An Activity Recognition System For Mobile Phones

By Norbert Gyorbíró, Ákos Fábián, Gergely Hományi. Mob. Networks Appl., 2009

330 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/856f2264525a4358ec9c1f458d645a3b6eeb78b4

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In Computer Vision and Pattern Recognition

In Control and Systems Engineering

In Human-Computer Interaction

Citations in the corpus

A survey on privacy in mobile participatory sensing applications (2011, methodology): “Additionally, the research field of activity recognition also makes extensive use of accelerometer readings [18].”
Activity recognition with smartphone sensors (2014, methodology): “Other classification methods used in activity recognition with mobile sensors include Gaussian Mixture Models[48, 49], Artificial Neural Networks (ANN)[50-52], Naive Bayes (NB)[53], Rule-Based classifier[54], and a fuzzy inference system[55].”
Window Size Impact in Human Activity Recognition (2014, background): “Activities involving legs, the trunk and legs and the complete body ( i.e. , 2–8, 18, 26–33) could be part of the rehabilitation or stimulation exercises of the lower body.”
Activity Recognition on Streaming Sensor Data (2012, background): “More recently researchers are exploring smart phones equipped with accelerometers and gyroscopes to recognize ambulatory movements and gesture patterns [14][15].”
Activity recognition using cell phone accelerometers (2011, background): “These accelerometers are also capable of detecting the orientation of the device (helped by the fact that they can detect the direction of Earth s gravity), which can provide useful information for activity recognition.”
A Survey on Ambient Intelligence in Health Care (2013, neutral): “Smart phones are increasing in popularity for activity recognition [66], [67] because sensors in the phone collect all of the gyro, accelerometer, GPS, acoustic, and video data found the other methods, as long as they are on the individual while they perform activities.”
Elderly activities recognition and classification for applications in assisted living (2013, methodology): “The highest accuracy achieved by MLP was 81.52% while RBF only achieved 72.18%.”
Multicolumn Bidirectional Long Short-Term Memory for Mobile Devices-Based Human Activity Recognition (2016, methodology): “…in Section II, we present related works on image annotation; in Section III, we detail the newly proposed MARS; in Section IV, we present the experimental results of the new approach applied to the naturalistic mobile devices-based human activity (NMHA) dataset; and we conclude in Section V.”