Activity recognition and monitoring using multiple sensors on different body positions

By Uwe Maurer, Asim Smailagic, D. Siewiorek, Michael E. Deisher. International Conference on Wearable and Implantable Body Sensor Networks, 2006

805 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/f86980de768169e0999cf62077dd8df14cbac5d9

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

An Activity Recognition System For Mobile Phones (2009, methodology): “could further enhance the recognition method, as suggested by the experiments in [ 17 ].”
Simple and Complex Activity Recognition through Smart Phones (2012, neutral): “[10] use accelerometers, temperature sensors and microphones for recognizing human locomotion.”
Transfer Learning for Activity Recognition: A Survey (2013, methodology): “, walking, running, sitting, climbing, and falling) [31,40].”
Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers (2008, methodology): “…range, mean absolute deviation, root mean square, standard deviation, and variance, which are usually extracted from the triaxial acceleration data (Maurer et al., 2006; Ravi et al., 2005; Wang et al., 2005) and explicated as follows: (1) mean: the mean value of the acceleration data over a window…”
Activity recognition using cell phone accelerometers (2011, methodology): “Additional studies have similarly focused on how one can use a variety of accelerometerbased devices to identify a range of user activities [4-7, 9-16, 21].”
A tutorial on human activity recognition using body-worn inertial sensors (2014, background): “2005] Light sensor (visible, IR, UV) B Localisation of windows, lamps, light tubes [Maurer et al. 2006; van Laerhoven and Cakmakci 2000] Skin temperature B Health state (e.g. fever) [Anliker et al. 2004] Galvanic skin response B Measure of skin conductivity to infer emotional states or levels of…”
A Survey on Activity Detection and Classification Using Wearable Sensors (2017, background): “Yet, while [58] and [85] chose the best locations, other systems were created that consist of using data from all of the multiple orientation-based sensors.”
Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations (2010, methodology): “To investigate the effect of the sensor position on the activity recognition performance, Maurer [15] deploys a multi-sensor device eWatch in locations like the left wrist, belt, necklace, right trousers pocket, shirt pocket and bag, and compares the recognition performance in those cases.”
Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “HAR using acceleration signal can generally achieve a high accuracy, and thus has been adopted for remote monitoring systems [269], [270] while taking care of privacy issues.”
Learning Setting-Generalized Activity Models for Smart Spaces (2010, methodology): “5 Other activities are not so easily distinguishable by body position.”
Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications (2013, background): “An innovative physical activity monitoring system is presented in [10].”
Multicolumn Bidirectional Long Short-Term Memory for Mobile Devices-Based Human Activity Recognition (2016, methodology): “Variance and standard deviation have also been used as feature extractors with some success [27].”