Context-Aware Mobile Patient Monitoring Framework Development: A Detailed Design

Al-Bashayreh, Mahmood Ghaleb; Hashim, Nor Laily; Khorma, Ola Taiseer

doi:10.1016/j.ieri.2013.11.023

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…The user activity is very important contextual information in health monitoring which can increase the accuracy of monitoring when it is combined with other pieces of information [9,17]. User activities are generally determined by performing machine learning algorithms (also known as Activity Recognition methods) over accelerometer data that can be obtained from wearable sensors or in-built sensors in smart phones.…”

Section: Related Workmentioning

confidence: 99%

“…Most of the current health monitoring systems that infer user activities [17][18][19][20][21] do not employ energy management techniques to preserve battery on mobile phones. To address this issue, we employ situation-aware adaptation strategies to improve costefficiency of machine learning algorithms, thereby extending lifetime of mobile application.…”

Section: Related Workmentioning

confidence: 99%

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Situation-Aware Mobile Health Monitoring

Haghighi¹,

Perera

Indrawan-Santiago³

et al. 2014

Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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Recent advances in mobile computing coupled with the widespread availability of inexpensive mobile devices are the key motivating factors for the development of mobile health monitoring systems. However, to leverage the full potential of such systems for continuous and real time monitoring, there are a number of challenges that need to be addressed. This paper proposes a situation-aware mobile health monitoring framework that aims to increase not only the accuracy in identifying the occurring health conditions but also the cost-efficiency of running algorithms (e.g. the activity recognition classifier) using a situation-aware adaptation technique. The proposed framework integrates high level knowledge (i.e. user activity) with low level sensory data (e.g. heart rate) in situation reasoning and data fusion. Such holistic situational information can significantly improve accuracy of clinical decision making and selfmanagement of chronic diseases. The implementation and evaluation of the framework for a health monitoring application is described. Categories and Subject Descriptors C.3 Special-purpose and Application-based systems-Real-time and embedded systems, and D.4.7 Organization and Design-Real-time systems and embedded systems.

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Section: Related Workmentioning

confidence: 99%