· Chris Thompson · Hamilton Turner · Brian Dougherty · Douglas C. Schmidt



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distance threshold to keep the detection process active below the threshold speed. As long as the smartphone does not travel more than M

feet from the last location the speed threshold was exceeded, the detection algorithm assumes that the user is still inside the car. This extra condition allows the algorithm to detect accidents that occur when the user’s car is struck by another vehicle while stopped Using acceleration events to detect collisions
The accident detection model,
γ relies on sampling the accelerometer to detect collisions, as shown in Fig.
5
Given a stream of values from the accelerometer, denoted As, where each value As
i
is recorded at time
T
As
i
, As
now is the most current value, and T
now is the current instant in time:
φ =





As
now
i f As
now
φ
As
i
i f
(T
now
T
As
i
S
φ
)

(As
j
As, As
i
As
j
)
The value for
φ is set to the greatest acceleration event experienced in any direction over the time span S
φ
. If the current acceleration value is greater than
φ, then φ
is updated to the most recent acceleration value.
Car
Accelerometers record forces experienced in collision
Car
Car
Car
Fig. 5 Device sensors provide acceleration information

Mobile Netw Appl (2011) 16:285–303 3.4 Using acoustic events to detect accidents
Our prior work [
6
] on accident detection was based solely on acceleration. It was thus potentially susceptible to false positives at low speeds and thus required higher settings for M
φ
(higher values for M
φ
, reduce the probability that low speed collisions will be reported. In our accident detection model described in
Section
3.2
, we added acoustic data analysis to improve lower speed collision detection and reduce the probability of a false positive by listening for high decibel acoustic events, such as impact noise, car horns, and airbag deployment. For example, airbag deployment is accompanied by high-amplitude, short-duration noise that can exceed 170 dB at peak amplitude The WreckWatch formal model for accident detection uses builtin microphones on a smartphone to detect high-decibel acoustic events indicative of an accident. Using a secondary sensor in conjunction with acceleration attempts to lower the probability of false positives. As discussed in Section, clipping of the audio above 150 decibels and other potential noises
(such as shouting) make it hard to use sound alone to detect accidents. It is possible that this limitation could be overcome, but we chose to make acoustic events a secondary filter for accident detection that aids in reducing false positives.
The accident detection model
γ relies on sampling the microphone to detect accident noise. Given a stream of sound event decibel values denoted Ks,
where each value Ks
i
is recorded at T
Ks
i
, Ks
now is the most current value, and T
now is the current instant in time:
ρ =





1
i f Ks
now
M
ρ
1
i f
Ks
i
Ks, (Ks
i
M
ρ
)

(T
now
T
Ks
i
S
ρ
)
0
other
wise
(4)
During anytime span of S

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