Paper Info
Title
Human activity recognition using social media data
Abstract
Human activity recognition is a core component of context-aware, ubiquitous computing systems. Traditionally, this task is accomplished by analyzing signals of wearable motion sensors. While such signals can effectively distinguish various low-level activities (e.g. walking or standing), two issues exist: First, high-level activities (e.g. watching movies or attending lectures) are difficult to distinguish from motion data alone. Second, instrumentation of complex body sensor network at population scale is impractical. In this work, we take an alternative approach of leveraging rich, dynamic, and crowd-generated self-report data as the basis for in-situ activity recognition. By treating the user as the "sensor", we make use of implicit signals emitted from natural use of mobile smart-phones. Applying an L1-regularized Linear SVM on features derived from textual content, semantic location, and time, we are able to infer 10 meaningful classes of daily life activities with a mean accuracy of up to 83.9%. Our work illustrates a promising first step towards comprehensive, high-level activity recognition using free, crowd-generated, social media data.
Year
DOI
Venue
2013
10.1145/2541831.2541852
MUM
Keywords
Field
DocType
complex body sensor network,motion data,human activity recognition,in-situ activity recognition,crowd-generated self-report data,high-level activity,social media data,various low-level activity,high-level activity recognition,daily life activity,web mining,activity recognition
Population,Social media,Web mining,Activity recognition,Computer science,Wearable computer,Human–computer interaction,Motion sensors,Ubiquitous computing,Wireless sensor network
Conference
Citations 
PageRank 
References 
6
0.46
23
Authors
4
Name
Order
Citations
PageRank
Zack Zhu1675.52
Ulf Blanke269936.03
Alberto Calatroni337523.43
Gerhard Tröster42493250.70