Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations

By Lin Sun, Daqing Zhang, Bin Li, Bin Guo, Shijian Li. International Conference on Ubiquitous Intelligence and Computing, 2010

296 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, General Health Professions, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/d4551e8bc6915fc9935230c236fa263c44170bfb

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In Computer Vision and Pattern Recognition

In General Health Professions

In Human-Computer Interaction

Citations in the corpus

On-body localization of wearable devices: An investigation of position-aware activity recognition (2016, background): “Activity Recognition is an active field of research in pervasive computing [1]–[4].”
: Privacy on Mobile Devices – It ’ s Complicated (2016, background): “, taps [3, 4], keypresses [5], position [6, 7, 9, 10], current activity [11]).”
Window Size Impact in Human Activity Recognition (2014, unknown): “0.666 Sun et al. (2010) (7 subjects) [54] Walking, running, stationary, upstairs, downstairs, driving, bicycling (7) Front/rear pockets on the trousers, front pockets on the coat (6 Reading, socializing, vacuuming and more (15) Chest, arm, wrist, waist, knee, ankle, right ear (7) kNN with k = 5 ( 56”
Fusion of Smartphone Motion Sensors for Physical Activity Recognition (2014, unknown): “This choice was motivated by the work done in [36] about orientation-independence in activity recognition, because the magnitude feature is less sensitive to orientation changes.”
A survey on wearable sensor modality centred human activity recognition in health care (2019, background): “Table 2 Sensor platforms in WSHAR Platform Case studies Strengths Weaknesses Picture Smartphones Sun et al., 2010 Guo et al., 2016 Hassan, et al., 2018 Ubiquitous, equipped with a variety of sensors, battery and memory Limited placing locations on body, arbitrary orientations in pockets, etc. Smart…”
Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition (2015, background): “, body motion approximations aimed at on-body sensor setups [28], gravity vector estimation techniques for smartphones [25], as well as the training of several classifiers each associated with an individual phone orientation and/or placement, often combined with algorithms for identifying this conte”
SoK: Privacy on Mobile Devices – It’s Complicated (2016, background): “, taps [3, 4], keypresses [5], position [6, 7, 9, 10], current activity [11]).”