Human activity recognition with smart watch based on H-SVM

Tao Tang; Lingxiang Zheng; Shaolin Weng; Ao Peng; Huiru Zheng

doi:10.1007/978-981-10-3187-8_19

Human activity recognition with smart watch based on H-SVM

Tao Tang, Lingxiang Zheng, Shaolin Weng, Ao Peng, Huiru Zheng

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

Activity recognition allows ubiquitous wearable device like smart watch to simplify the study and experiment. It is very convenient and extensibility that we do study with the accelerometer sensor of a smart watch. In this paper, we use Samsung GEAR smart watch to collect data, then extract features, classify with H-SVM (Hierarchical Support Vector Machine) classifier and identify human activities classification. Experiment results show great effect at low sampling rate, such as 10 and 5 Hz, which will give us the energy saving. In most cases, the accuracies of activity recognition experiment are above 99%.

Original language	English
Title of host publication	Frontier Computing - Theory, Technologies and Applications, FC 2016
Publisher	Springer
Pages	179-186
Number of pages	8
Volume	422
ISBN (Print)	9789811031861
DOIs	https://doi.org/10.1007/978-981-10-3187-8_19
Publication status	Published online - 27 Sept 2017
Event	5th International Conference on Frontier Computing, FC 2016 - Tokyo, Japan Duration: 13 Jul 2016 → 15 Jul 2016

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	422
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	5th International Conference on Frontier Computing, FC 2016
Country/Territory	Japan
City	Tokyo
Period	13/07/16 → 15/07/16

Funding

Acknowledgements This work was supported by the Major Science and Technology special project of Fujian Province (No. 2012HZ0003-2).

Keywords

H-SVM
Human activity recognition
Smart watch

Access to Document

10.1007/978-981-10-3187-8_19

Cite this

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title = "Human activity recognition with smart watch based on H-SVM",

abstract = "Activity recognition allows ubiquitous wearable device like smart watch to simplify the study and experiment. It is very convenient and extensibility that we do study with the accelerometer sensor of a smart watch. In this paper, we use Samsung GEAR smart watch to collect data, then extract features, classify with H-SVM (Hierarchical Support Vector Machine) classifier and identify human activities classification. Experiment results show great effect at low sampling rate, such as 10 and 5 Hz, which will give us the energy saving. In most cases, the accuracies of activity recognition experiment are above 99\%.",

keywords = "H-SVM, Human activity recognition, Smart watch",

author = "Tao Tang and Lingxiang Zheng and Shaolin Weng and Ao Peng and Huiru Zheng",

year = "2017",

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language = "English",

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note = " 5th International Conference on Frontier Computing, FC 2016 ; Conference date: 13-07-2016 Through 15-07-2016",

}

Tang, T, Zheng, L, Weng, S, Peng, A & Zheng, H 2017, Human activity recognition with smart watch based on H-SVM. in Frontier Computing - Theory, Technologies and Applications, FC 2016. vol. 422, Lecture Notes in Electrical Engineering, vol. 422, Springer, pp. 179-186, 5th International Conference on Frontier Computing, FC 2016, Tokyo, Japan, 13/07/16. https://doi.org/10.1007/978-981-10-3187-8_19

Human activity recognition with smart watch based on H-SVM. / Tang, Tao; Zheng, Lingxiang; Weng, Shaolin et al.
Frontier Computing - Theory, Technologies and Applications, FC 2016. Vol. 422 Springer, 2017. p. 179-186 (Lecture Notes in Electrical Engineering; Vol. 422).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Human activity recognition with smart watch based on H-SVM

AU - Tang, Tao

AU - Zheng, Lingxiang

AU - Weng, Shaolin

AU - Peng, Ao

AU - Zheng, Huiru

PY - 2017/9/27

Y1 - 2017/9/27

N2 - Activity recognition allows ubiquitous wearable device like smart watch to simplify the study and experiment. It is very convenient and extensibility that we do study with the accelerometer sensor of a smart watch. In this paper, we use Samsung GEAR smart watch to collect data, then extract features, classify with H-SVM (Hierarchical Support Vector Machine) classifier and identify human activities classification. Experiment results show great effect at low sampling rate, such as 10 and 5 Hz, which will give us the energy saving. In most cases, the accuracies of activity recognition experiment are above 99%.

AB - Activity recognition allows ubiquitous wearable device like smart watch to simplify the study and experiment. It is very convenient and extensibility that we do study with the accelerometer sensor of a smart watch. In this paper, we use Samsung GEAR smart watch to collect data, then extract features, classify with H-SVM (Hierarchical Support Vector Machine) classifier and identify human activities classification. Experiment results show great effect at low sampling rate, such as 10 and 5 Hz, which will give us the energy saving. In most cases, the accuracies of activity recognition experiment are above 99%.

KW - H-SVM

KW - Human activity recognition

KW - Smart watch

UR - https://www.scopus.com/pages/publications/85031412123

U2 - 10.1007/978-981-10-3187-8_19

DO - 10.1007/978-981-10-3187-8_19

M3 - Conference contribution

AN - SCOPUS:85031412123

SN - 9789811031861

VL - 422

T3 - Lecture Notes in Electrical Engineering

SP - 179

EP - 186

BT - Frontier Computing - Theory, Technologies and Applications, FC 2016

PB - Springer

T2 - 5th International Conference on Frontier Computing, FC 2016

Y2 - 13 July 2016 through 15 July 2016

ER -

Human activity recognition with smart watch based on H-SVM

Abstract

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Keywords

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