Indoor Activity Recognition by Using Recurrent Neural Networks

Yu Zhao; Qingjuan Li; Fadi Farha; Tao Zhu; Liming Chen; Huansheng Ning

doi:10.1007/978-981-15-1925-3_15

Indoor Activity Recognition by Using Recurrent Neural Networks

Yu Zhao
, Qingjuan Li
, Fadi Farha
, Tao Zhu
, Liming Chen
, Huansheng Ning

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

3 Citations (Scopus)

Abstract

Because of the development of the ageing population, most countries are facing an increasingly serious pension resources problem. With the development of Internet of Things, the integration of smart home and smart retirement provides a new solution for the new smart home for the elderly, to achieve the elderly to intelligently support the elderly. This paper is based on the development of this background, mainly to solve the problem of indoor activity recognition of the elderly, so as to prepare for the construction of smart medical care. The specific research process is to process the sensor data collected from the smart environment, identify different activities using RNN, LSTM and GRU models with strong ability to process time series data, realize the target of activity recognition.

Original language	English
Title of host publication	International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health
Subtitle of host publication	CyberDI 2019, CyberLife 2019: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health
Place of Publication	Singapore
Pages	205-215
Number of pages	10
ISBN (Electronic)	978-981-15-1925-3
DOIs	https://doi.org/10.1007/978-981-15-1925-3_15
Publication status	Published (in print/issue) - 6 Dec 2019

Publication series

Name	Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health
Volume	1138
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

activity recognition
Smart environment
Recurrent Neural Network

Access to Document

10.1007/978-981-15-1925-3_15

Cite this

Zhao, Y., Li, Q., Farha, F., Zhu, T., Chen, L., & Ning, H. (2019). Indoor Activity Recognition by Using Recurrent Neural Networks. In International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health: CyberDI 2019, CyberLife 2019: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health (pp. 205-215). Article Chapter 15 (Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health; Vol. 1138).. https://doi.org/10.1007/978-981-15-1925-3_15

Zhao, Yu ; Li, Qingjuan ; Farha, Fadi et al. / Indoor Activity Recognition by Using Recurrent Neural Networks. International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health: CyberDI 2019, CyberLife 2019: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health . Singapore, 2019. pp. 205-215 (Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health).

@inproceedings{1394232ddc6b4b63b8f58bf5d827b180,

title = "Indoor Activity Recognition by Using Recurrent Neural Networks",

abstract = "Because of the development of the ageing population, most countries are facing an increasingly serious pension resources problem. With the development of Internet of Things, the integration of smart home and smart retirement provides a new solution for the new smart home for the elderly, to achieve the elderly to intelligently support the elderly. This paper is based on the development of this background, mainly to solve the problem of indoor activity recognition of the elderly, so as to prepare for the construction of smart medical care. The specific research process is to process the sensor data collected from the smart environment, identify different activities using RNN, LSTM and GRU models with strong ability to process time series data, realize the target of activity recognition.",

keywords = "activity recognition, Smart environment, Recurrent Neural Network",

author = "Yu Zhao and Qingjuan Li and Fadi Farha and Tao Zhu and Liming Chen and Huansheng Ning",

year = "2019",

month = dec,

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doi = "10.1007/978-981-15-1925-3\_15",

language = "English",

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booktitle = "International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health",

}

Zhao, Y, Li, Q, Farha, F, Zhu, T, Chen, L & Ning, H 2019, Indoor Activity Recognition by Using Recurrent Neural Networks. in International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health: CyberDI 2019, CyberLife 2019: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health ., Chapter 15, Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health, vol. 1138, Singapore, pp. 205-215. https://doi.org/10.1007/978-981-15-1925-3_15

Indoor Activity Recognition by Using Recurrent Neural Networks. / Zhao, Yu; Li, Qingjuan; Farha, Fadi et al.
International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health: CyberDI 2019, CyberLife 2019: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health . Singapore, 2019. p. 205-215 Chapter 15 (Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health; Vol. 1138).

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

TY - GEN

T1 - Indoor Activity Recognition by Using Recurrent Neural Networks

AU - Zhao, Yu

AU - Li, Qingjuan

AU - Farha, Fadi

AU - Zhu, Tao

AU - Chen, Liming

AU - Ning, Huansheng

PY - 2019/12/6

Y1 - 2019/12/6

N2 - Because of the development of the ageing population, most countries are facing an increasingly serious pension resources problem. With the development of Internet of Things, the integration of smart home and smart retirement provides a new solution for the new smart home for the elderly, to achieve the elderly to intelligently support the elderly. This paper is based on the development of this background, mainly to solve the problem of indoor activity recognition of the elderly, so as to prepare for the construction of smart medical care. The specific research process is to process the sensor data collected from the smart environment, identify different activities using RNN, LSTM and GRU models with strong ability to process time series data, realize the target of activity recognition.

AB - Because of the development of the ageing population, most countries are facing an increasingly serious pension resources problem. With the development of Internet of Things, the integration of smart home and smart retirement provides a new solution for the new smart home for the elderly, to achieve the elderly to intelligently support the elderly. This paper is based on the development of this background, mainly to solve the problem of indoor activity recognition of the elderly, so as to prepare for the construction of smart medical care. The specific research process is to process the sensor data collected from the smart environment, identify different activities using RNN, LSTM and GRU models with strong ability to process time series data, realize the target of activity recognition.

KW - activity recognition

KW - Smart environment

KW - Recurrent Neural Network

UR - http://link.springer.com/10.1007/978-981-15-1925-3_15

U2 - 10.1007/978-981-15-1925-3_15

DO - 10.1007/978-981-15-1925-3_15

M3 - Conference contribution

SN - 978-981-15-1924-6

T3 - Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health

SP - 205

EP - 215

BT - International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health

CY - Singapore

ER -

Zhao Y, Li Q, Farha F, Zhu T, Chen L, Ning H. Indoor Activity Recognition by Using Recurrent Neural Networks. In International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health: CyberDI 2019, CyberLife 2019: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health . Singapore. 2019. p. 205-215. Chapter 15. (Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health). doi: 10.1007/978-981-15-1925-3_15

Indoor Activity Recognition by Using Recurrent Neural Networks

Abstract

Publication series

UN SDGs

Keywords

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