Table 1 Summary of extracted features and their relevance to ambient environmental data segments

Measures of central tendency

Mean

Measures of central tendency describe the center or typical value of a dataset. The mean is the average value of the data which helps us capture the general level of the data and provide information about the baseline behavior.

Measures of dispersion

Standard deviation (SD)

Measures of dispersion describe how spread out the data are. The SD measures how much the data varies from the average value.

Minimum (Min) & Maximum (Max)

Min and Max values give the lower and upper range of the data which can be beneficial when certain ranges of values are indicative of specific conditions (e.g., pain severity/intensity)

Median deviation (MD)

The median deviation is a measure of how much the data varies from the middle value [26].

We extract median deviation (MD) related features which consist of mean-MD, max-MD, and min-MD so we can capture variability while being less influenced by extreme values (e.g., high light levels at night show high MD feature, but low Mean compared to daytime).

Measures of shape

Slope

Slope measures the shape (or pattern) of the data distribution by measuring how steeply a line fits to the data. This feature is important for capturing trends, identifying periods of growth or decline.

Mean-crossing-rate (MCR)

The MCR measures how often the data goes above and below its average value [27]. This feature is useful for characterizing oscillatory behavior and cyclical patterns in the data.