Table 1.
State of the art of human motor activity classification systems.
| Reference | Sensors | Features | Classifiers | Activity | Subjects | Accuracy [%] |
|---|---|---|---|---|---|---|
| [38] | 1 tri-axis accelerometer (3D acc) | Raw data Delta coefficients DC component |
GMM | 8 | 6 | 91.3 |
| [39] | 1 bi-axis accelerometer (2D acc) | Wavelet coefficients | k-NN | 5 | 6 | 86.6 |
| [40] | 1 3D acc | Standard deviation Energy distribution DC component Correlation coefficients |
Naive Bayesian k-NN SVM Binary decision |
8 | NA | 46.3–99.3 |
| [32] | 5 2D acc | Standard deviation Energy distribution DC component Entropy Correlation coefficients |
Naive Bayesian k-NN Binary decision |
20 | 20 | 84 |
| [34] | 2 3D acc | Wavelet coefficients | ANN | 4 | 6 | 83–90 |
| [41] | 1 2D acc | RMS velocity | ANN | 6 | 10 | 95 |
| [33] | 1 2D acc Ambient sensors |
Standard deviation FFT coefficients Derivative |
ANN Markov chains |
7 | NA | 42–96 |
| [42] | 1 3D acc | Wavelet coefficients Fractal dimension |
Threshold-based | 3 | 23 | p < 0.01 |
| [43] | 1 3D acc | Wavelet coefficients | Threshold-based | 3 | 20 | 98.8 |
| [44] | 1 2D acc 1 gyro | Wavelet coefficients | Threshold-based | 5 | 44 | > 90 |
| [35] | 1 3D acc | FFT | Threshold-based | 9 | 12 | 95.1 |
| [45] | 1 2D acc 1 gyro 1 compass |
Raw data Standard deviation Derivative |
Threshold-based | 5 | 8 | 92.9–95.9 |
| [23] | 2 uni-axis acc (1D acc) | Median Absolute deviation |
Threshold-based | 4 | 5 | 89.3 |
| [19] | 4 1D acc Heart and breath rate |
FFT | Template matching | 9 | 24 | 95.8 |
| [30] | 3 1D acc | DC component Standard deviation Signal morphology |
Threshold-based Template matching |
6 | 10 | 80–97.5 |
| [46] | 5 1D acc 1 2D acc |
Angular signal Motility FFT |
Binary decision | 23 | NA | 81–93 |
| [47] | 1 3D acc | Magnitude area/vector Tilt angle FFT |
Binary decision | 10 | 6 | 90.8 |