@article{ref1,
title="Eigen posture based fall risk assessment system using Kinect",
journal="Conference proceedings - IEEE engineering in medicine and biology society",
year="2018",
author="Tripathy, Soumya Ranjan and Chakravarty, Kingshuk and Sinha, Aniruddha",
volume="2018",
number="",
pages="1-4",
abstract="Postural Instability (PI) is a major reason for fall in geriatric population as well as for people with diseases or disorders like Parkinson's, stroke etc. Conventional stability indicators like Berg Balance Scale (BBS) require clinical settings with skilled personnel's interventions to detect PI and finally classify the person into low, mid or high fall risk categories. Moreover these tests demand a number of functional tasks to be performed by the patient for proper assessment. In this paper a machine learning based approach is developed to determine fall risk with minimal human intervention using only Single Limb Stance exercise. The analysis is done based on the spatiotemporal dynamics of skeleton joint positions obtained from Kinect sensor. A novel posture modeling method has been applied for feature extraction along with some traditional time domain and metadata features to successfully predict the fall risk category. The proposed unobstrusive, affordable system is tested over 224 subjects and is able to achieve 75% mean accuracy on the geriatric and patient population.<p /> <p>Language: en</p>",
language="en",
issn="1557-170X",
doi="10.1109/EMBC.2018.8513263",
url="http://dx.doi.org/10.1109/EMBC.2018.8513263"
}