TY  - JOUR
PY  - 2019//
TI  - Agitation detection in people living with dementia using multimodal sensors
JO  - Conference proceedings - IEEE engineering in medicine and biology society
A1  - Khan, Shehroz S.
A1  - Spasojevic, Sofija
A1  - Nogas, Jacob
A1  - Ye, Bing
A1  - Mihailidis, Alex
A1  - Iaboni, Andrea
A1  - Wang, Angel
A1  - Martin, Lori Schindel
A1  - Newman, Kristine
SP  - 3588
EP  - 3591
VL  - 2019
IS  - 
N2  - People Living with Dementia (PLwD) often exhibit behavioral and psychological symptoms of dementia; with agitation being one of the most prevalent symptoms. Agitated behaviour in PLwD indicates distress and confusion and increases the risk to injury to both the patients and the caregivers. In this paper, we present the use of wearable devices to detect agitation in PLwD. We hypothesize that combining multi-modal sensor data can help in building better classifiers to identify agitation in PLwD in comparison to a single sensor. We present a unique study to collect motion and physiological data from PLwD. This multi-modal sensor data is subsequently used to build predictive models to detect agitation in PLwD. The results on Random Forest for 28 days of data from PLwD show a strong evidence to support our hypothesis and highlight the importance of using multi-modal sensor data for detecting agitation events amongst them.<p />  <p>Language: en</p>
LA  - en
SN  - 1557-170X
UR  - http://dx.doi.org/10.1109/EMBC.2019.8857781
ID  - ref1
ER  -