| mean_x |
The mean of the x value over the accelerometer points in the segment. |
| mean_y |
The mean of the y value over the accelerometer points in the segment. |
| mean_z |
The mean of the z value over the accelerometer points in the segment. |
| std_x |
The standard deviation of the x value over the accelerometer points in the segment. |
| std_y |
The standard deviation of the y value over the accelerometer points in the segment. |
| std_z |
The standard deviation of the z value over the accelerometer points in the segment. |
| mean_pitch |
The mean value of the pitch over the accelerometer points in the segment. The pitch is defined as atan2(x, sqrt(y^2 + z^2)) in degrees. |
| std_pitch |
The standard deviation of the pitch over the accelerometer points in the segment. |
| mean_roll |
The mean value of the roll over the accelerometer points in the segment. The pitch is defined as atan2(y, sqrt(x^2 + z^2)) in degrees. |
| std_roll |
The standard deviation of the roll over the accelerometer points in the segment. |
| correlation_xy |
The Pearson’s correlation between the signal of x and the signal of y. |
| correlation_yz |
The Pearson’s correlation between the signal of y and the signal of z. |
| correlation_xz |
The Pearson’s correlation between the signal of x and the signal of z. |
| gps_speed |
The speed as measured by the GPS device. |
| meanabsder_x |
The mean of the absolute value of the derivative of x. Derivative is calculated by convolving the signal with at kernel of [-1,1]. |
| meanabsder_y |
The mean of the absolute value of the derivative of y. Derivative is calculated by convolving the signal with at kernel of [-1,1]. |
| meanabsder_z |
The mean of the absolute value of the derivative of z. Derivative is calculated by convolving the signal with at kernel of [-1,1]. |
| noise_x |
Measure of the noise in x signal. Noise is measured as by convolving the signal with a kernel of [-0.5, 1, -0.5]. |
| noise_y |
Measure of the noise in y signal. Noise is measured as by convolving the signal with a kernel of [-0.5, 1, -0.5]. |
| noise_z |
Measure of the noise in z signal. Noise is measured as by convolving the signal with a kernel of [-0.5, 1, -0.5]. |
| noise/absder_x |
Noise in signal of x divided by the mean of the absolute derivative of x. This is effectively the quotient between noise_x and meanabsder_x. |
| noise/absder_y |
Noise in signal of y divided by the mean of the absolute derivative of y. This is effectively the quotient between noise_y and meanabsder_y. |
| noise/absder_z |
Noise in signal of z divided by the mean of the absolute derivative of z. This is effectively the quotient between noise_z and meanabsder_z. |
| fundfreq_x |
The fundamental frequency of the x signal. It is defined as the frequency belonging to the highest peak in the frequency domain of the Fourier transformation of the signal. A Hamming window is used. The windowed signal is zero padded. The number of bins used can be configured. |
| fundfreq_y |
The fundamental frequency of the y signal. |
| fundfreq_z |
The fundamental frequency of the z signal. |
| odba |
Overall dynamic body acceleration. A measure that can be used as a proxy for for energy expenditure. |
| vedba |
Vector of dynamic body acceleration. A measure that can be used as a proxy for for energy expenditure. |
| fundfreqcorr_x |
Pearson correlation of signal x with a generated sine wave with equal mean, and the fundamental frequency of x as its frequency. The sine wave’s phase was shifted to maximize the correlation. |
| fundfreqcorr_y |
Pearson correlation of signal y with a generated sine wave with equal mean, and the fundamental frequency of y as its frequency. The sine wave’s phase was shifted to maximize the correlation. |
| fundfreqcorr_z |
Pearson correlation of signal z with a generated sine wave with equal mean, and the fundamental frequency of z as its frequency. The sine wave’s phase was shifted to maximize the correlation. |
| fundfreqmagnitude_x |
The magnitude of the highest peak in the frequency domain of the Fourier transformation of the x signal. |
| fundfreqmagnitude_y |
The magnitude of the highest peak in the frequency domain of the Fourier transformation of the y signal. |
| fundfreqmagnitude_z |
The magnitude of the highest peak in the frequency domain of the Fourier transformation of the z signal. |
| raw |
The raw input. The keyword raw will add all values of x, y and z to the features. This is rather a feature group than a single feature. |
| first_x |
The first (raw) value of the x signal. |
| first_y |
The first (raw) value of the y signal. |
| first_z |
The first (raw) value of the z signal. |
| measurement_classifier |
Each measurement classified individually by a specific classifier. This is again a feature group rather than a single feature. A classifier for this feature can be set in the configuration file. The features is a normalized histogram of measurements that were put in each class. To train a classifier for this role, use features first_x, first_y, first_z and gps_speed. |
| stepresponse |
The maximum response of the x signal (with its mean subtracted) to the convolution with kernel shaped as the smoothed average of several x signals of a Vulture stepping. The resulting kernel: [-0.0667, 0.1463, 0.3886, 0.4430, 0.3763, 0.3213, 0.2795, 0.2016, 0.0878, -0.0424, -0.1720, -0.2821, -0.3319, -0.2668] |