Original language | English |
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Title of host publication | EWSN ’18 |
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Subtitle of host publication | Proceedings of the 2018 International Conference on Embedded Wireless Systems and Networks |
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Publisher or commissioning body | Association for Computing Machinery (ACM) |
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Pages | 163-186 |
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Number of pages | 4 |
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ISBN (Print) | 9780994988621 |
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Date | Accepted/In press - 1 Dec 2017 |
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Date | Published (current) - 16 Feb 2018 |
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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.