TY  - JOUR
PY  - 2016//
TI  - Real-time measures of context to improve fall-detection models
JO  - Conference proceedings - IEEE engineering in medicine and biology society
A1  - Jimison, Holly B.
A1  - Pavel, Misha
A1  - Jimison, Holly B.
A1  - Pavel, Misha
A1  - Jimison, Holly B.
A1  - Pavel, Misha
SP  - 574
EP  - 577
VL  - 2016
IS  - 
N2  - Real-time fall detection has been a challenging area of research and even more challenging as a viable commercial service, given the need for near perfect classification algorithms. True fall events are rare is monitored data sets, whereas confounding events for automated algorithms are quite frequent. In this paper we describe a decision theoretic approach to classification and alerting that incorporates context, such as location and activities, to improve probability and utility estimates for new classes, including near falls and known confounding events. We describe how to use monitored context to provide real-time assessment of true patient state to improve training data sets, as well as the use of context in improving classification, detection and alerting.<p />  <p>Language: en</p>
LA  - en
SN  - 1557-170X
UR  - http://dx.doi.org/10.1109/EMBC.2016.7590767
ID  - ref1
ER  -