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Table 3.3
Summary of existing offline HAR systems
HAR system
P
∗
Machine learning
Sensor
Activities
Computing
Acc. (%)
No. Type
Location
No. Type
algorithm
device
Bao and Intille (
2004
)
5
2D-acc
Thigh, ankle,
arm, wrist,
hip
20 ADL
20 NB, DT, k-NN
PC
84
Ravi et al. (
2005
)
1
3D-acc
Pelvic region 8
Locomotion,
ADL
2
SVM, NB, DT, k-NN,
bagging, boosting, stacking,
plurality voting
PDA, PC
73.3-99.8
Lukowicz et al. (
2004
)
5
3D-acc,
microphone
Arm, chest
8 Workshop
1
Intensity analysis (IA),
LDA, HMM, majority
voting: (IA+LDA)
Laptop, PC
95.5-100
Kwapisz et al. (
2011
)
1
3D-acc
Pocket
6
Locomotion
29 DT, LR, ANN
Smartphone, PC
91.7
Mannini and Sabatini (
2010
)
5
2D-acc
Thigh, ankle,
arm, wrist,
hip
7
Locomotion
13 NB, DT, k-NN, ANN,
GMM, cHMM
PC
92.2-98.5
Lee and Mase (
2002
)
2
2D-acc
1D-gyro
Wa i s t
5
Locomotion
8
Threshold-based
PC
92.9-95.9
Allen et al. (
2006
)
1
3D-acc
Wa i s t
8
Locomotion,
PTs
6 GMM, threshold-based
PC
71.1-91.3
Najafi et al. (
2003
)
2
1D-acc,
1D-gyro
Chest
6
Locomotion,
PTs
11 Threshold-based
PC
94.3
Salarian et al. (
2007
)
3
2D-acc,
1D-gyro
Trunk, shanks 6
Locomotion,
PTs
20 Threshold-based, fuzzy
logic
Data logger, PC
95.4
Rodríguez-Martín et al. (
2013
) 1
3D-acc
Wa i s t
9
Locomotion,
PTs
31 Threshold-based, SVM
Microcontroller, PC 90.5
Wu et al. (
2012
)
2
3D-acc,
3D-gyro
Arm, pockets 13 Locomotion
16
k-NN
Smartphone, PC
90.2
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