Partition the dataset by assigning 75% of the signals for training and 25% for testing. These objects allow performant computation of multiple features in the time and frequency domains with one function call.įeatures1 features2 features3 features4 features5 features6 features7 features8 features9 features10 features11 features12 features13 features14 features15 features16 features17 features18 features19 features20 features21 features22 ActivityID We extract different features from the filtered and unfiltered signals using the signalTimeFeatureExtractor and signalFrequencyFeatureExtractor objects. To isolate the rapid signal variations from the slower ones, we apply a high pass filter to the original signals. The other consisting of 'slow' variations over time caused by the position of the body with respect to the vertical gravitational field. The accelerometer signals may be thought of as containing two main components, one consisting of 'fast' variations over time caused by body dynamics (physical movements of the subject).

Atx - Buffered x-direction accelerometer sensor data of fixed length (44 by 7776 matrix)Īctid - Response vector containing the activity IDs in integers: 1, 2, 3, and 4 representing Sitting, Standing, Walking and Running, respectivelyĪctnames - List of activity names for each activity IDįs - Sample rate of accelerometer sensor data

Human Activity Detection Matlab Code