Authors:
Pedro Fernandes
1
;
Cesar Analide
1
and
Bruno Fernandes
1
;
2
Affiliations:
1
University of Minho, Largo do Paço, 4704-553, Braga, Portugal
;
2
PluggableAI, Braga, 4700-312, Portugal
Keyword(s):
Activity Recognition, Human Behavior, Machine Learning, Mobile Device, Sensor Data and Smartphones.
Abstract:
Activity recognition using smartphones has gained increased attention in recent years due to the widespread adoption of these devices and, consequently, their various sensors. These sensors are capable of providing very relevant data for this purpose. Non-intrusive sensors, in particular, offer the advantage of collecting data without requiring the user to perform any specific action or use any additional devices. The objective of this study was, therefore, the development of an application designed for activity recognition using exclusively non-intrusive sensors available in any smartphone. The data collected by these sensors underwent several processing stages, and after numerous iterations, a set of highly favorable features for training the machine learning models was obtained. The most prominent result was achieved by the model using the XGBoost algorithm, which achieved an impressive accuracy rate of 0.979. This quite robust result confirms the high effectiveness of using this
type of sensors for activity recognition.
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