It is well recognized that walking while using mobile phones will make people more susceptible at various risks. Existing studies to improve smartphone users' safety are mainly limited to detecting incoming vehicles. They are not able to address some more common and equally dangerous accidents such as trips, falling from stairs, platforms or falling into an open manhole. These hazards are generally caused by sudden change of ground. In this paper, we propose UltraSee, the first system that is able to detect sudden change of ground for pedestrian mobile phone users. UltraSee augments smartphones with a small ultrasonic sensor which can detect the abrupt change of distance ahead. UltraSee also leverages the context information of smartphone usage such as screen status and holding orientation to improve detection accuracy and reduce energy consumption as well as unnecessary alarms. We have carried out extensive experiments in different scenarios and by different users. The results show that UltraSee can achieve accident detection rate of 94% with false positive rate of 4.4% and reduce unnecessary alarms by 90%. In terms of energy consumption, UltraSee costs only about 20% energy compared to the existing works that only rely on smartphone cameras.
[1]
S. Gard,et al.
What Determines the Vertical Displacement of the Body During Normal Walking?
,
2001
.
[2]
Xing-Dong Yang,et al.
Surround-see: enabling peripheral vision on smartphones during active use
,
2013,
UIST.
[3]
David A. Landgrebe,et al.
A survey of decision tree classifier methodology
,
1991,
IEEE Trans. Syst. Man Cybern..
[4]
Antonio Corradi,et al.
WalkSafe: a pedestrian safety app for mobile phone users who walk and talk while crossing roads
,
2012,
HotMobile '12.
[5]
Jianqing Fan,et al.
Generalized likelihood ratio statistics and Wilks phenomenon
,
2001
.
[6]
Pourang Irani,et al.
CrashAlert: enhancing peripheral alertness for eyes-busy mobile interaction while walking
,
2013,
CHI.
[7]
S. Haykin,et al.
Adaptive Filter Theory
,
1986
.
[8]
Tan Yee Fan,et al.
A Tutorial on Support Vector Machine
,
2009
.
[9]
Marco Gruteser,et al.
On the limits of positioning-based pedestrian risk awareness
,
2014,
MARS '14.