TY  - JOUR
PY  - 2015//
TI  - A fall prediction methodology for elderly based on a depth camera
JO  - Conference proceedings - IEEE engineering in medicine and biology society
A1  - Alazrai, Rami
A1  - Mowafi, Yaser
A1  - Hamad, Eyad
SP  - 4990
EP  - 4993
VL  - 2015
IS  - 
N2  - With the aging of society population, efficient tracking of elderly activities of daily living (ADLs) has gained interest. Advancements of assisting computing and sensor technologies have made it possible to support elderly people to perform real-time acquisition and monitoring for emergency and medical care. In an earlier study, we proposed an anatomical-plane-based human activity representation for elderly fall detection, namely, motion-pose geometric descriptor (MPGD). In this paper, we present a prediction framework that utilizes the MPGD to construct an accumulated histograms-based representation of an ongoing human activity. The accumulated histograms of MPGDs are then used to train a set of support-vector-machine classifiers with a probabilistic output to predict fall in an ongoing human activity. Evaluation results of the proposed framework, using real case scenarios, demonstrate the efficacy of the framework in providing a feasible approach towards accurately predicting elderly falls.<p />  <p>Language: en</p>
LA  - en
SN  - 1557-170X
UR  - http://dx.doi.org/10.1109/EMBC.2015.7319512
ID  - ref1
ER  -