Many research areas use accelerometers to study people’s activities including sleep, sedentary behaviour, and physical activity. Actigraph is one of the most common research grade accelerometers. Actigraph accelerometers include ActiLife is a closed-source software, which generates activity counts. There is considerable research validating and developing algorithms for human activity using Actilife counts. Unfortunately, Actilife counts are proprietary and difficult to implement if researchers use different accelerometer brands. Recently, Brond et al. developed a code in MATLAB, which can convert raw accelerometer data to Actilife counts. Their work can help researchers use different accelerometers and calculate Actilife counts, MATLAB is a commercial program. Unlike MATLAB, R is open-source, and also R is very popular among health and activity researchers. The package activityCounts allow users to convert the accelerometer data to Actilife counts.
You can install the released version of activityCounts from CRAN with:
You can install the development version from GitHub with:
Your dataset should contain at least three columns. Typically the first column is the raw accelerometer data for the x-axis and the second and the third columns are raw accelerometer data for the y and z-axes, respectively. There is sample dataset available with this package, which you can check the sample data format. To see the sample dataset run:
To calculate counts for your data, use the counts() function. Here is an example of using the counts() function. We use the included sampleXYZ dataset and then call the counts() function. The sampling frequency of our data is 100Hz, so we need to pass this value when calling the function counts:
The default value for hertz() function is 30. The user should be aware of the data sampling frequency and pass the correct value to hertz(). If the data frequency is less than 30 Hz, first resample your data and increase the frequency to more than 30 Hz and then calculate the counts.
activityCounts is flexible, and it can handle different data formats. However, to use the function, you need to provide x, y, and z raw data. The rest of the arguments are optional. The package assumes x, y, and z raw data are stored in the first, second, and the third columns, respectively. If the order is different use the x_axis, y_axis, and z_axis functions to indicated each column of your input has which one the axes. You should use the hertz argument to pass the sampling frequency. The default value for sampling frequency is 30 Hz. If your data contains a column of the time of the measurements, you can use time_column argument to indicate your desired column, otherwise, use the start_time argument to designate the starting time of your analysis.
If none of these methods are used to indicate the start time, the current time is considered as the start time.
In this example code, the first column has the time stamp for the data. The second column has the x-axis data, the third column has the y-axis data, and the fourth column has the z-axis data. Therefore, assuming the sampling frequency is 100 Hz, we call the function like this:
The default values for x_axis, y_axis, and z_axis are one, two, and three respectively. So if you don’t specify them, the function assumes the first column is for the x-axis, the second for the y-axis and the third is for the z-axis.
In the following example, starting time is given:
my_start_time <- "2017-08-22 12:30:10"
my_counts <- counts(data = sampleXYZ, hertz = 100, start_time = my_start_time)To verify the accuracy of the calculated counts for this particular dataset, you can compare them with the provided sampleCounts dataset. It contains counts calculated by ActiLife software and the counts() function.
To see the package help page run: