Sonia Soubam, Dipyaman Banerjee, et al.
ICDCN 2016
We analyze the ability of mobile phone-generated accelerometer data to detect high-level (i.e., at the semantic level) indoor lifestyle activities, such as cooking at home and working at the workplace, in practical settings. We design a 2-Tier activity extraction framework (called SAMMPLE) for our purpose. Using this, we evaluate discriminatory power of activity structures along the dimension of statistical features and after a transformation to a sequence of individual locomotive micro-activities (e.g. sitting or standing). Our findings from 152 days of real-life behavioral traces reveal that locomotive signatures achieve an average accuracy of 77.14%, an improvement of 16.37% over directly using statistical features. © 2012 IEEE.
Sonia Soubam, Dipyaman Banerjee, et al.
ICDCN 2016
Jay Black, Paul Castro, et al.
MDM 2005
Wanqing Tu, Cormac J. Sreenan, et al.
ICNP 2008
Suman Banerjee, Archan Misra
MobiHoc 2002