Information Technology Reference
In-Depth Information
Algorithm 3:
HAR Online Algorithm for the PTA-HAR system
Require:
a
: Triaxial linear acceleration
ω
: Triaxial angular velocity
g
: Gravity
H
1
(
·
)
: Noise reduction transfer function (Sect. 7.3.1)
H
2
(
·
)
: Body acceleration transfer function (Sect. 7.3.1)
ˆ(
·
)
: Feature extraction function (Sect. 7.3.1)
T
: Windows size
m
: Number of classes
p
: activity probability vector
p
=
[
p
1
,...,
p
m
]
T
m
×
m
P
: Buffer of probability vectors
P
∈ R
P
: Filtered buffer of probability vectors
z
: Buffer of discrete activity predictions
z
s
∈ R
(
·
)
: Probability filtering function (Sect. 7.3.3.1)
: Discrete filtering function (Sect. 7.3.3.2)
function
ProcessInertialSignals(
a
r
(
(
·
)
t
) ,
ω
r
(
t
)
)
a
˄
(
t
)
=
H
1
(
a
r
(
t
)) ,
//
Noise Filtering
ω
(
t
)
=
H
2
(
H
1
(
ω
r
(
t
)))
)
=
a
(
t
H
2
(
a
˄
(
t
))
//
Body acceleration Extraction
g
(
)
=
)
−
t
a
˄
(
t
a
(
t
)
//
Gravity extraction
return
a
(
t
) ,
g
(
t
) ,
ω
(
t
)
end
function
OnlinePrediction(
t
,
a
(
t
) ,
g
(
t
) ,
ω
(
t
) ,
B
,
z
)
=
a
t
:
t
]
,
t
∈
A
[
t
−
T
,...,
//
Window sampling
=
g
t
:
t
]
,
t
∈
G
[
t
−
T
,...,
=
ω
t
:
t
]
t
∈
[
t
−
T
,...,
x
=
ˆ (
A
,
G
,)
//
Feature Extraction and Normalization
∈
{
}
for
c
1
,...,
m
do
//
Multiclass SVM
)
=
w
c
T
x
f
c
(
x
+
b
c
//
FFP
/
1
e
(
c
f
c
(
x
)
+
c
)
p
i
=
1
+
//
Prob. Estimation (Sect. 7.3.2)
end
p
T
P
(
1
:
end
−
1
,
:
)
P
=
//
Append probability vector
P
=
(
P
)
//
Activity probability filtering
c
∗
=
P
(
s
−
1
,
c
)
argmax
c
∈
[1
,...,
m
]
//
MAP
c
∗
z
=
//
Append last activity prediction
z
(
1
:
end
−
1
)
c
ˆ
=
(
z
)
//
Discrete filtering and activity estimation
return
c
ˆ
end
Search WWH ::
Custom Search