000154603 001__ 154603
000154603 005__ 20190812205441.0
000154603 037__ $$aCONF
000154603 245__ $$aRobust activity recognition for assistive technologies: Benchmarking machine learning techniques
000154603 269__ $$a2010
000154603 260__ $$c2010
000154603 336__ $$aConference Papers
000154603 520__ $$aAn increasing need for healthcare provision and assistive technologies (AT) calls for the development of machine learning techniques able to cope with the variability inherent to real-world deployments. In the particular case of activity recognition applications sensor networks may be prone to changes at different levels ranging from sensor data variability to network reconfiguration. Robust methods are required to deal with those changes providing graceful degradation upon failure or self-configuration and adaptation capabilities that ensure their proper operation for long periods of time. Currently there is a lack of common tools and datasets that allow for replicable and fair comparison of different recognition approaches. We introduce a large database of human daily activities recorded in a sensor-rich environment. The database provides large amount of instances of the recorded activities using a significant number of sensors. In addition, we reviewed some of the techniques that have been proposed to cope with changes in the system, including missing data, sensor location/orientation change, as well as the possibility to exploit data from unknown discovered sensors. These techniques have been tested in the aforementioned datasets showing its suitability to emulate different sensor network configurations and recognition goals.
000154603 6531_ $$a[Opportunity]
000154603 700__ $$0241256$$g137762$$aChavarriaga, Ricardo
000154603 700__ $$0242182$$g191533$$aSagha, Hesam
000154603 700__ $$0242183$$g191359$$aBayati, Hamidreza
000154603 700__ $$0240030$$g149175$$aMillán, José del R.
000154603 700__ $$aRoggen, Daniel
000154603 700__ $$aFörster, Kilian
000154603 700__ $$aCalatroni, Alberto
000154603 700__ $$aTröster, Gerhard
000154603 700__ $$aLukowicz, Paul
000154603 700__ $$aBannach, David
000154603 700__ $$aKurz, Marc
000154603 700__ $$aHölzl, Gerold
000154603 700__ $$aFerscha, Alois
000154603 7112_ $$dDecember 10, 2010$$cWhistler, Canada$$aWorkshop on Machine Learning for Assistive Technologies at the Twenty-fourth Annual Conference on Neural Information Processing Systems (NIPS)
000154603 8564_ $$zURL$$uhttp://www.cs.uwaterloo.ca/~jhoey/mlat-nips2010/index.html
000154603 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/154603/files/ChavarriagaSaBaMiRoFoCaTr10.pdf$$s989153
000154603 909C0 $$xU12103$$pCNBI$$0252018
000154603 909C0 $$0252517$$xU12599$$pCNP
000154603 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.tind.io:154603
000154603 917Z8 $$x137762
000154603 917Z8 $$x137762
000154603 917Z8 $$x137762
000154603 937__ $$aEPFL-CONF-154603
000154603 973__ $$rNON-REVIEWED$$sPUBLISHED$$aEPFL
000154603 980__ $$aCONF