A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection

Asma Khatun; Sk. Golam Sarowar Hossain

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arXiv · 2003.05209

A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection

Asma Khatun·Sk. Golam Sarowar Hossain

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Abstract

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.

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BibTeX RIS

Asma Khatun, Sk. Golam Sarowar Hossain. 2020-03-11. A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection. https://arxiv.org/abs/2003.05209

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