• International Journal of Technology (IJTech)
  • Vol 7, No 5 (2016)

A simple Hierarchical Activity Recognition System Using a Gravity Sensor and Accelerometer on a Smartphone

A simple Hierarchical Activity Recognition System Using a Gravity Sensor and Accelerometer on a Smartphone

Title: A simple Hierarchical Activity Recognition System Using a Gravity Sensor and Accelerometer on a Smartphone
Alvin Prayuda Juniarta Dwiyantoro, I Gde Dharma Nugraha, Deokjai Choi

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Cite this article as:

Dwiyantoro, A.P.J.,  Nugraha, I.G.D., Choi, D., 2016. A simple Hierarchical Activity Recognition System Using a Gravity Sensor and Accelerometer on a Smartphone. International Journal of Technology. Volume 7(5), pp.



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Alvin Prayuda Juniarta Dwiyantoro School of Electronics & Computer Engineering, Chonnam National University, Gwangju 61186, South Korea
I Gde Dharma Nugraha School of Electronics & Computer Engineering, Chonnam National University, Gwangju 61186, South Korea
Deokjai Choi School of Electronics & Computer Engineering, Chonnam National University, Gwangju 61186, South Korea
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Abstract
A simple Hierarchical Activity Recognition System Using a Gravity Sensor and Accelerometer on a Smartphone

The routine daily activities that tend to be sedentary and repetitive may cause severe health problems. This issue has encouraged researchers to design a system to detect and record people activities in real time and thus encourage them to do more physical exercise. By utilizing sensors embedded in a smartphone, many research studies have been conducted to try to recognize user activity. The most common sensors used for this purpose are accelerometers and gyroscopes; however, we found out that a gravity sensor has significant potential to be utilized as well. In this paper, we propose a novel method to recognize activities using the combination of an accelerometer and gravity sensor. We design a simple hierarchical system with the purpose of developing a more energy efficient application to be implemented in smartphones. We achieved an average of 95% for the activity recognition accuracy, and we also succeed at proving that our work is more energy efficient compared to other works.

Accelerometer; Activity recognition; Gravity sensor; Smartphone