental qualities (user experience, UX). Using the collected data, this article also aims to contribute to the general discussion about the role of UX beyond usability in industrial applications and deepen the understanding of non-instrumental qualities when it comes to user-oriented process and machine design. The exploratory study examines insights into the application of elaborated UUX measuring tools like the User Experience Questionnaire (UEQ) on the interaction with industrial goods accompanied by their rating with other tools, namely System Usability Scale (SUS), Intuitive Interaction Questionnaire (INTUI) and the National Aeronautics and Space Administration (NASA) Task Load Index (NASA-TLX). Four goals are pursued in this study. The first goal is to compare in-depth two different ways of interaction with the novel HMI—namely one by a control pad on the right joystick and one by touch. Therefore, a sample of 17 subjects in total was split into two groups and differences in UUX measures were tested. Secondly, the performances of both groups were tested over the course of trials to investigate possible differences in detail. The third goal is to interpret measures of usability and user experience against existing benchmark values. Fourth and finally, we use the data gathered to analyse correlations between measures of UUX. The results of our study show that the different ways of interaction did not impact any of the measures taken. In terms of detailed performance analysis, both groups yielded differences in terms of time per action, but not between the groups. The comparison of UUX measures with benchmark values yielded mixed results. The UUX measures show some relevant significant correlations. The participants mostly reported enjoying the use of the HMI concept, but several practical issues (e.g., efficiency) still need to be overcome. Once again, the study confirms the urge of user inclusion in product development. Especially in the course of digitalisation, as big scale advancements of systems and user interfaces bring uncertainty for many manufacturers regarding whether or how a feature should be integrated.

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“…The development of haptic technology, which was traditionally an important sense, was relatively unremarkable. However, since touch screens have been used as an input interface for mobile phones, the demand for haptic feedback has increased, and the surface haptics field in haptics has begun to emerge [1,2].…”

Section: Introductionmentioning

confidence: 99%

Development of a Human-Display Interface with Vibrotactile Feedback for Real-World Assistive Applications

Kim

Jeong

Cho

et al. 2021

Sensors

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It is important to operate devices with control panels and touch screens assisted by haptic feedback in mobile environments such as driving automobiles and electric power wheelchairs. A lot of consideration is needed to give accurate haptic feedback, especially, presenting clear touch feedback to the elderly and people with reduced sensation is a very critical issue from healthcare and safety perspectives. In this study, we aimed to identify the perceptual characteristics for the frequency and direction of haptic vibration on the touch screen with vehicle-driving vibration and to propose an efficient haptic system based on these characteristics. As a result, we demonstrated that the detection threshold shift decreased at frequencies above 210 Hz due to the contact pressure during active touch, but the detection threshold shift increased at below 210 Hz. We found that the detection thresholds were 0.30–0.45 gpeak with similar sensitivity in the 80–270 Hz range. The haptic system implemented by reflecting the experimental results achieved characteristics suitable for use scenarios in automobiles. Ultimately, it could provide practical guidelines for the development of touch screens to give accurate touch feedback in the real-world environment.

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Towards Haptic Images: A Survey on Touchscreen-Based Surface Haptics

Cited by 21 publications

References 45 publications

Comparison Analysis of Different Time‐Scale Heart Rate Variability Signals for Mental Workload Assessment in Human‐Robot Interaction

Comparison Analysis of Different Time‐Scale Heart Rate Variability Signals for Mental Workload Assessment in Human‐Robot Interaction

UUX Evaluation of a Digitally Advanced Human–Machine Interface for Excavators

Development of a Human-Display Interface with Vibrotactile Feedback for Real-World Assistive Applications

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