Self-Awareness in Remote Health Monitoring Systems using Wearable Electronics
Arman Anzanpour1,a, Iman Azimi1,b, Maximilian Götzinger1,c, Amir M. Rahmani2,3,g, Nima TaheriNejad2,e, Pasi Liljeberg1,d, Axel Jantsch2,f and Nikil Dutt3,h
1Department of Information Technology, University of Turku, Finland.
aarmanz@utu.fi
bimaazi@utu.fi
cmaxgot@utu.fi
dpakrli@utu.fi
2Institute of Computer Technology, TU Wien, Austria.
enima.taherinejad@tuwien.ac.at
faxel.jantsch@tuwien.ac.at
3Department of Computer Science, University of California Irvine, USA.
gamirr1@uci.edu
hdutt@uci.edu
ABSTRACT
In healthcare, effective monitoring of patients plays a key role in detecting health deterioration early enough. Many signs of deterioration exist as early as 24 hours prior having a serious impact on the health of a person. As hospitalization times have to be minimized, in-home or remote early warning systems can fill the gap by allowing in-home care while having the potentially problematic conditions and their signs under surveillance and control. This work presents a remote monitoring and diagnostic system that provides a holistic perspective of patients and their health conditions. We discuss how the concept of self-awareness can be used in various parts of the system such as information collection through wearable sensors, confidence assessment of the sensory data, the knowledge base of the patient's health situation, and automation of reasoning about the health situation. Our approach to self-awareness provides (i) situation awareness to consider the impact of variations such as sleeping, walking, running, and resting, (ii) system personalization by reflecting parameters such as age, body mass index, and gender, and (iii) the attention property of self-awareness to improve the energy efficiency and dependability of the system via adjusting the priorities of the sensory data collection. We evaluate the proposed method using a full system demonstration.
Keywords: Self-awareness, Health monitoring, Wearable electronics, Situation-awareness, Early warning score.
