1 research outputs found
A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection
Elder people consequence a variety of problems while living Activities of
Daily Living (ADL) for the reason of age, sense, loneliness and cognitive
changes. These cause the risk to ADL which leads to several falls. Getting real
life fall data is a difficult process and are not available whereas simulated
falls become ubiquitous to evaluate the proposed methodologies. From the
literature review, it is investigated that most of the researchers used raw and
energy features (time domain features) of the signal data as those are most
discriminating. However, in real life situations fall signal may be noisy than
the current simulated data. Hence the result using raw feature may dramatically
changes when using in a real life scenario. This research is using frequency
domain Fourier coefficient features to differentiate various human activities
of daily life. The feature vector constructed using those Fast Fourier
Transform are robust to noise and rotation invariant. Two different supervised
classifiers kNN and SVM are used for evaluating the method. Two standard
publicly available datasets are used for benchmark analysis. In this research,
more discriminating results are obtained applying kNN classifier than the SVM
classifier. Various standard measure including Standard Accuracy (SA), Macro
Average Accuracy (MAA), Sensitivity (SE) and Specificity (SP) has been
accounted. In all cases, the proposed method outperforms energy features
whereas competitive results are shown with raw features. It is also noticed
that the proposed method performs better than the recently risen deep learning
approach in which data augmentation method were not used