Human Activity Recognition (HAR) using UCI dataset. Classifying the type of movement amongst six categories:
- WALKING,
- WALKING_UPSTAIRS,
- WALKING_DOWNSTAIRS,
- SITTING,
- STANDING,
- LAYING.
- The dataset can be downloaded from https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones
- dataset is collected from 30 persons(referred as subjects in this dataset), performing different activities with a smartphone to their waists. The data is recorded with the help of sensors (accelerometer and Gyroscope) in that smartphone. This experiment was video recorded to label the data manually.
- The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window).
- The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity.
- The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used
- Further details about the dataset can be found on this file
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