Abstract
Studies have shown that children can be exposed to smart devices at a very early age. This has important implications on research in children–computer interaction, children online safety and early education. Many systems have been built based on such research. In this work, we present multiple techniques to automatically detect the presence of a child on a smart device, which could be used as the first step on such systems. Our methods distinguish children from adults based on behavioral differences while operating a touch-enabled modern computing device. Behavioral differences are extracted from data recorded by the touchscreen and built-in sensors. To evaluate the effectiveness of the proposed methods, a new data set has been created from 50 children and adults who interacted with off-the-shelf applications on smart phones. Results show that it is possible to achieve 99% accuracy and less than 0.5% error rate after 8 consecutive touch gestures using only touch information or 5 s of sensor reading. If information is used from multiple sensors, then only after 3 gestures, similar performance could be achieved.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 334-348 |
| Number of pages | 15 |
| Journal | Computers and Security |
| Volume | 84 |
| DOIs | |
| State | Published - Jul 1 2019 |
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Keywords
- Adults
- Age group
- Behavior
- Children
- Mobile devices
- Online safety
- Parental control
ASJC Scopus subject areas
- Computer Science(all)
- Law
Cite this
Kid on the phone! Toward automatic detection of children on mobile devices. / Nguyen, Toan; Roy, Aditi; Memon, Nasir.
In: Computers and Security, Vol. 84, 01.07.2019, p. 334-348.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Kid on the phone! Toward automatic detection of children on mobile devices
AU - Nguyen, Toan
AU - Roy, Aditi
AU - Memon, Nasir
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Studies have shown that children can be exposed to smart devices at a very early age. This has important implications on research in children–computer interaction, children online safety and early education. Many systems have been built based on such research. In this work, we present multiple techniques to automatically detect the presence of a child on a smart device, which could be used as the first step on such systems. Our methods distinguish children from adults based on behavioral differences while operating a touch-enabled modern computing device. Behavioral differences are extracted from data recorded by the touchscreen and built-in sensors. To evaluate the effectiveness of the proposed methods, a new data set has been created from 50 children and adults who interacted with off-the-shelf applications on smart phones. Results show that it is possible to achieve 99% accuracy and less than 0.5% error rate after 8 consecutive touch gestures using only touch information or 5 s of sensor reading. If information is used from multiple sensors, then only after 3 gestures, similar performance could be achieved.
AB - Studies have shown that children can be exposed to smart devices at a very early age. This has important implications on research in children–computer interaction, children online safety and early education. Many systems have been built based on such research. In this work, we present multiple techniques to automatically detect the presence of a child on a smart device, which could be used as the first step on such systems. Our methods distinguish children from adults based on behavioral differences while operating a touch-enabled modern computing device. Behavioral differences are extracted from data recorded by the touchscreen and built-in sensors. To evaluate the effectiveness of the proposed methods, a new data set has been created from 50 children and adults who interacted with off-the-shelf applications on smart phones. Results show that it is possible to achieve 99% accuracy and less than 0.5% error rate after 8 consecutive touch gestures using only touch information or 5 s of sensor reading. If information is used from multiple sensors, then only after 3 gestures, similar performance could be achieved.
KW - Adults
KW - Age group
KW - Behavior
KW - Children
KW - Mobile devices
KW - Online safety
KW - Parental control
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UR - http://www.scopus.com/inward/citedby.url?scp=85064542057&partnerID=8YFLogxK
U2 - 10.1016/j.cose.2019.04.001
DO - 10.1016/j.cose.2019.04.001
M3 - Article
AN - SCOPUS:85064542057
VL - 84
SP - 334
EP - 348
JO - Computers and Security
JF - Computers and Security
SN - 0167-4048
ER -