Machine Assisted Video Tagging of Elderly Activities in K-Log Centre

Chanwoong Lee; Hyorim Choi; Shapna Muralidharan; Heedong Ko; Byounghyun Yoo; Gerard Jounghyun Kim

doi:10.1109/MFI49285.2020.9235269

Machine Assisted Video Tagging of Elderly Activities in K-Log Centre

Chanwoong Lee, Hyorim Choi, Shapna Muralidharan, Heedong Ko, Byounghyun Yoo, Gerard Jounghyun Kim

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

3 Citations (Scopus)

Abstract

In a rapidly aging society, like in South Korea, the number of Alzheimer's Disease (AD) patients is a significant public health problem, and the need for specialized healthcare centers is in high demand. Healthcare providers generally rely on caregivers (CG) for elderly persons with AD to monitor and help them in their daily activities. K-Log Centre is a healthcare provider located in Korea to help AD patients meet their daily needs with assistance from CG in the center. The CG'S in the K-Log Centre need to attend the patients' unique demands and everyday essentials for long-term care. Moreover, the CG also describes and logs the day-to-day activities in Activities of Daily Living (ADL) log, which comprises various events in detail. The CG's logging activities can overburden their work, leading to appalling results like suffering quality of elderly care and hiring additional CG's to maintain the quality of care and a negative feedback cycle. In this paper, we have analyzed this impending issue in K-Log Centre and propose a method to facilitate machine-assisted human tagging of videos for logging of the elderly activities using Human Activity Recognition (HAR). To enable the scenario, we use a You Only Look Once (YOLO-v3)-based deep learning method for object detection and use it for HAR creating a multi-modal machine-assisted human tagging of videos. The proposed algorithm detects the HAR with a precision of 98.4%. After designing the HAR model, we have tested it in a live video feed from the K-Log Centre to test the proposed method. The model showed an accuracy of 81.4% in live data, reducing the logging activities of the CG's.

Original language	English
Title of host publication	2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	237-242
Number of pages	6
ISBN (Electronic)	9781728164229
DOIs	https://doi.org/10.1109/MFI49285.2020.9235269
Publication status	Published - 2020 Sept 14
Event	2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020 - Karlsruhe, Germany Duration: 2020 Sept 14 → 2020 Sept 16

Publication series

Name	IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Volume	2020-September

Conference

Conference	2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020
Country/Territory	Germany
City	Karlsruhe
Period	20/9/14 → 20/9/16

Bibliographical note

Funding Information:
This work was supported by the Korea Institute of Science and Technology (KIST) under the Institutional Program (Grant No. 2E30270). REFERENCES

Publisher Copyright:
© 2020 IEEE.

Keywords

Activities of Daily Living (ADL)
Human Activity Recognition (HAR)
K-Log Center
Machine-assisted human tagging
Multi-modal
You Only Look Once (YOLO-v3)

ASJC Scopus subject areas

Control and Systems Engineering
Software
Computer Science Applications

UN SDGs

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

Access to Document

10.1109/MFI49285.2020.9235269

Cite this

Lee, C., Choi, H., Muralidharan, S., Ko, H., Yoo, B., & Kim, G. J. (2020). Machine Assisted Video Tagging of Elderly Activities in K-Log Centre. In 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020 (pp. 237-242). Article 9235269 (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems; Vol. 2020-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MFI49285.2020.9235269

Machine Assisted Video Tagging of Elderly Activities in K-Log Centre. / Lee, Chanwoong; Choi, Hyorim; Muralidharan, Shapna et al.
2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 237-242 9235269 (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems; Vol. 2020-September).

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

Lee, C, Choi, H, Muralidharan, S, Ko, H, Yoo, B & Kim, GJ 2020, Machine Assisted Video Tagging of Elderly Activities in K-Log Centre. in 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020., 9235269, IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, vol. 2020-September, Institute of Electrical and Electronics Engineers Inc., pp. 237-242, 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020, Karlsruhe, Germany, 20/9/14. https://doi.org/10.1109/MFI49285.2020.9235269

Lee C, Choi H, Muralidharan S, Ko H, Yoo B, Kim GJ. Machine Assisted Video Tagging of Elderly Activities in K-Log Centre. In 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 237-242. 9235269. (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems). doi: 10.1109/MFI49285.2020.9235269

Lee, Chanwoong ; Choi, Hyorim ; Muralidharan, Shapna et al. / Machine Assisted Video Tagging of Elderly Activities in K-Log Centre. 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 237-242 (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems).

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abstract = "In a rapidly aging society, like in South Korea, the number of Alzheimer's Disease (AD) patients is a significant public health problem, and the need for specialized healthcare centers is in high demand. Healthcare providers generally rely on caregivers (CG) for elderly persons with AD to monitor and help them in their daily activities. K-Log Centre is a healthcare provider located in Korea to help AD patients meet their daily needs with assistance from CG in the center. The CG'S in the K-Log Centre need to attend the patients' unique demands and everyday essentials for long-term care. Moreover, the CG also describes and logs the day-to-day activities in Activities of Daily Living (ADL) log, which comprises various events in detail. The CG's logging activities can overburden their work, leading to appalling results like suffering quality of elderly care and hiring additional CG's to maintain the quality of care and a negative feedback cycle. In this paper, we have analyzed this impending issue in K-Log Centre and propose a method to facilitate machine-assisted human tagging of videos for logging of the elderly activities using Human Activity Recognition (HAR). To enable the scenario, we use a You Only Look Once (YOLO-v3)-based deep learning method for object detection and use it for HAR creating a multi-modal machine-assisted human tagging of videos. The proposed algorithm detects the HAR with a precision of 98.4%. After designing the HAR model, we have tested it in a live video feed from the K-Log Centre to test the proposed method. The model showed an accuracy of 81.4% in live data, reducing the logging activities of the CG's.",

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Machine Assisted Video Tagging of Elderly Activities in K-Log Centre

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this