Enhancing accelerometer-based human activity recognition with relative barometric pressure signal

Name: Jantakarn Makma

Organization : Kasetsart University. Department of Electrical Engineering

Email : jantakarn.m@ku.th

Name: Dusit Thanapatay

Organization : Kasetsart University. Department of Electrical Engineering

Name: lsshiki, Tsuyoshi

Organization : Tokyo Institute of Technology

Name: Jatuporn Chinrungrueng

Organization : Thailand Thailand Science Park. National Science and Development Agency

Name: Surapa Thiemja

Organization : Thailand Thailand Science Park. National Science and Development Agency

keyword: Random forests

LCSH: Human activity recognition

LCSH: Accelerometers

LCSH: Atmospheric pressure -- Measurement

LCSH: Detectors

LCSH: Pattern recognition systems

Abstract: This paper proposes the use of relative barometric pressure sensors for enhancing the accuracy of human activity detection based on a tri-axial accelerometer. In our experiment, we employ a device consisting of a tri-axial accelerometer and a barometric pressure sensor on 12 subjects' waists while they are performing different sequences of activities including sitting, standing, walking, and lying. We also set another barometric pressure sensor on the wall as a reference for barometric pressure. We compare activity classification with features extracted from the following datasets: 1) acceleration data, 2) acceleration and on-body barometric pressure data, and 3) acceleration, on-body barometric pressure, and the reference barometric pressure data. Three classifiers are compared: k-nearest neighbors (KNN), decision tree, and random forest. The results show that the highest classification accuracy can be achieved when acceleration, on-body barometric pressure, and the reference barometric pressure information are used. This dataset provides the highest classification accuracy of 90.4% with the random forest algorithm.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2021

Modified: 2026-04-22

Issued: 2026-04-22

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BibliograpyCitation : In Chiang Mai University. Faculty of Engineering. 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2021) (pp.529-532). Chiang Mai : Chiang Mai University

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