Studying design process and example use with Macaron, a web-based vibrotactile effect editor

Schneider, Oliver; MacLean, Karon E.

doi:10.1109/haptics.2016.7463155

Cited by 43 publications

(19 citation statements)

References 33 publications

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“…Existing tools are built around the most important design parameters and approaches identified in the literature or by practitioners. Such tools use graphical, mathematical representations to edit the characteristics of the wave, including the amplitude, frequency, and duration of tactile sequences [8]- [10], [20]. For example, a web-based system [8] is an opportunity to edit and observe the effect of high availability.…”

Section: ) Vibrotactile Authoring Toolmentioning

confidence: 99%

“…Such tools use graphical, mathematical representations to edit the characteristics of the wave, including the amplitude, frequency, and duration of tactile sequences [8]- [10], [20]. For example, a web-based system [8] is an opportunity to edit and observe the effect of high availability. These studies provided a low-level, simple waveform timeline interface for combining them into vibrotactile patterns.…”

Section: ) Vibrotactile Authoring Toolmentioning

confidence: 99%

“…The vibrotactile design process consists of preparation, initial designing, and fine-tuning. It is known that the finetuning phase occupies more than half of all workloads [8]. Thus, improving productivity in the fine-tuning phase will contribute to the improvement of all workloads.…”

Section: Introductionmentioning

confidence: 99%

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GAN-Based Fine-Tuning of Vibrotactile Signals to Render Material Surfaces

2020

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The design productivity of fine-tuning for vibrotactile stimuli becomes important as consumer devices equipped with vibrotactile actuators will become widespread. The fine-tuned vibrotactile stimuli output by vibrotactile actuators allows the end-users to feel the surface of the virtual material. However, there is no suitable tool for fine-tuning while there are existing tools suitable for initial designing. In this paper, we test whether we can use GAN (Generative Adversarial Network)-based vibrotactile signal generator at the tuning phase. The generator provides a material-level interface to designers. Designers can define any intermediate materials among pre-defined 108 materials and obtain corresponding intermediate signals that the generator generates. We showed the applicability of the generator to the fine-tuning of vibrotactile signals from the viewpoints of principal component analysis and a user test. INDEX TERMS Human computer interaction, design tools, haptic interfaces.

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Section: ) Vibrotactile Authoring Toolmentioning

confidence: 99%

Section: ) Vibrotactile Authoring Toolmentioning

confidence: 99%

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GAN-Based Fine-Tuning of Vibrotactile Signals to Render Material Surfaces

2020

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“…Schneider suggests using visual or audio proxies for speeding up design cycles. Schneider and colleagues present various design tools [59] that support sketching [60] and leverage visual and acoustic proxies for designing [61] as well as low-f vibrotactile representations [62]. An orthogonal approach is exemplifed by Pescara et al who explore the use of evolutionary [50] and genetic algorithms [49] for personalizing vibrotactile patterns, in an iterative process.…”

Section: Compliance Illusionsmentioning

confidence: 99%

bARefoot

Strohmeier¹,

Güngör²,

Herres³

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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Many features of materials can be experienced through tactile cues, even using one's feet. For example, one can easily distinguish between moss and stone without looking at the ground. However, this type of material experience is largely not supported in AR and VR applications. We present bARefoot, a prototype shoe providing tactile impulses tightly coupled to motor actions. This enables generating virtual material experiences such as compliance, elasticity, or friction. To explore the parameter space of such sensorimotor coupled vibrations, we present a design tool enabling rapid design of virtual materials. We report initial explorations to increase understanding of how parameters can be optimized for generating compliance, and to examine the effect of dynamic parameters on material experiences. Finally, we present a series of use cases that demonstrate the potential of bARefoot for VR and AR.

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“…Breathing behaviors were designed by extending a keyframe-based editor for vibrotactile sensations (i.e., Macaron [96]) to robot position control, the result was named 'MacaronBit'. To design different breathing patterns a user starts with a pure sine wave and can adjust its parameters (i.e., frequency, amplitude, bias and amplitude/frequency variability).…”

Section: Design and Selection Of Breathing Behaviorsmentioning

confidence: 99%

Socially intelligent robots that understand and respond to human touch

Jung¹

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Touch is an important nonverbal form of interpersonal interaction which is used to communicate emotions and other social messages. As interactions with social robots are likely to become more common in the near future these robots should also be able to engage in tactile interaction with humans. Therefore, the aim of the research presented in this dissertation is to work towards socially intelligent robots that can understand and respond to human touch. To become a socially intelligent actor a robot must be able to sense, classify and interpret human touch and respond to this in an appropriate manner. To this end we present work that addresses different parts of this interaction cycle.After the introduction in Part I of the dissertation, we have taken a datadriven approach in Part II. We have focused on the sense and classify steps of the interaction cycle to automatically recognize social touch gestures such as pat, stroke and tickle from pressure sensor data.In Chapter 2 we present CoST: Corpus of Social Touch, a dataset containing 7805 captures of 14 different social touch gestures. All touch gestures were performed in three variants: gentle, normal and rough on a pressure sensitive mannequin arm. Recognition of these 14 gesture classes using various classifiers yielded accuracies of up to 60%; moreover, gentle gestures proved to be harder to classify than normal and rough gestures. We further investigated how different classifiers, interpersonal differences, gesture confusions and gesture variants affected the recognition accuracy.In Chapter 3 we describe the outcome of a machine learning challenge on touch gesture recognition. This challenge was extended to the research community working on multimodal interaction with the goal of sparking interest in the touch modality and to promote exploration of the use of data processing techniques from other more mature modalities for touch recognition. Two datasets were made available containing labeled pressure sensor data of social touch gestures: the CoST dataset presented in Chapter 2 and the Human-Animal Affective Robot Touch (HAART) gesture set. The most important outcomes of the challenges were: (1) transferring techniques from other modalities, such as image processing, speech, and human action recognition provided valuable feature sets; (2) gesture classification confusions were similar despite the various data processing methods that were used. vii In Part III of the dissertation we present three studies on the use of social touch in interaction with robot pets. We have mainly focused on the interpret and respond steps of the interaction cycle to identify which touch gestures a robot pet should understand, how touch can be interpreted within a social context and in which ways a robot can respond to human touch.In Chapter 4 we present a study of which the aim was to gain more insight into the factors that are relevant to interpret the meaning of touch within a social context. We elicited touch behaviors by letting participants interact with a robot pet com...

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Studying design process and example use with Macaron, a web-based vibrotactile effect editor

Cited by 43 publications

References 33 publications

GAN-Based Fine-Tuning of Vibrotactile Signals to Render Material Surfaces

GAN-Based Fine-Tuning of Vibrotactile Signals to Render Material Surfaces

bARefoot

Socially intelligent robots that understand and respond to human touch

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