Yürür, Özgür, Liu, Chi Harold, Perera, Charith ![]() ![]() |
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Abstract
New-generation mobile devices will inevitably be employed within the realm of ubiquitous sensing. In particular, smartphones have been increasingly used for human activity recognition (HAR)-based studies. It is believed that recognizing human-centric activity patterns could accurately enough give a better understanding of human behaviors. Further, such an ability could have a chance to assist individuals to enhance the quality of their lives. However, the integration and realization of HAR-based mobile services stand as a significant challenge on resourceconstrained mobile-embedded platforms. In this manner, this paper proposes a novel discrete-time inhomogeneous hidden semi-Markov model (DT-IHS-MM)-based generic framework to address a better realization of HAR-based mobile context awareness. In addition, we utilize power-efficient sensor management strategies by providing three intuitive methods and constrained Markov decision process (CMDP), as well as partially observable Markov decision process (POMDP)-based optimal methods. Moreover, a feedback control mechanism is integrated to balance the tradeoff between accuracy in context inference and power consumption. In conclusion, the proposed sensor management methods achieve a 40% overall enhancement in the power consumption caused by the physical sensor with respect to the overall 85–90% accuracy ratio due to the provided adaptive context inference framework.
Item Type: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | Computer Science & Informatics |
Subjects: | Q Science > QA Mathematics > QA76 Computer software |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
ISSN: | 0018-9545 |
Date of First Compliant Deposit: | 25 September 2018 |
Date of Acceptance: | 17 October 2014 |
Last Modified: | 05 Jan 2024 18:35 |
URI: | https://orca.cardiff.ac.uk/id/eprint/114933 |
Citation Data
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