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Energy and Accuracy Trade-Offs in Accelerometry-Based Activity Recognition

Energy and Accuracy Trade-Offs in Accelerometry-Based Activity Recognition
Energy and Accuracy Trade-Offs in Accelerometry-Based Activity Recognition
Driven by real-world applications such as fitness, wellbeing and healthcare, accelerometry-based activity recognition has been widely studied to provide context-awareness to future pervasive technologies. Accurate recognition and energy efficiency are key issues in enabling long-term and unobtrusive monitoring. While the majority of accelerometry-based activity recognition systems stream data to a central point for processing, some solutions process data locally on the sensor node to save energy. In this paper, we investigate the trade-offs between classification accuracy and energy efficiency by comparing on- and off-node schemes. An empirical energy model is presented and used to evaluate the energy efficiency of both systems, and a practical case study (monitoring the physical activities of office workers) is developed to evaluate the effect on classification accuracy. The results show a 40% energy saving can be obtained with a 13% reduction in classification accuracy, but this performance depends heavily on the wearer’s activity.
activity recognition, body sensor networks, classification accuracy, energy efficiency
978-1-4673-5775-3
Wang, Ning
12c074fb-be39-46a1-b3b1-670e6e57c16c
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
Maunder, Robert G.
76099323-7d58-4732-a98f-22a662ccba6c
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Wang, Ning
12c074fb-be39-46a1-b3b1-670e6e57c16c
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
Maunder, Robert G.
76099323-7d58-4732-a98f-22a662ccba6c
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc

Wang, Ning, Merrett, Geoff V., Maunder, Robert G. and Rogers, Alex (2013) Energy and Accuracy Trade-Offs in Accelerometry-Based Activity Recognition. 2013 22nd International Conference on Computer Communications and Networks (ICCCN), Nassau, Bahamas. 30 Jul - 02 Aug 2013. 6 pp . (doi:10.1109/ICCCN.2013.6614133).

Record type: Conference or Workshop Item (Paper)

Abstract

Driven by real-world applications such as fitness, wellbeing and healthcare, accelerometry-based activity recognition has been widely studied to provide context-awareness to future pervasive technologies. Accurate recognition and energy efficiency are key issues in enabling long-term and unobtrusive monitoring. While the majority of accelerometry-based activity recognition systems stream data to a central point for processing, some solutions process data locally on the sensor node to save energy. In this paper, we investigate the trade-offs between classification accuracy and energy efficiency by comparing on- and off-node schemes. An empirical energy model is presented and used to evaluate the energy efficiency of both systems, and a practical case study (monitoring the physical activities of office workers) is developed to evaluate the effect on classification accuracy. The results show a 40% energy saving can be obtained with a 13% reduction in classification accuracy, but this performance depends heavily on the wearer’s activity.

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More information

Accepted/In Press date: 29 April 2013
Published date: July 2013
Additional Information: (c) IEEE 2013
Venue - Dates: 2013 22nd International Conference on Computer Communications and Networks (ICCCN), Nassau, Bahamas, 2013-07-30 - 2013-08-02
Keywords: activity recognition, body sensor networks, classification accuracy, energy efficiency
Organisations: Agents, Interactions & Complexity, Electronic & Software Systems, Southampton Wireless Group

Identifiers

Local EPrints ID: 352018
URI: http://eprints.soton.ac.uk/id/eprint/352018
ISBN: 978-1-4673-5775-3
PURE UUID: 1ebccca7-78a2-429d-92a0-d5078a5c4c88
ORCID for Geoff V. Merrett: ORCID iD orcid.org/0000-0003-4980-3894
ORCID for Robert G. Maunder: ORCID iD orcid.org/0000-0002-7944-2615

Catalogue record

Date deposited: 29 Apr 2013 10:41
Last modified: 15 Mar 2024 03:30

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Contributors

Author: Ning Wang
Author: Geoff V. Merrett ORCID iD
Author: Robert G. Maunder ORCID iD
Author: Alex Rogers

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