research-article

Markov Dynamic Subsequence Ensemble for Energy-Efficient Activity Recognition

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Kotagiri RamamohanaraoAuthors Info & Claims

MobiQuitous 2017: Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Pages 282 - 291

https://doi.org/10.1145/3144457.3144470

Published: 07 November 2017 Publication History

Abstract

Ubiquitous mobile computing technology provides opportunities for accurate Activity Recognition (AR). Recently, ensemble models using multiple feature representations based on time series subsequences have demonstrated excellent performance on recognition accuracy. However, these models can significantly increase the energy overhead and shorten battery lifespans of the mobile devices. We formalize a dynamic subsequence selection problem that minimizes the computational cost while persevering a high recognition accuracy. To solve the problem, we propose Markov Dynamic Subsequence Ensemble (MDSE), an algorithm for the selection of the subsequences via a Markov Decision Process (MDP), where a policy is learned for choosing the best subsequence given the state of prediction. Regarding MDSE, we derive an upper bound of the expected ensemble size, so that the energy consumption caused by the computations of the proposed method is guaranteed. Extensive experiments are conducted on 6 real AR datasets to evaluate the effectiveness of MDSE. Compared to the state-of-the-art methods, MDSE reduces 70.8% computational cost which is 3.42 times more energy efficient, and achieves a comparably high accuracy.

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Cited By

Milot JChampagne Gareau JBeaudry É(2020)An Energy-Efficient Method with Dynamic GPS Sampling Rate for Transport Mode Detection and Trip ReconstructionAdvances in Artificial Intelligence10.1007/978-3-030-47358-7_42(408-419)Online publication date: 13-May-2020
https://dl.acm.org/doi/10.1007/978-3-030-47358-7_42

Index Terms

Markov Dynamic Subsequence Ensemble for Energy-Efficient Activity Recognition
1. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image ACM Other conferences

MobiQuitous 2017: Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

November 2017

555 pages

ISBN:9781450353687

DOI:10.1145/3144457

General Chairs:
Tao Gu
RMIT University, Australia
,
Ramamohanarao Kotagiri
University of Melbourne, Australia
,
Publications Chair:
Huai Liu
Victoria University, Australia

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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EAI: The European Alliance for Innovation
SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2017

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MobiQuitous 2017

MobiQuitous 2017: Computing, Networking and Services

November 7 - 10, 2017

VIC, Melbourne, Australia

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Overall Acceptance Rate 26 of 87 submissions, 30%

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Cited By

Milot JChampagne Gareau JBeaudry É(2020)An Energy-Efficient Method with Dynamic GPS Sampling Rate for Transport Mode Detection and Trip ReconstructionAdvances in Artificial Intelligence10.1007/978-3-030-47358-7_42(408-419)Online publication date: 13-May-2020
https://dl.acm.org/doi/10.1007/978-3-030-47358-7_42

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