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User-Centric and Real-Time Activity Recognition Using Smart Glasses

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Green, Pervasive, and Cloud Computing

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9663))

Abstract

In this paper, we present a personalized and real-time prototyping solution on smart glasses targeting activity recognition. Our work is based on the analysis of sensor data to study user’s motions and activities, while utilizing wearable glasses bundled with various sensors. The software system collects, trains data, and builds the model for fast classification, which emphasizes on how specific features annotate and extract head-mounted behavior. Based on our feature selection algorithm, the system reaches high accuracy and low computation cost in the experiments. Other than some previous works in data mining on sensors of smart phones or smart glasses, and related works of activity recognition on smartphones, our results show the accuracy achieves 87 %, and the responsive time is less than 3 s. The proposed system can provide more insightful and powerful services for the glass wearers. It would be possibly expected to carry out more user-centric and context-aware wearable applications in the future.

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Notes

  1. 1.

    https://www.google.com/glass/start/.

References

  1. Lockhart, J.W., Pulickal, T., Weiss, G.M.: Applications of mobile activity recognition. In: 2012 Proceedings of the ACM Conference on Ubiquitous Computing. ACM (2012)

    Google Scholar 

  2. McNaney, R., et al.: Exploring the acceptability of google glass as an everyday assistive device for people with Parkinson’s. In: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems. ACM (2014)

    Google Scholar 

  3. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, Berlin (2011)

    Chapter  Google Scholar 

  4. Briem, V., Hedman, L.R.: Behavioural effects of mobile telephone use during simulated driving. Ergonomics 38(12), 2536–2562 (1995)

    Article  Google Scholar 

  5. Ishimaru, S., et al.: Shiny: an activity logging platform for Google Glass. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. ACM (2014)

    Google Scholar 

  6. Gouveia, R., Karapanos, E.: Footprint tracker: supporting diary studies with lifelogging. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM (2013)

    Google Scholar 

  7. Mazilu, S., et al.: GaitAssist: a wearable assistant for gait training and rehabilitation in Parkinson’s disease. In: 2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). IEEE (2014)

    Google Scholar 

  8. Khan, A.M., et al.: Human activity recognition via an accelerometer-enabled-smartphones using kernel discriminant analysis. In: 2010 5th International Conference on Future Information Technology (FutureTech). IEEE (2010)

    Google Scholar 

  9. Anjum, A., Ilyas, M.U.: Activity recognition using smartphones sensors. In: 2013 IEEE Consumer Communications and Networking Conference (CCNC). IEEE (2013)

    Google Scholar 

  10. Shoaib, M., Scholten, H., Havinga, P.J.: Towards physical activity recognition using smartphones sensors. In: IEEE 10th International Conference on Ubiquitous Intelligence and Computing, 2013 and 10th International Conference on Autonomic and Trusted Computing (UIC/ATC). IEEE (2013)

    Google Scholar 

  11. Kern, N., Schiele, B., Schmidt, A.: Multi-sensor activity context detection for wearable computing. In: Aarts, E., Collier, R.W., van Loenen, E., de Ruyter, B. (eds.) EUSAI 2003. LNCS, vol. 2875, pp. 220–232. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  12. Randell, C., Muller, H.: Context awareness by analysing accelerometer data. In: The Fourth International Symposium on Wearable Computers. IEEE (2000)

    Google Scholar 

  13. Parkka, J., et al.: Activity classification using realistic data from wearable sensors. IEEE Trans. Inf Technol. Biomed. 10(1), 119–128 (2006)

    Article  Google Scholar 

  14. Krause, A., Smailagic, A., Siewiorek, D.P.: Context-aware mobile computing: learning context-dependent personal preferences from a wearable sensor array. IEEE Trans. Mob. Comput. 5(2), 113–127 (2006)

    Article  Google Scholar 

  15. Ishimaru, S., et al.: Smarter eyewear: using commercial EOG glasses for activity recognition. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. ACM (2014)

    Google Scholar 

  16. Teichmann, D., et al.: Human motion classification based on a textile integrated and wearable sensor array. Physiol. Meas. 34(9), 963 (2013)

    Article  Google Scholar 

  17. LIBSVM: LIBSVM (2015). http://www.csie.ntu.edu.tw/~cjlin/libsvm/

  18. Chen, Y.-W., Lin, C.-J.: Combining SVMs with various feature selection strategies. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds.) Feature Extraction, vol. 207, pp. 315–324. Springer, Berlin (2006)

    Chapter  Google Scholar 

  19. Apple Watch: Apple Watch (2015). https://www.apple.com/tw/watch/

  20. Epson Moverio BT-200: Epson 2016. http://www.epson.com/cgi-bin/Store/jsp/Landing/moverio-bt-200-smart-glasses.do

  21. Sony SmartEyeglass Developer Edition: Sony (2016). http://developer.sonymobile.com/products/smarteyeglass/

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Author information

Authors and Affiliations

  1. Institute of Information Science, Academia Sinica, 115, Taipei, Taiwan

    Joshua Ho & Chien-Min Wang

Authors
  1. Joshua Ho
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  2. Chien-Min Wang
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Corresponding author

Correspondence to Chien-Min Wang .

Editor information

Editors and Affiliations

  1. Fujian Normal University, Fuzhou, China

    Xinyi Huang

  2. Deakin University, Burwood, Australia

    Yang Xiang

  3. Providence University, Taichung, Taiwan

    Kuan-Ching Li

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© 2016 Springer International Publishing Switzerland

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Ho, J., Wang, CM. (2016). User-Centric and Real-Time Activity Recognition Using Smart Glasses. In: Huang, X., Xiang, Y., Li, KC. (eds) Green, Pervasive, and Cloud Computing. Lecture Notes in Computer Science(), vol 9663. Springer, Cham. https://doi.org/10.1007/978-3-319-39077-2_13

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  • DOI: https://doi.org/10.1007/978-3-319-39077-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39076-5

  • Online ISBN: 978-3-319-39077-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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