@inproceedings{SisLab2065,
       booktitle = {The 9th International Conference on Frontiers of Information Technology, Applications and Tools},
           month = {April},
           title = {Activity Recognition based on Clustering Methods for Senior Homecare Services},
          author = {Thi Hong Nhan Vu and Yang Koo Lee and Namsrai Oyun-Erdene},
            year = {2016},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2065/},
        abstract = {In modern society, most seniors prolong their
independence. To guarantee the safety of them when
living on their own, we need to monitor their activities
all the time and react to critical situations. The rapid
advances in wireless networks, wearable sensors, and
communications technologies pave the way for the
advent of homecare service systems. Activity
recognition is a crucial task in building such systems.
This paper investigates two clustering methods, kmeans
and Self-organizing map (SOM) for recognizing
human daily activities. An experiment is performed on
a real data set. The results show that k-means performs
pretty well in classifying two activities; however the
accuracy is pretty low when the data set is scaled up to
five activities. SOM outperforms k-means in most cases
of data sets. On average, the resulting accuracy of
SOM is 87\% and of k-means is 54\% for five activities.
As a result, SOM is most suitable to be integrated in
systems for providing remote homecare services.}
}