Activity Recognition from Accelerometer Data on a Mobile Phone

By Tomas Brezmes, J. Gorricho, Josep Cotrina Navau. International Work-Conference on Artificial and Natural Neural Networks, 2009

346 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/48a785fec5180382dc2dd91ae4b176d742f0c58d

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In Biomedical Engineering

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

A robust human activity recognition system using smartphone sensors and deep learning (2018, methodology): “In [35], a smartphone was used to recognize six different activities in real-time.”
Scaling Human Activity Recognition via Deep Learning-based Domain Adaptation (2018, background): “, for inferring human activity and its correlation with behavioral health [11], [1], [12].”
Transition-Aware Human Activity Recognition Using Smartphones (2016, methodology): “A Nokia smartphone was used in [28] for the online recognition of 6 activities.”
Simple and Complex Activity Recognition through Smart Phones (2012, methodology): “[15] proposes a subject dependent real time activity recognition system again using the Nokia N95 smart phone.”
Accelerometer-based transportation mode detection on smartphones (2013, background): “A number of early systems used the embedded accelerometer for detecting different pedestrian and non-motorised modalities, such as walking and running [20, 12], ascending or descending stairs [4] or cycling [2].”
Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition (2015, background): “selected activity classes (or a subset of them) have been investigated prominently in a large number of HAR publications [10, 11, 14, 27, 29, 41, 47].”
A Public Domain Dataset for Human Activity Recognition using Smartphones (2013, methodology): “…approaches to exploit an Android smartphone for HAR employing its embedded triaxial accelerometers; additional results have also been presented in [7, 8] are still expected in topics such as in multi-sensor fusion for better HAR classiﬁcation, standardizing performance evaluation metrics [9], and…”
Activity recognition using cell phone accelerometers (2011, methodology): “The few studies that have involved commercial devices such as smart phones have focused either on a very small set of users [21] or have trained models for particular users [ 4 ] rather than creating a universal model that can be applied to any user.”
Activity recognition with smartphone sensors (2014, background): “Sensor amount Single sensor (Accelerometer) Single accelerometer [19-26] Multiple accelerometers [16]”