192
F. Othmen et al.
Table 1. Performance comparison for different methods of raw data scenario.
Algorithm
SRC
FDDL
LRSDL
AC SE SP AC SE SP AC SE SP
Acc
99.8 100 99.6 98.0 98.0 98.0 97.4 97.9 96.9
Acc, Gyr
90.6 90.6 90.6 90.6 93.8 87.5 90.1 93.8 86.5
Acc, Gyr, Mag 97.4 96.9 97.9 96.4 96.9 95.8 97.4 96.9 97.9
Table 2. Performance comparison for different methods of feature extraction scenario.
Algorithm
SRC
FDDL
LRSDL
AC SE SP AC SE SP AC SE SP
VA, VV, VD 96.4 97.9 94.8 95.9 99.0 92.7 95.8 99.0 92.7
Euler
99.5 99.0 100 98.4 100 96.9 98.4 100 96.88
(+Euler)
96.9 96.9 96.9 95.8 96.9 94.8 96.4 95.8 96.9
1st Experiment. The SRC algorithm generates a dictionary D with the size
of the training samples, we set a D size of 300 atoms per class.
Table 1 and Table 2 exhibit respectively the performance of the tested SDL
algorithms under Scenario (A) and Scenario (B). In Table 1, an impressive performance is achieved by the SRC algorithm using a single triaxial accelerometer
raw data. Even though joining the gyroscope has significantly decreased efficiency, it has proved its convenience when fused with the magnetometer. Table 2
shows that the extracted spacial orientation angles present a better accuracy
compared with it when fused with a vertical movement component. Overall, the
SRC has reached the best accuracy of 99.8% compared to FDDL and LRSDL
when processed with a raw data accelerometer.
2nd Experiment. In order to inspect the best performance of both SDL algorithms, i.e FDDL and LRSDL, we vary the D size in the range of [50, . . . , 300]
atoms per class. As illustrated in Fig. 3, the change in SDL performance depends
roughly on the patterns of the input set. Consequently, the LRSDL has reached
the best accuracy of 99.5%, when processed with Euler angles an input data and
D presents a total of 400 atoms.
Table 3. Comparison of performance for related on-wrist fall detectors.
[34],
2014
[7],
2014
[14],
2016
[19],
2018
[11],
2018
[20],
2019
[32],
2019
[33],
2019
Our
work
Accuracy 93.75 NA
92.9
99.0
95.47 98.1
98.36 99.86 99.8
Sensitivity 83.33 95.0
80.95 100
83.33 98.1
95.1
99.93 100
Specificity 95.4
96.7
98.35 97.9
95.96 98.1
100
99.8
99.6
F. Othmen et al.
Table 1. Performance comparison for different methods of raw data scenario.
Algorithm
SRC
FDDL
LRSDL
AC SE SP AC SE SP AC SE SP
Acc
99.8 100 99.6 98.0 98.0 98.0 97.4 97.9 96.9
Acc, Gyr
90.6 90.6 90.6 90.6 93.8 87.5 90.1 93.8 86.5
Acc, Gyr, Mag 97.4 96.9 97.9 96.4 96.9 95.8 97.4 96.9 97.9
Table 2. Performance comparison for different methods of feature extraction scenario.
Algorithm
SRC
FDDL
LRSDL
AC SE SP AC SE SP AC SE SP
VA, VV, VD 96.4 97.9 94.8 95.9 99.0 92.7 95.8 99.0 92.7
Euler
99.5 99.0 100 98.4 100 96.9 98.4 100 96.88
(+Euler)
96.9 96.9 96.9 95.8 96.9 94.8 96.4 95.8 96.9
1st Experiment. The SRC algorithm generates a dictionary D with the size
of the training samples, we set a D size of 300 atoms per class.
Table 1 and Table 2 exhibit respectively the performance of the tested SDL
algorithms under Scenario (A) and Scenario (B). In Table 1, an impressive performance is achieved by the SRC algorithm using a single triaxial accelerometer
raw data. Even though joining the gyroscope has significantly decreased efficiency, it has proved its convenience when fused with the magnetometer. Table 2
shows that the extracted spacial orientation angles present a better accuracy
compared with it when fused with a vertical movement component. Overall, the
SRC has reached the best accuracy of 99.8% compared to FDDL and LRSDL
when processed with a raw data accelerometer.
2nd Experiment. In order to inspect the best performance of both SDL algorithms, i.e FDDL and LRSDL, we vary the D size in the range of [50, . . . , 300]
atoms per class. As illustrated in Fig. 3, the change in SDL performance depends
roughly on the patterns of the input set. Consequently, the LRSDL has reached
the best accuracy of 99.5%, when processed with Euler angles an input data and
D presents a total of 400 atoms.
Table 3. Comparison of performance for related on-wrist fall detectors.
[34],
2014
[7],
2014
[14],
2016
[19],
2018
[11],
2018
[20],
2019
[32],
2019
[33],
2019
Our
work
Accuracy 93.75 NA
92.9
99.0
95.47 98.1
98.36 99.86 99.8
Sensitivity 83.33 95.0
80.95 100
83.33 98.1
95.1
99.93 100
Specificity 95.4
96.7
98.35 97.9
95.96 98.1
100
99.8
99.6