Using Touch Gestures in Interactive Playgrounds

2017 ◽  
pp. 143-166
Author(s):  
Jesse Feiler
Keyword(s):  
2011 ◽  
Author(s):  
Sean T. Hayes ◽  
Eli R. Hooten ◽  
Julie A. Adams

2013 ◽  
Author(s):  
Jaclyn Baron ◽  
Michael Hamilton ◽  
Jim Creager ◽  
John Ziriax

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1328
Author(s):  
Jorge Martin-Gutierrez ◽  
Marta Sylvia Del Rio Guerra

There has been a conscious shift towards developing increasingly inclusive applications. However, despite this fact, most research has focused on supporting those with visual or hearing impairments and less attention has been paid to cognitive impairments. The purpose of this study is to analyse touch gestures used for touchscreens and identify which gestures are suitable for individuals living with Down syndrome (DS) or other forms of physical or cognitive impairments. With this information, app developers can satisfy Design for All (DfA) requirements by selecting adequate gestures from existing lists of gesture sets. Twenty touch gestures were defined for this study and a sample group containing eighteen individuals with Down syndrome was used. A tool was developed to measure the performance of touch gestures and participants were asked to perform simple tasks that involved the repeated use of these twenty gestures. Three variables are analysed to establish whether they influence the success rates or completion times of gestures, as they could have a collateral effect on the skill with which gestures are performed. These variables are Gender, Type of Down syndrome, and Socioeconomic Status. Analysis reveals that significant difference is present when a pairwise comparison is performed, meaning individuals with DS cannot perform all gestures with the same ease. The variables Gender and Socioeconomic Status do not influence success rates or completion times, but Type of DS does.


Author(s):  
Yi-Hsiang Lo ◽  
Che-Chun Hsu ◽  
Hsin-Yin Chang ◽  
Wen-Yao Kung ◽  
Yen-Chun Lee ◽  
...  
Keyword(s):  

2021 ◽  
Vol 12 ◽  
Author(s):  
Mary L. Courage ◽  
Lynn M. Frizzell ◽  
Colin S. Walsh ◽  
Megan Smith

Although very young children have unprecedented access to touchscreen devices, there is limited research on how successfully they operate these devices for play and learning. For infants and toddlers, whose cognitive, fine motor, and executive functions are immature, several basic questions are significant: (1) Can they operate a tablet purposefully to achieve a goal? (2) Can they acquire operating skills and learn new information from commercially available apps? (3) Do individual differences in executive functioning predict success in using and learning from the apps? Accordingly, 31 2-year-olds (M = 30.82 month, SD = 2.70; 18 female) were compared with 29 3-year-olds (M = 40.92 month, SD = 4.82; 13 female) using two commercially available apps with different task and skill requirements: (1) a shape matching app performed across 3 days, and (2) a storybook app with performance compared to that on a matched paper storybook. Children also completed (3) the Minnesota Executive Functioning Scale. An adult provided minimal scaffolding throughout. The results showed: (1) toddlers could provide simple goal-directed touch gestures and the manual interactions needed to operate the tablet (2) after controlling for prior experience with shape matching, toddlers’ increased success and efficiency, made fewer errors, decreased completion times, and required less scaffolding across trials, (3) they recognized more story content from the e-book and were less distracted than from the paper book, (4) executive functioning contributed unique variance to the outcome measures on both apps, and (5) 3-year-olds outperformed 2-year-olds on all measures. The results are discussed in terms of the potential of interactive devices to support toddlers’ learning.


F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1651 ◽  
Author(s):  
Ajit Singh ◽  
Christopher J. Rawlings ◽  
Keywan Hassani-Pak

KnetMaps is a BioJS component for the interactive visualization of biological knowledge networks. It is well suited for applications that need to visualise complementary, connected and content-rich data in a single view in order to help users to traverse pathways linking entities of interest, for example to go from genotype to phenotype. KnetMaps loads data in JSON format, visualizes the structure and content of knowledge networks using lightweight JavaScript libraries, and supports interactive touch gestures. KnetMaps uses effective visualization techniques to prevent information overload and to allow researchers to progressively build their knowledge.


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