Using motion capture to produce learning software to aid teachers of sign language

2014 ◽  
Vol 3 (1) ◽  
pp. 62-94
Author(s):  
Adam Kale
Keyword(s):  
Sign Language ◽  
Motion Capture ◽  
Learning Software

Current and future methodologies for quantitative analysis of information transfer in sign language and gesture data

2017 ◽  
Vol 40 ◽  
Author(s):  
Evie Malaia
Keyword(s):  
Quantitative Analysis ◽  
Data Collection ◽  
Sign Language ◽  
Motion Capture ◽  
Information Transfer ◽  
State Of The Art ◽  
Video Data ◽  
Noninvasive Methods ◽  
Video Recordings ◽  
Biological Differentiation

AbstractState-of-the-art methods of analysis of video data now include motion capture and optical flow from video recordings. These techniques allow for biological differentiation between visual communication and noncommunicative motion, enabling further inquiry into neural bases of communication. The requirements for additional noninvasive methods of data collection and automatic analysis of natural gesture and sign language are discussed.

scholarly journals How Fast Is Sign Language? A Reevaluation of the Kinematic Bandwidth Using Motion Capture

2021 ◽  
Author(s):  
Felix Bigand ◽  
Elise Prigent ◽  
Bastien Berret ◽  
Annelies Braffort
Keyword(s):  
Sign Language ◽  
Motion Capture

scholarly journals A Combined Semantic and Motion Capture Database for Real-Time Sign Language Synthesis

2009 ◽  
pp. 432-438 ◽  
Author(s):  
Charly Awad ◽  
Nicolas Courty ◽  
Kyle Duarte ◽  
Thibaut Le Naour ◽  
Sylvie Gibet
Keyword(s):  
Real Time ◽  
Sign Language ◽  
Motion Capture ◽  
Sign Language Synthesis

scholarly journals Do all aspects of learning benefit from iconicity? Evidence from motion capture

2019 ◽  
Vol 12 (1) ◽  
pp. 36-55
Author(s):  
ASHA SATO ◽  
MARIEKE SCHOUWSTRA ◽  
MOLLY FLAHERTY ◽  
SIMON KIRBY
Keyword(s):  
Language Learning ◽  
Sign Language ◽  
Motion Capture ◽  
Dynamic Time Warping ◽  
Negative Impact ◽  
Conclusive Evidence ◽  
Microsoft Kinect ◽  
Time Warping ◽  
3D Camera ◽  
Dynamic Time

abstractRecent work suggests that not all aspects of learning benefit from an iconicity advantage (Ortega, 2017). We present the results of an artificial sign language learning experiment testing the hypothesis that iconicity may help learners to learn mappings between forms and meanings, whilst having a negative impact on learning specific features of the form. We used a 3D camera (Microsoft Kinect) to capture participants’ gestures and quantify the accuracy with which they reproduce the target gestures in two conditions. In the iconic condition, participants were shown an artificial sign language consisting of congruent gesture–meaning pairs. In the arbitrary condition, the language consisted of non-congruent gesture–meaning pairs. We quantified the accuracy of participants’ gestures using dynamic time warping (Celebi et. al., 2013). Our results show that participants in the iconic condition learn mappings more successfully than participants in the arbitrary condition, but there is no difference in the accuracy with which participants reproduce the forms. While our work confirms that iconicity helps to establish form–meaning mappings, our study did not give conclusive evidence about the effect of iconicity on production; we suggest that iconicity may only have an impact on learning forms when these are complex.

Sign in / Sign up

Export Citation Format

Share Document