Children’s touchscreen stroke gestures are poorly recognized by existing recognition algorithms, especially compared to adults’ gestures. It seems clear that improved recognition is necessary, but how much is realistic? Human recognition rates may be a good starting point, but no prior work exists establishing an empirical threshold for a target accuracy in recognizing children’s gestures based on human recognition. To this end, we present a crowdsourcing study in which naïve adult viewers recruited via Amazon Mechanical Turk were asked to classify gestures produced by 5- to 10-year-old children. We found a significant difference between human (90.60%) and machine (84.14%) recognition accuracy, over all ages. We also found significant differences between human and machine recognition of gestures of different types: humans perform much better than machines do on letters and numbers versus symbols and shapes. We provide an empirical measure of the accuracy that future machine recognition should aim for, as well as a guide for which categories of gestures have the most room for improvement in automated recognition. Our findings will inform future work on recognition of children’s gestures and improving applications for children.

As well as: Alex Shaw and Lisa Anthony

Alex Shaw, Jaime Ruiz, and Lisa Anthony. 2017. Comparing human and machine recognition of children’s touchscreen stroke gestures. In Proceedings of the 19th ACM International Conference on Multimodal Interaction (ICMI 2017). ACM, New York, NY, USA, 32-40. DOI: https://doi.org/10.1145/3136755.3136810

@inproceedings{Shaw:2017:CHM:3136755.3136810,
 author = {Shaw, Alex and Ruiz, Jaime and Anthony, Lisa},
 title = {Comparing Human and Machine Recognition of Children's Touchscreen Stroke Gestures},
 booktitle = {Proceedings of the 19th ACM International Conference on Multimodal Interaction},
 series = {ICMI 2017},
 year = {2017},
 isbn = {978-1-4503-5543-8},
 location = {Glasgow, UK},
 pages = {32--40},
 numpages = {9},
 url = {http://doi.acm.org/10.1145/3136755.3136810},
 doi = {10.1145/3136755.3136810},
 acmid = {3136810},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Gesture recognition, children, crowdsourcing, touchscreen},
}