A Survey of Online Activity Recognition Using Mobile Phones

By Muhammad Shoaib, Stephan Bosch, Özlem Durmaz Incel, Hans Scholten, Paul J. M. Havinga. Italian National Conference on Sensors, 2015

501 citations recorded by the source corpus.

Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8b3a5c0778e881712bcb4905770365f3b80b7c85

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Citations in the corpus

Human Action Recognition From Various Data Modalities: A Review (2020, background): “work of [45], approaches based on mobile phones were reviewed.”
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, methodology): “The presence of noise is handled by preprocessing the raw data to eliminate this interference and prepare the data for being feed to the recognition models [35].”
Computer Vision and Image Understanding Computer Vision for Assistive Technologies (unknown): “In some studies, classifiers are combined in different ways, thereby creating multi-layer or hierarchical classification ( Shoaib et al., 2015 ), whereas other ones proposed relational graph of visual words for activity analysis for smart home security ( Zhang et al., 2015b ).”
Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors (2016, methodology): “We selected three classiﬁers, which are commonly used for practical activity recognition: Naive Bayes, k-nearest neighbor (KNN) and decision tree [35,36].”
Advanced internet of things for personalised healthcare systems: A survey (2017, background): “Magnetic field sensors [77] Location of higher spatial resolution”