Machine Learning Based Human Activity Detection in a Privacy-Aware Compliance Tracking System

Qing Wu; Wenbing Zhao

doi:10.1109/EIT.2018.8500131

Machine Learning Based Human Activity Detection in a Privacy-Aware Compliance Tracking System

Qing Wu
, Wenbing Zhao

Electrical and Computer Engineering

Cleveland State University

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

4 Scopus citations

Abstract

In this paper, we report our work on using machine learning techniques to predict back bending activity based on field data acquired in a local nursing home. The data are recorded by a privacy-aware compliance tracking system (PACTS). The objective of PACTS is to detect back-bending activities and issue real-time alerts to the participant when she bends her back excessively, which we hope could help the participant form good habits of using proper body mechanics when performing lifting/pulling tasks. We show that our algorithms can differentiate nursing staffs baseline and high-level bending activities by using human skeleton data without any expert rules.

Original language	English
Title of host publication	IEEE International Conference on Electro Information Technology
Place of Publication	usa
Publisher	IEEE Computer [email protected]
Pages	673-676
Number of pages	4
Volume	2018-May
ISBN (Electronic)	9781538653982
DOIs	https://doi.org/10.1109/EIT.2018.8500131
State	Published - Oct 18 2018
Event	2018 IEEE International Conference on Electro/Information Technology, EIT 2018 - Rochester, United States Duration: May 3 2018 → May 5 2018

Conference

Conference	2018 IEEE International Conference on Electro/Information Technology, EIT 2018
Country/Territory	United States
City	Rochester
Period	05/3/18 → 05/5/18

Keywords

Human Activity Prediction
Machine Learning
Microsoft Kinect
Neural Network
Privacy-Aware Compliance Tracking System

Access to Document

10.1109/EIT.2018.8500131

Cite this

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title = "Machine Learning Based Human Activity Detection in a Privacy-Aware Compliance Tracking System",

abstract = "In this paper, we report our work on using machine learning techniques to predict back bending activity based on field data acquired in a local nursing home. The data are recorded by a privacy-aware compliance tracking system (PACTS). The objective of PACTS is to detect back-bending activities and issue real-time alerts to the participant when she bends her back excessively, which we hope could help the participant form good habits of using proper body mechanics when performing lifting/pulling tasks. We show that our algorithms can differentiate nursing staffs baseline and high-level bending activities by using human skeleton data without any expert rules.",

keywords = "Human Activity Prediction, Machine Learning, Microsoft Kinect, Neural Network, Privacy-Aware Compliance Tracking System",

author = "Qing Wu and Wenbing Zhao",

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doi = "10.1109/EIT.2018.8500131",

language = "English",

volume = "2018-May",

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note = "2018 IEEE International Conference on Electro/Information Technology, EIT 2018 ; Conference date: 03-05-2018 Through 05-05-2018",

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Wu, Q & Zhao, W 2018, Machine Learning Based Human Activity Detection in a Privacy-Aware Compliance Tracking System. in IEEE International Conference on Electro Information Technology. vol. 2018-May, 8500131, IEEE Computer [email protected], usa, pp. 673-676, 2018 IEEE International Conference on Electro/Information Technology, EIT 2018, Rochester, United States, 05/3/18. https://doi.org/10.1109/EIT.2018.8500131

Machine Learning Based Human Activity Detection in a Privacy-Aware Compliance Tracking System. / Wu, Qing; Zhao, Wenbing.
IEEE International Conference on Electro Information Technology. Vol. 2018-May usa: IEEE Computer [email protected], 2018. p. 673-676 8500131.

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

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AB - In this paper, we report our work on using machine learning techniques to predict back bending activity based on field data acquired in a local nursing home. The data are recorded by a privacy-aware compliance tracking system (PACTS). The objective of PACTS is to detect back-bending activities and issue real-time alerts to the participant when she bends her back excessively, which we hope could help the participant form good habits of using proper body mechanics when performing lifting/pulling tasks. We show that our algorithms can differentiate nursing staffs baseline and high-level bending activities by using human skeleton data without any expert rules.

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KW - Machine Learning

KW - Microsoft Kinect

KW - Neural Network

KW - Privacy-Aware Compliance Tracking System

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Machine Learning Based Human Activity Detection in a Privacy-Aware Compliance Tracking System

Abstract

Conference

Keywords

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