scholarly journals Evaluation of Full-Body Gestures Performed by Individuals with Down Syndrome: Proposal for Designing User Interfaces for All Based on Kinect Sensor

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3930
Author(s):  
Marta Sylvia Del Rio Guerra ◽  
Jorge Martin-Gutierrez

The ever-growing and widespread use of touch, face, full-body, and 3D mid-air gesture recognition sensors in domestic and industrial settings is serving to highlight whether interactive gestures are sufficiently inclusive, and whether or not they can be executed by all users. The purpose of this study was to analyze full-body gestures from the point of view of user experience using the Microsoft Kinect sensor, to identify which gestures are easy for individuals living with Down syndrome. With this information, app developers can satisfy Design for All (DfA) requirements by selecting suitable gestures from existing lists of gesture sets. A set of twenty full-body gestures were analyzed in this study; to do so, the research team developed an application to measure the success/failure rates and execution times of each gesture. The results show that the failure rate for gesture execution is greater than the success rate, and that there is no difference between male and female participants in terms of execution times or the successful execution of gestures. Through this study, we conclude that, in general, people living with Down syndrome are not able to perform certain full-body gestures correctly. This is a direct consequence of limitations resulting from characteristic physical and motor impairments. As a consequence, the Microsoft Kinect sensor cannot identify the gestures. It is important to remember this fact when developing gesture-based on Human Computer Interaction (HCI) applications that use the Kinect sensor as an input device when the apps are going to be used by people who have such disabilities.

2017 ◽  
Vol 26 (12) ◽  
pp. e382-e389 ◽  
Author(s):  
James D. Wilson ◽  
Jennifer Khan-Perez ◽  
Dominic Marley ◽  
Susan Buttress ◽  
Michael Walton ◽  
...  

2012 ◽  
Vol 19 (2) ◽  
pp. 4-10 ◽  
Author(s):  
Zhengyou Zhang

2020 ◽  
pp. 114179
Author(s):  
Lourdes Ramirez Cerna ◽  
Edwin Escobedo Cardenas ◽  
Dayse Garcia Miranda ◽  
David Menotti ◽  
Guillermo Camara-Chavez

Sensors ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 286 ◽  
Author(s):  
Ali Al-Naji ◽  
Kim Gibson ◽  
Sang-Heon Lee ◽  
Javaan Chahl

2013 ◽  
Vol 2 (4) ◽  
pp. 28-37 ◽  
Author(s):  
Maliheh Fakhar ◽  
Saeed Behzadipour ◽  
Amir Mobini

In this study, motion performance indices based on the kinematics of upper body have been presented and compared to be used in a home-based rehabilitation device. Microsoft Kinect sensor is used to extract and calculate such indices. A set of experiments has been designed and carried out in which, kinematic data of three patients has been recorded. Finally, the selected indices have been calculated, and the results were compared with those of a healthy subject.


2019 ◽  
Vol 277 ◽  
pp. 03005
Author(s):  
Abrar Alharbi ◽  
Fahad Alharbi ◽  
Eiji Kamioka

Human gait is a significant biometric feature used for the identification of people by their style of walking. Gait offers recognition from a distance at low resolution while requiring no user interaction. On the other hand, other biometrics are likely to require a certain level of interaction. In this paper, a human gait recognition method is presented to identify people who are wearing long baggy clothes like Thobe and Abaya. Microsoft Kinect sensor is used as a tool to establish a skeleton based gait database. The skeleton joint positions are obtained and used to create five different datasets. Each dataset contained different combination of joints to explore their effectiveness. An evaluation experiment was carried out with 20 walking subjects, each having 25 walking sequences in total. The results achieved good recognition rates up to 97%.


2020 ◽  
Vol 25 (2) ◽  
pp. 79-94
Author(s):  
Ditte Rudå ◽  
Gudmundur Einarsson ◽  
Jannik Boll Matthiassen ◽  
Christoph U. Correll ◽  
Karsten Gjessing Jensen ◽  
...  

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