Comparative Analysis of Machine Learning Algorithms for Classification of Environmental Sounds and Fall Detection

Farman Hassan; Muhammad Hamza Mehmood; Babar Younis; Nasir Mehmood; Talha Imran; Usama Zafar

Authors

Farman Hassan University of Engineering and Technology Taxila, Punjab Pakistan
Muhammad Hamza Mehmood University of Engineering and Technology Taxila, Punjab Pakistan
Babar Younis University of Engineering and Technology Taxila, Punjab Pakistan
Nasir Mehmood University of Engineering and Technology Taxila, Punjab Pakistan
Talha Imran University of Engineering and Technology Taxila, Punjab Pakistan
Usama Zafar University of Engineering and Technology Taxila, Punjab Pakistan

Keywords:

Decision tree, Fall incidents, Environmental Sounds, Machine Learning, Old houses.

Abstract

In recent years, number of elderly people in population has been increased because of the rapid advancements in the medical field, which make it necessary to take care of old people. Accidental fall incidents are life-threatening and can lead to the death of a person if first aid is not given to the injured person. Immediate response and medical assistance are necessary in case of accidental fall incidents to elderly people. The research community explored various fall detection systems to early detect fall incidents, however, still there exist numerous limitations of the systems such as using expensive sensors, wearable sensors that are hard to wear all the time, camera violates the privacy of person, and computational complexity. In order to address the above-mentioned limitations of the existing systems, we proposed a novel set of integrated features that consist of melcepstral coefficients, gammatone cepstral coefficients, and spectral skewness. We employed a decision tree for the classification performance of both binary problems and multi-class problems. We obtained an accuracy of 91.39%, precision of 96.19%, recall of 91.81%, and F1-score of 93.95%. Moreover, we compared our method with existing state-of-the-art methods and the results of our method are higher than other methods. Experimental results demonstrate that our method is reliable for use in medical centers, nursing houses, old houses, and health care provisions.

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