Journal
SENSORS
Volume 19, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/s19030621
Keywords
activity recognition; indoor localization; deep learning; smartphone
Funding
- National Key R&D Program of China [2016YFB0502204]
- National Natural Science Foundation of China [41701519]
- Natural Science Foundation of Guangdong Province [2017A030310544]
- Open Research Fund Program of State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing [16I02]
- Research Program of Shenzhen S&T Innovation Committee [JCYJ20170412105839839]
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In the indoor environment, the activity of the pedestrian can reflect some semantic information. These activities can be used as the landmarks for indoor localization. In this paper, we propose a pedestrian activities recognition method based on a convolutional neural network. A new convolutional neural network has been designed to learn the proper features automatically. Experiments show that the proposed method achieves approximately 98% accuracy in about 2 s in identifying nine types of activities, including still, walk, upstairs, up elevator, up escalator, down elevator, down escalator, downstairs and turning. Moreover, we have built a pedestrian activity database, which contains more than 6 GB of data of accelerometers, magnetometers, gyroscopes and barometers collected with various types of smartphones. We will make it public to contribute to academic research.
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