A Novel Energy-Efficient Approach for Human Activity Recognition

Lingxiang Zheng; Dihong Wu; Xiaoyang Ruan; Shaolin Weng; Ao Peng; Biyu Tang; Hai Lu; Haibin Shi; Huiru Zheng

doi:10.3390/s17092064

A Novel Energy-Efficient Approach for Human Activity Recognition

Lingxiang Zheng, Dihong Wu, Xiaoyang Ruan, Shaolin Weng, Ao Peng, Biyu Tang, Hai Lu, Haibin Shi, Huiru Zheng

Research output: Contribution to journal › Article › peer-review

34 Citations (Scopus)

54 Downloads (Pure)

Original language	English
Pages (from-to)	2064
Journal	Sensors
Volume	17
Issue number	9
DOIs	https://doi.org/10.3390/s17092064
Publication status	Published (in print/issue) - 8 Sept 2017

Keywords

activity recognition
low power consumption
low sampling rate
energy-efficient classifier

Access to Document

10.3390/s17092064

Sensors-17-02064Final published version, 1.7 MB

http://uir.ulster.ac.uk/38807/1/Sensors_acceptance%20email.pdf

Cite this

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title = "A Novel Energy-Efficient Approach for Human Activity Recognition",

keywords = "activity recognition, low power consumption, low sampling rate, energy-efficient classifier",

author = "Lingxiang Zheng and Dihong Wu and Xiaoyang Ruan and Shaolin Weng and Ao Peng and Biyu Tang and Hai Lu and Haibin Shi and Huiru Zheng",

year = "2017",

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day = "8",

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T1 - A Novel Energy-Efficient Approach for Human Activity Recognition

AU - Zheng, Lingxiang

AU - Wu, Dihong

AU - Ruan, Xiaoyang

AU - Weng, Shaolin

AU - Peng, Ao

AU - Tang, Biyu

AU - Lu, Hai

AU - Shi, Haibin

AU - Zheng, Huiru

PY - 2017/9/8

Y1 - 2017/9/8

KW - activity recognition

KW - low power consumption

KW - low sampling rate

KW - energy-efficient classifier

U2 - 10.3390/s17092064

DO - 10.3390/s17092064

M3 - Article

C2 - 28885560

VL - 17

SP - 2064

JO - Sensors

JF - Sensors

IS - 9

ER -