scholarly journals TouchGrid – Combining Touch Interaction with Musical Grid Interfaces

2021 ◽  
Author(s):  
Beat Rossmy ◽  
Sebastian Unger ◽  
Alexander Wiethoff
Keyword(s):  
Touch Interaction

scholarly journals Critical Examination of the Parametric Approaches to Analysis of the Non-Verbal Human Behavior: A Case Study in Facial Pre-Touch Interaction

2020 ◽  
Vol 10 (11) ◽  
pp. 3817
Author(s):  
Soheil Keshmiri ◽  
Masahiro Shiomi ◽  
Kodai Shatani ◽  
Takashi Minato ◽  
Hiroshi Ishiguro
Keyword(s):  
Normal Distribution ◽  
Human Subjects ◽  
Critical Examination ◽  
Reliable Estimate ◽  
Support Vector ◽  
Face Area ◽  
Behavioral Studies ◽  
Touch Interaction ◽  
Ordinary Regression ◽  
Simplifying Assumptions

A prevailing assumption in many behavioral studies is the underlying normal distribution of the data under investigation. In this regard, although it appears plausible to presume a certain degree of similarity among individuals, this presumption does not necessarily warrant such simplifying assumptions as average or normally distributed human behavioral responses. In the present study, we examine the extent of such assumptions by considering the case of human–human touch interaction in which individuals signal their face area pre-touch distance boundaries. We then use these pre-touch distances along with their respective azimuth and elevation angles around the face area and perform three types of regression-based analyses to estimate a generalized facial pre-touch distance boundary. First, we use a Gaussian processes regression to evaluate whether assumption of normal distribution in participants’ reactions warrants a reliable estimate of this boundary. Second, we apply a support vector regression (SVR) to determine whether estimating this space by minimizing the orthogonal distance between participants’ pre-touch data and its corresponding pre-touch boundary can yield a better result. Third, we use ordinary regression to validate the utility of a non-parametric regressor with a simple regularization criterion in estimating such a pre-touch space. In addition, we compare these models with the scenarios in which a fixed boundary distance (i.e., a spherical boundary) is adopted. We show that within the context of facial pre-touch interaction, normal distribution does not capture the variability that is exhibited by human subjects during such non-verbal interaction. We also provide evidence that such interactions can be more adequately estimated by considering the individuals’ variable behavior and preferences through such estimation strategies as ordinary regression that solely relies on the distribution of their observed behavior which may not necessarily follow a parametric distribution.

scholarly journals Multi-Touch Interaction Generation Device by Spatiotemporally Switching Electrodes

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1475
Author(s):  
Masahiro Okamoto ◽  
Kazuya Murao
Keyword(s):  
High Speed ◽  
High Accuracy ◽  
Conductive Ink ◽  
Control Board ◽  
Touch Panel ◽  
Electrode Size ◽  
Touch Input ◽  
Touch Interaction ◽  
Multiple Electrodes ◽  
Touch Panels

With the spread of devices equipped with touch panels, such as smartphones, tablets, and laptops, the opportunity for users to perform touch interaction has increased. In this paper, we constructed a device that generates multi-touch interactions to realize high-speed, continuous, or hands-free touch input on a touch panel. The proposed device consists of an electrode sheet printed with multiple electrodes using conductive ink and a voltage control board, and generates eight multi-touch interactions: tap, double-tap, long-press, press-and-tap, swipe, pinch-in, pinch-out, and rotation, by changing the capacitance of the touch panel in time and space. In preliminary experiments, we investigated the appropriate electrode size and spacing for generating multi-touch interactions, and then implemented the device. From the evaluation experiments, it was confirmed that the proposed device can generate multi-touch interactions with high accuracy. As a result, tap, press-and-tap, swipe, pinch-in, pinch-out, and rotation can be generated with a success rate of 100%. It was confirmed that all the multi-touch interactions evaluated by the proposed device could be generated with high accuracy and acceptable speed.

scholarly journals Differences between young and old users when interacting with a humanoid robot: a qualitative usability study

2018 ◽  
Vol 9 (1) ◽  
pp. 183-192 ◽  
Author(s):  
Ronit Feingold-Polak ◽  
Avital Elishay ◽  
Yonat Shahar ◽  
Maayan Stein ◽  
Yael Edan ◽  
...  
Keyword(s):  
Young Adults ◽  
Humanoid Robot ◽  
Motor Task ◽  
Humanoid Robots ◽  
Limited Information ◽  
Old Adults ◽  
Body Of Knowledge ◽  
People With Dementia ◽  
Touch Interaction ◽  
The Difference

Abstract With the aging of the population worldwide, humanoid robots are being used with an older population, e.g., stroke patients and people with dementia. There is a growing body of knowledge on how people interact with robots, but limited information on the difference between young and old adults in their preferences when interacting with humanoid robots and what factors influence these preferences.We developed a gamified robotic platform of a cognitive-motor task.We conducted two experiments with the following aims: to test how age, location of touch interaction (touching the robot’s tablet or hand), and embodied presence of a humanoid robot affect the motivation of different age-group users to continue performing a cognitive-motor task. A total of 60 participants (30 old adults and 30 young adults) took part in two experiments with the humanoid Pepper robot (Softbank robotics). Both old and young adults reported they enjoyed the interaction with the robot as they found it engaging and fun, and preferred the embodied robot over the non-embodied computer screen. This study highlights that in order for the experience of the user to be positive a personalization of the interaction according to the age, the needs of the user, the characteristics, and the pace of the task is needed.

Exploring the Causal Modeling of Human-Robot Touch Interaction

2019 ◽  
pp. 235-244
Author(s):  
Soheil Keshmiri ◽  
Hidenobu Sumioka ◽  
Takashi Minato ◽  
Masahiro Shiomi ◽  
Hiroshi Ishiguro
Keyword(s):  
Causal Modeling ◽  
Touch Interaction

scholarly journals Exploring Mobile Game Interactions

2018 ◽  
Vol 8 (5) ◽  
pp. 3954
Author(s):  
Pitsanu Chaichitwanidchakol ◽  
Witcha Feungchan
Keyword(s):  
Social Interaction ◽  
Mobile Games ◽  
Mobile Game ◽  
Time Interaction ◽  
Network Interaction ◽  
Touch Interaction ◽  
Movement Interaction ◽  
Game Industry ◽  
The Right ◽  
Location Interaction

The mobile game industry has been growing rapidly in both the number of games and revenues. Choosing the right interactions for a game has become a major challenge for developers. Some developers use inappropriate interactions in their games which causes them to be less fun than they should be. This research focuses on gathering and defining possible mobile game interactions so as to guide and enable designers and developers to choose the right interactions for their games. The researchers have extensively reviewed and explored various mobile game interactions both through research studies and through existing mobile games. Subsequent to observations, mobile game interactions were then categorized as follows: 1) Touch interaction 2) Motion/Movement interaction 3) Video interaction 4) Sound interaction 5) Special purpose interaction 6) Location interaction 7) Electroencep-halography (EEG) interaction 8) Date/Time interaction 9) Weather interaction 10) Light interaction 11) Proximity interaction 12) Network interaction 13) Social interaction and 14) Bioinformatics interaction. These 14 interactions can be used to support gameplay, ideas, and innovation of mobile games.

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