Wearable sensor based human posture recognition

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Wearable_sensor_based_human_posture_recognition.pdf (335.83 KB)

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https://ieeexplore.ieee.org/document/7841004

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https://doi.org/10.1109/BigData.2016.7841004
 http://hdl.handle.net/11603/31596

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UMBC Information Systems Department
UMBC Center for Accelerated Real Time Analysis
UMBC Center for Real-time Distributed Sensing and Autonomy
UMBC Computer Science and Electrical Engineering Department
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Author/Creator ORCID

https://orcid.org/0000-0002-9933-1170

Date

2017-02-06

Type of Work

conference papers and proceedings
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Citation of Original Publication

J. Wang et al., "Wearable sensor based human posture recognition," 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 2016, pp. 3432-3438, doi: 10.1109/BigData.2016.7841004.

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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0 Universal

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UMBC Big Data Analytics Lab

Abstract

Human posture recognition has a wide range of applications including elderly care and video surveillance. This paper discusses how to recognize human postures using wearable devices. From real-world data, we analyze the challenges in terms of result performance, recognition efficiency and sensor selection. To deal with the challenges, we present our design with five techniques: i) oversampling and undersampling methods, ii) ensemble learning, iii) sensor selection, iv) stream data classification and v) post-processing techniques. We verify our design and show our findings through extensive experiments on real-world data, which shows our approach can achieve up to 91.5% overall weighted average accuracy for all three postures. We also discuss possible extensions of our work.