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On-Board Feature Extraction from Acceleration Data for Activity Recognition

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationEWSN ’18
Subtitle of host publicationProceedings of the 2018 International Conference on Embedded Wireless Systems and Networks
Publisher or commissioning bodyAssociation for Computing Machinery (ACM)
Pages163-186
Number of pages4
ISBN (Print)9780994988621
DateAccepted/In press - 1 Dec 2017
DatePublished (current) - 16 Feb 2018

Abstract

Modern wearable devices are equipped with increasingly powerful microcontrollers and therefore are increasingly capable of doing computationally heavy operations, such as feature extraction from sensor data. This paper quantifies the time and energy costs required for on-board computation of features on acceleration data, the reduction achieved in subsequent communication load compared with transmission of the raw data, and the impact on daily activity recognition in terms of classification accuracy. The results show that platforms based on modern 32-bit ARM Cortex-M microcontrollers significantly benefit from on-board extraction of time-domain features. On the other hand, efficiency gains from computation of frequency domain features at the moment largely remain out of their reach.

    Structured keywords

  • Digital Health

    Research areas

  • Digital Health

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via ACM at https://dl.acm.org/citation.cfm?id=3234847.3234868 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 402 KB, PDF document

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