HumanActivityRecognition

Objective

To classify common human activities like walking,standing,laying on the basis of readings obtained from smartphone sensors

Dataset

Source: UCI ML Repository
Human Activity Recognition Using Smartphones Data Set
https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones#

Model

• Dataset has 561 attributes so Principal Component Analysis(PCA) is used to reduce the dimension.
• Best results are obtained by taking about 200 principal components.
• Linear SVM("one vs one") was used to classify the data

About Repository

• ActivityRecognition.py --- script to pickle data
• ActivityRecognition2.py --- classification script
• HAR pca.png -- image showing 2 principal components of the data
• TDT.png -- shows a plot of training, development and testing accuracies over number of principal components

Results

• Training accuracy ~ 99%
• Development or cross-validation accuracy ~ 98%
• Testing accuracy ~ 95-96%
• Most mis-classifications were obtained for standing and sitting classes as there is not quite of a difference between the 2 postures.

Future Scope

• Neural networks can be tried for the dataset