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Body location independent activity monitoring

: Figueira, Carina; Matias, Ricardo; Gamboa, Hugo


Gilbert, J. ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
BIOSTEC 2016, 9th International Joint Conference on Biomedical Engineering Systems and Technologies. Proceedings. Vol.4: BIOSIGNALS : Rome, Italy, February 21-23, 2016
SciTePress, 2016
ISBN: 978-989-758-170-0
International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) <9, 2016, Rome>
International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS) <9, 2016, Rome>
Fraunhofer AICOS ()
human activity recognition; signal processing; feature extraction; feature selection; machine learning

Human Activity Recognition (HAR) is increasingly common in people's daily lives, being applied in health areas, sports and safety. Because of their high computational power, small size and low cost, smartphones and wearable sensors are suitable to monitor user's daily living activities. However, almost all existing systems require devices to be worn in certain positions, making them impractical for long-term activity monitoring, where a change in position can lead to less accurate results. This work describes a novel algorithm to detect human activity independent of the sensor placement. Taking into account the battery consumption, only two sensors were considered: the accelerometer (ACC) and the barometer (BAR), with a sample frequency of 30 and 5 Hz, respectively. The signals obtained were then divided into 5 seconds windows. The dataset used is composed of 25 subjects, with more than 7 hours of recording. Daily living activities were performed with the smartphone worn in 12 different positions.