singhal373 / human-activity-recognition-ml Goto Github PK

Human Activity Recognition ( HAR ) has wide applications in medical research and human survey system. In this project, we design a robust activity recognition system based on a smartphone. The system uses a 3-dimentional smartphone accelerometer and gyroscope to collect time series signals, from which 561 features are generated in both time and frequency domain. The feature space is reduced to 200 using PCA.Activities are classified using 6 different methods, i.e., Random Forest, Gaussian Naive Bayes, Logistic Regression, Multi-layer Perceptron, Support Vector Machine, and Extra-trees. Experiment results show that the classification rate of SVM reaches 98.26 and is robust to common positions and poses of cellphone.

The dataset comprises of 3-axial linear acceleration and 3-axial angular velocity measured at a constant rate of 50Hz using the embedded accelerometer and gyroscope of an attached device. After noise reduction and signal processing on the raw inertial data, the time signals are sampled in a sliding window of 2.56 seconds, with a 50% overlap. Moreover, standard measures like mean, median, standard deviation etc. are also calculated. Source: UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets/Smartphone-Based+Recognition+of+Human+Activities+and+Postural+Transitions#)

Samples: 10929 ; Features: 561 ( Physical quantities like jerk, energy etc) ; Classes: 12 {Walking, Walking_upstairs, Walking_Downstairs, Sitting, Standing, Laying, Stand_to_Sit, Sit_to_Stand, Sit_to_Lie, Lie_to_Sit, Stand_to_Lie, Lie_to_Stand }

Gaussian Naive Bayes, Random Forest classifier, Extra-trees classifier, Multi-layer perceptron (with ReLU activation), Logistic Regression, C-Support Vector Classifier

The highest accuracy was obtained with the SVM ( with PCA ) : 98.26%

1. Navigate to the colab notebook and use the given data to run the code