THE APPLICATION OF MACHINE LEARNING ON THE SENSORS OF SMARTPHONES TO DETECT FALLS IN REAL-TIME

Achraf Benba

achraf.benba@um5s.net.ma
Mohammed V University in Rabat, Ecole Nationale Supérieure d'Arts et Métiers, Electronic Systems Sensors and Nanobiotechnologies (Morocco)
https://orcid.org/0000-0001-7939-0790

Mouna Akki


Mohammed V University in Rabat, Ecole Nationale Supérieure d'Arts et Métiers, Electronic Systems Sensors and Nanobiotechnologies (Morocco)
http://orcid.org/0009-0001-8532-1158

Sara Sandabad


Hassan II University of Casablanca, Ecole Normale Supérieure de l'Enseignement Technique de Mohammadia, Electrical Engineering and Intelligent Systems (Morocco)
http://orcid.org/0000-0002-0813-6178

Abstract

With the increasing prevalence of smartphones, they now come equipped with a multitude of sensors such as GPS, microphones, cameras, magnetometers, accelerators, and more, which can simplify our daily lives. When it comes to healthcare, smartphones can become indispensable. The detection of geriatric falls is crucial as even the slightest injury can have fatal consequences. Therefore, we proposed the use of accelerometers in our research to detect falls in the elderly. Our project involved the development of an automated, continuous, and reliable monitoring system that would generate a list of elderly people at risk of falling and present it on a webpage for emergency services. This approach aimed to minimize the long-term impacts and save lives promptly. We started by developing a mobile application and used MATLAB to classify the falls as either "fall" or "not fall." Finally, we created a webpage that would facilitate communication between the mobile application and MATLAB.


Keywords:

fall detection, smartphone accelerometers, SVM, KNN, machine learning

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Published
2023-06-30

Cited by

Benba, A., Akki, M., & Sandabad, S. (2023). THE APPLICATION OF MACHINE LEARNING ON THE SENSORS OF SMARTPHONES TO DETECT FALLS IN REAL-TIME. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(2), 50–55. https://doi.org/10.35784/iapgos.3459

Authors

Achraf Benba 
achraf.benba@um5s.net.ma
Mohammed V University in Rabat, Ecole Nationale Supérieure d'Arts et Métiers, Electronic Systems Sensors and Nanobiotechnologies Morocco
https://orcid.org/0000-0001-7939-0790

Authors

Mouna Akki 

Mohammed V University in Rabat, Ecole Nationale Supérieure d'Arts et Métiers, Electronic Systems Sensors and Nanobiotechnologies Morocco
http://orcid.org/0009-0001-8532-1158

Authors

Sara Sandabad 

Hassan II University of Casablanca, Ecole Normale Supérieure de l'Enseignement Technique de Mohammadia, Electrical Engineering and Intelligent Systems Morocco
http://orcid.org/0000-0002-0813-6178

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