TY  - JOUR
PY  - 2016//
TI  - Fall risk probability estimation based on supervised feature learning using public fall datasets
JO  - Conference proceedings - IEEE engineering in medicine and biology society
A1  - Koshmak, Gregory A.
A1  - Linden, Maria
A1  - Loutfi, Amy
A1  - Koshmak, Gregory A.
A1  - Linden, Maria
A1  - Loutfi, Amy
A1  - Koshmak, Gregory A.
A1  - Linden, Maria
SP  - 752
EP  - 755
VL  - 2016
IS  - 
N2  - Risk of falling is considered among major threats for elderly population and therefore started to play an important role in modern healthcare. With recent development of sensor technology, the number of studies dedicated to reliable fall detection system has increased drastically. However, there is still a lack of universal approach regarding the evaluation of developed algorithms. In the following study we make an attempt to find publicly available fall datasets and analyze similarities among them using supervised learning. After preforming similarity assessment based on multidimensional scaling we indicate the most representative feature vector corresponding to each specific dataset. This vector obtained from a real-life data is subsequently deployed to estimate fall risk probabilities for a statistical fall detection model. Finally, we conclude with some observations regarding the similarity assessment results and provide suggestions towards an efficient approach for evaluation of fall detection studies.<p />  <p>Language: en</p>
LA  - en
SN  - 1557-170X
UR  - http://dx.doi.org/10.1109/EMBC.2016.7590811
ID  - ref1
ER  -