posture recognition with g-sensors on smart phones
DESCRIPTION
Posture Recognition with G-Sensors on Smart Phones. Hui-Huang Hsu , Kang-Chun Tsai Dept of Computer Science and Information Engineering Tamkang University Zixue Cheng, Tongjun Huang School of Computer Science and Engineering University of Aizu. - PowerPoint PPT PresentationTRANSCRIPT
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Posture Recognition with
G-Sensors on Smart Phones
2012 15th International Conference on Network-Based Information Systems
Professor: Yih-Ran SheuStudent : Chan-jung WU
Hui-Huang Hsu , Kang-Chun TsaiDept of Computer Science and Information Engineering Tamkang
University
Zixue Cheng, Tongjun HuangSchool of Computer Science and Engineering University of Aizu
Digital Object Identifier :10.1109/NBiS.2012.135Date of Conference: 26-28 Sept. 2012Page(s):588 - 591
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Abstract Introduction Posture Recognition App Experimental Results and Implementation Conclusion and Future Work References
Outline
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Using smart phone to recognize the posture of the user. The app can record the postures of the user for the whole day and estimate the burned calories accordingly.
Abstract
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Weight control is a major issue in health management since overweighting is a very serious social problem in developed countries
Introduction 1/3
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Use the signals from G-sensor in the mobile phone to identify the postures of the user
Introduction 2/3
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Introduction 3/3
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System architecture
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Example posture signals
Posture Recognition App 1/3
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Artificial Neural Networks(ANN)
Posture Recognition App 2/3
sampling period of 0.04seconds
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Artificial Neural Networks
Posture Recognition App 2/3
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Posture Recognition App 2/3
摩托車
腳踏車
開車
搭車
Hidden note
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It is basically the weight (in Kg) of the user times the duration of the posture state (in hour) and a posture factor
Posture Recognition App 3/3
Calorie consumption
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Experimental Results and Implementation 1/3
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The sampling rate is 5 times per seconds. There are totally 20445 data points in the posture dataset
Experimental Results and Implementation 2/3
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The overall classification accuracy is 97 percent
Experimental Results and Implementation 3/3
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The user can be aware of his/her daily activities in a better way and possibly move more to enjoy a healthier life.
The user’s activity signals are collected and used to train a personalized neural network model for posture classification. This should be able to make the classification accuracy nearly perfect.
Conclusion and Future Work
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[1]http://www.airitilibrary.com/Publication/alDetailedMesh?docid=16086961 -200812-200907210037-200907210037-286-298[2] http://developer.android.com/about/index.html[3] http://developer.android.com/tools/sdk/eclipse-adt.html[4] http://www.csie.nctu.edu.tw/~kensl/AIrpt.html[5] http://developer.android.com/guide/components/index.html
REFERENCES