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Posture Recognition with G-Sensors on Smart Phones

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.

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Posture Recognition with G-Sensors on Smart Phones

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  1. 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 2012 15th International Conference on Network-Based Information Systems Digital Object Identifier :10.1109/NBiS.2012.135 Date of Conference: 26-28 Sept. 2012 Page(s):588 - 591 Professor: Yih-Ran Sheu Student : Chan-jung WU

  2. Outline Abstract Introduction Posture Recognition App Experimental Results and Implementation Conclusion and Future Work References

  3. Abstract 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.

  4. Introduction 1/3 Weight control is a major issue in health management since overweighting is a very serious social problem in developed countries

  5. Introduction 2/3 Use the signals from G-sensor in the mobile phone to identify the postures of the user

  6. Introduction 3/3

  7. System architecture

  8. Posture Recognition App 1/3 Example posture signals

  9. Posture Recognition App 2/3 sampling period of 0.04seconds Artificial Neural Networks(ANN)

  10. Artificial Neural Networks Posture Recognition App 2/3

  11. Posture Recognition App 2/3 Hidden note 晴天 晴天 晴天 ? 早上 腳踏車 陰天 陰天 陰天 ? 雨天 雨天 雨天 ? 摩托車 ? 中午 ? 開車 ? ? 搭車 晚上 ? ?

  12. Posture Recognition App 3/3 Calorie consumption It is basically the weight (in Kg) of the user times the duration of the posture state (in hour) and a posture factor

  13. Experimental Results and Implementation 1/3

  14. The sampling rate is 5 times per seconds. There are totally 20445 data points in the posture dataset Experimental Results and Implementation 2/3

  15. The overall classification accuracy is 97 percent Experimental Results and Implementation 3/3

  16. Conclusion and Future Work 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.

  17. REFERENCES [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

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