Toward physical activity diary: motion recognition using simple acceleration features with mobile phones

By Jun Yang. IMCE '09, 2009

373 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Accelerometer-based transportation mode detection on smartphones (2013, positive): “The lag of the approach can be reduced by shortening the time window over which gravity is estimated, as has been used in several sensing systems [19, 35].”
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 ].”
Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations (2010, methodology): “To compare with Yang’s solution, we estimate the gravity acceleration by averaging the signal in each axis during a sampling interval of 10 seconds as proposed in Yang’s work and use the result to compute the vertical and horizontal accelerations.”
Simple and Complex Activity Recognition through Smart Phones (2012, methodology): “Yang [14] developed an activity recognition system using the Nokia N95 cell phone for distinguishing between different locomotion.”
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].”