Table 1. Initial features before feature selection. | ||

Features for IMU signals | ||

Basic descriptive statistics | Mean | Minimum |

Median | Maximum | |

Standard deviation (STD) | 25th percentile | |

Variance | 75th percentile | |

Median absolute deviation (MAD) | Skewness | |

Interquartile range (IQR) | Kurtosis | |

Signal metrics | Peak to peak | |

Root-mean-square level (RMS) | ||

Signal power | ||

1-lag autocorrelation | ||

Frequency domain properties | Dominant frequency | |

Dominant frequency magnitude | ||

Correlations | Correlation coefficients between axes |

Table 2. Features ranked highest by the ReliefF algorithm. | ||

Ranked features for each axis by the ReliefF algorithm (Rank order) | ||

Activity recognition | Stoniness classification | |

Accelerometer X | - | Minimum (10) Maximum (18) Peak to peak (20) Dominant frequency magnitude (21) Autocorrelation (22) |

Accelerometer Y | Kurtosis (4) Maximum (5) MAD (11) IQR (14) 75-percentile (24) | Max (2) Peak to peak (3) Autocorrelation (5) 75-percentile (12) STD (14) Minimum (16) Dominant frequency (23) Power (24) Variance (25) |

Accelerometer Z | Dominant frequency magnitude (7) Autocorrelation (12) Mean (15) 75-percentile (22) Median (25) | Autocorrelation (13) MAD (15) IQR (19) |

Gyroscope X | MAD (1) Dominant frequency magnitude (10) 75-percentile (21) | Autocorrelation (6) |

Gyroscope Y | - | Autocorrelation (8) |

Gyroscope Z | Minimum (3) 75-percentile (6) Median (8) 25-percentile (13) Mean (20) MAD (23) | - |

Correlation coeff. | Acc. X – Acc. Y (2) Acc. Y – Acc. Z (9) Gyro. Y – Gyro. Z (19) | Gyro. X – Gyro. Y (1) Acc. Z – Gyro. Y (4) Acc. X – Acc. Y (7) Acc. Y – Gyro. X (9) Acc. Z – Gyro. X (11) Acc. X – Gyro. Z (17) |

STD = standard deviation, MAD = Median absolute deviation, IQR = Interquartile range. |

Table 3. Selected features in activity recognition by the SFS algorithm. | ||||||

Selected features for each axis by the SFS algorithm (activity recognition) | ||||||

SVM | BDTree | KNN | NaïveBayes | LDA | QDA | |

Accelerometer X | RMS Peak to peak | Minimum | - | - | - | - |

Accelerometer Y | Variance | STD | Maximum STD Peak to peak | STD | Variance | Variance |

Accelerometer Z | - | - | - | - | - | - |

Gyroscope X | - | - | 25-percentile | Median | MAD | MAD |

Gyroscope Y | - | Peak to peak | - | - | - | - |

Gyroscope Z | - | - | - | Mean | Mean | Mean |

SVM = Support Vector Machine, BDTree = Binary Decision Tree, KNN = K-Nearest Neighbors, LDA = Linear Discriminant Analysis, QDA = Quadratic Discriminant Analysis. RMS = Root-mean-square level, STD = standard deviation, MAD = Median absolute deviation. |

Table 4. Selected features in stoniness classification by the SFS algorithm. | ||||||

Selected features for each axis by the SFS algorithm (stoniness classification) | ||||||

SVM | BDTree | KNN | NaïveBayes | LDA | QDA | |

Accelerometer X | Dom. freq. | - | - | - | RMS 75-percentile Dom. freq. mag. | RMS 75-percentile Dom. freq. mag. |

Accelerometer Y | Autocorrelation | - | - | Mean Skewness Autocorrelation | Median Maximum Variance Power Autocorrelation | Median Maximum Variance Power Autocorrelation |

Accelerometer Z | STD 75-percentile | - | - | Median 75-percentile | Mean | Mean |

Gyroscope X | Maximum | - | - | - | Maximum Peak to peak | Maximum Peak to peak |

Gyroscope Y | Maximum | - | - | MAD | MAD Kurtosis Peak to peak | MAD Kurtosis Peak to peak |

Gyroscope Z | Minimum | Dom. freq. | Dom. freq. | - | Minimum Autocorrelation | Minimum Autocorrelation |

Correlation coeff. | - | - | - | Acc. X – Acc. Y Acc. X – Gyro. Y | Acc. X – Gyro. Y | Acc. X – Gyro. Y |

SVM = Support Vector Machine, BDTree = Binary Decision Tree, KNN = K-Nearest Neighbors, LDA = Linear Discriminant Analysis, QDA = Quadratic Discriminant Analysis. RMS = Root-mean-square level, STD = standard deviation, MAD = Median absolute deviation. |

Table 5. The stoniness prediction accuracy. | |||||||

Stoniness prediction accuracy (%) | |||||||

SVM | BDTree | KNN | NaïveBayes | LDA | QDA | ANN | |

Point prediction (ReliefF) | 43.3 | 43.8 | 42.6 | 42.6 | 47.6 | 44.6 | 43.0 |

Point prediction (SFS) | 45.0 | 37.7 | 45.6 | 44.6 | 44.6 | 40.8 | 43.0 |

Grid prediction (ReliefF) | 66.1 | 69.6 | 37.5 | 64.3 | 35.7 | 57.1 | 62.5 |

Grid prediction (SFS) | 75.0 | 26.8 | 46.4 | 50.0 | 28.6 | 53.6 | 62.5 |

SVM = Support Vector Machine, BDTree = Binary Decision Tree, KNN = K-Nearest Neighbors, LDA = Linear Discriminant Analysis, QDA = Quadratic Discriminant Analysis, ANN = a neural network model. |