Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.
%0 Journal Article
%1 ChenHoeyEtAl12tsmcc
%A Chen, Liming
%A Hoey, Jesse
%A Nugent, Chris D.
%A Cook, Diane J.
%A Yu, Zhiwen
%D 2012
%J IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
%K v1205 ieee paper embedded ai sensor data processing video image user learn action recognition zzz.sfit zzz.vitra
%N 6
%P 790-808
%R 10.1109/TSMCC.2012.2198883
%T Sensor-Based Activity Recognition
%V 42
%X Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.
@article{ChenHoeyEtAl12tsmcc,
abstract = {Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.},
added-at = {2014-03-20T16:32:09.000+0100},
author = {Chen, Liming and Hoey, Jesse and Nugent, Chris D. and Cook, Diane J. and Yu, Zhiwen},
biburl = {https://www.bibsonomy.org/bibtex/2d696694bf8f798f53427678d1807b177/flint63},
doi = {10.1109/TSMCC.2012.2198883},
file = {IEEE Digital Library:2012/ChenHoeyEtAl12tsmcc.pdf:PDF},
groups = {public},
interhash = {0c3a3deb1a6893dff23b81067577bcc6},
intrahash = {d696694bf8f798f53427678d1807b177},
issn = {1094-6977},
journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews},
keywords = {v1205 ieee paper embedded ai sensor data processing video image user learn action recognition zzz.sfit zzz.vitra},
month = {#nov#},
number = 6,
pages = {790-808},
timestamp = {2018-04-16T12:09:16.000+0200},
title = {Sensor-Based Activity Recognition},
username = {flint63},
volume = 42,
year = 2012
}