%0 Conference Paper %1 lau2010supporting %A Lau, Sian Lun %A König, Immanuel %A David, Klaus %A Parandian, Baback %A Carius-Düssel, Christine %A Schultz, Martin %B 2010 7th International Symposium on Wireless Communication Systems %C York, England %D 2010 %I IEEE %K ComTec itegpub %P 810-814 %R 10.1109/ISWCS.2010.5624490 %T Supporting patient monitoring using activity recognition with a smartphone %U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5624490&tag=1 %X In our work we envision the use of context awareness to complement and improve telemedicine services. One concrete example is the recognition of physical activity (PA) for therapies and treatments. The usage of context can provide newer services by assisting medical professionals and patients to obtain implicit information. We introduce the Context Aware Remote Monitoring Assistant (CARMA), an application implemented on the MATRIX telemedicine middleware platform. It enables activity recognition for patients using non-obtrusive devices. In this paper, a movement recognition approach is investigated using a smartphone with a built-in accelerometer. Experiments were carried out to compare the influences of classification algorithms, features and combinations for feature extraction conditions on the recognition accuracy. The obtained results indicated that the approach is viable and shows much potential.

@inproceedings{lau2010supporting, abstract = {In our work we envision the use of context awareness to complement and improve telemedicine services. One concrete example is the recognition of physical activity (PA) for therapies and treatments. The usage of context can provide newer services by assisting medical professionals and patients to obtain implicit information. We introduce the Context Aware Remote Monitoring Assistant (CARMA), an application implemented on the MATRIX telemedicine middleware platform. It enables activity recognition for patients using non-obtrusive devices. In this paper, a movement recognition approach is investigated using a smartphone with a built-in accelerometer. Experiments were carried out to compare the influences of classification algorithms, features and combinations for feature extraction conditions on the recognition accuracy. The obtained results indicated that the approach is viable and shows much potential.}, added-at = {2016-04-16T12:26:34.000+0200}, address = {York, England}, author = {Lau, Sian Lun and König, Immanuel and David, Klaus and Parandian, Baback and Carius-Düssel, Christine and Schultz, Martin}, biburl = {https://www.bibsonomy.org/bibtex/21511fc5bfa8d39d0ba4fd75854b6a23c/comtec_pub}, booktitle = {2010 7th International Symposium on Wireless Communication Systems}, doi = {10.1109/ISWCS.2010.5624490}, interhash = {661643ee5ec02911c136b17456f84b09}, intrahash = {1511fc5bfa8d39d0ba4fd75854b6a23c}, issn = {2154-0217}, keywords = {ComTec itegpub}, month = {September}, pages = {810-814}, publisher = {IEEE}, timestamp = {2016-10-28T11:22:42.000+0200}, title = {Supporting patient monitoring using activity recognition with a smartphone}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5624490&tag=1}, year = 2010 }