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Open AccessArticle

Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks

1
Department of Computing, Sheffield Hallam University, Sheffield S1 2NU, UK
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College of Computer Science & Information Technology, University of Al-Qadisiyah, Al Diwaniyah 58002, Iraq
3
Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 2NU, UK
4
Department of Computer Science, Liverpool John Moores University, Liverpool L3 3AF, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3803; https://doi.org/10.3390/s20133803
Received: 25 May 2020 / Revised: 19 June 2020 / Accepted: 3 July 2020 / Published: 7 July 2020
(This article belongs to the Special Issue Smart Mobile and Sensor Systems)
With the widespread use of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications, such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose augmentation of the time series signals from inertial sensors with signals from ambient sensing to train Deep Convolutional Neural Network (DCNNs) models. DCNNs provide the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertial and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors, such as environmental noise level and illumination, with an overall improvement of 5.3% accuracy. View Full-Text
Keywords: activity context sensing; smartphones; deep convolutional neural networks; smart devices activity context sensing; smartphones; deep convolutional neural networks; smart devices
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Otebolaku, A.; Enamamu, T.; Alfoudi, A.; Ikpehai, A.; Marchang, J.; Lee, G.M. Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks. Sensors 2020, 20, 3803.

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