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Multimodal Measurement Systems for Health and Behavior Analysis in Living Environment

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Book cover Current Trends in Biomedical Engineering and Bioimages Analysis (PCBEE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1033))

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Abstract

This review reveals and briefly discusses problems of capturing, archiving and analyzing the human behavior in a natural dwelling environment. Ambient assisted living systems are defined and justified with current social needs. Sensing paradigms and various examples of sensors are presented with a focus to their imperceptibility and data safety. Two paradigms of behavioral data storage are presented and examples of behavior predictive methods conclude the paper.

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References

  1. Augustyniak, P.: Layered design of an assisted living system for disabled. In: Piętka, E., Kawa, J. (eds.) Information Technologies in Biomedicine, pp. 498–509. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  2. Tseng, Y., Ho, Y., Kao, S., Su, C.: A 0.09 μW low power front-end biopotential amplifier for biosignal recording. IEEE Trans. Biomed. Circuits Syst. 6(5), 508–516 (2012)

    Article  Google Scholar 

  3. Bailey, C., Hollier, G., Moulds, A., Freeman, M., Austin, J., Fargus, A., Lampert, T.: Miniature multisensor biosignal data recorder and its evaluation for unsupervised Parkinson’s disease data collection. In: Sensordevices 2014, The Fifth International Conference on Sensor Device Technologies and Applications, pp. 84–92 (2014)

    Google Scholar 

  4. Zoladz, M., Kmon, P., Rauza, J., Grybos, P., Blasiak, T.: Multichannel neural recording system based on family ASICs processed in submicron technology. Microelectron. J. 45(9), 1226–1231 (2014)

    Article  Google Scholar 

  5. Kmon, P., Gryboś, P., Żołądź, M., Lisicka, A.: Fast and effective method of CMRR enhancement for multichannel integrated circuits dedicated to biomedical measurements. IEEE Electron. Lett. 51(22), 1736–1738 (2015)

    Article  Google Scholar 

  6. Liu, Y.-P., Chen, H.-C., Sung, P.-C.: Wireless logger for biosignals. Int. J. Appl. Sci. Eng. 8(1), 27–37 (2010)

    Google Scholar 

  7. Yang, G.: Hybrid integration of active bio-signal cable with intelligent electrode. Steps toward wearable pervasive-healthcare applications. Doctoral thesis, KTH Information and Communication Technology, Stockholm (2012). http://kth.diva-portal.org/smash/get/diva2:610512/FULLTEXT01.pdf. Accessed 23 Mar 2019

  8. Yang, G., Chen, J., Cao, Y., Tenhunen, H., Zheng, L.-R.: A novel wearable ECG monitoring system based on active-cable and intelligent electrodes. In: Proceedings of 10th International Conference on e-health Networking, Applications and Services, HealthCom 2008 (2008)

    Google Scholar 

  9. Chandler, R.J.: A system-level analysis of a wireless low-power biosignal recording device. UCLA Electronic Theses and Dissertations (2012). http://escholarship.org/uc/item/1836k3z4. Accessed 23 Mar 2019

  10. Augustyniak, P., Smoleń, M., Mikrut, Z., Kańtoch, E.: Seamless tracing of human behavior using complementary wearable and house-embedded sensors. Sensors 14, 7831–7856 (2014)

    Article  Google Scholar 

  11. Wojtowicz, B., Dobrowolski, A., Tomczykiewicz, K.: Fall detector using discrete wavelet decomposition and SVM classifier. Metrol. Meas. Syst. 22, 304 (2015)

    Article  Google Scholar 

  12. Augustyniak, P., Kantoch, E.: Turning domestic appliances into a sensor network for monitoring of activities of daily living. J. Med. Imaging Health Inform. 5(8), 1662–1667 (2015)

    Article  Google Scholar 

  13. Augustyniak, P.: Detection of behavioral data based on recordings from energy usage sensor. In: Rutkowski, L., et al. (eds.) Proceedings of 15th International Conference Artificial Intelligence and Soft Computing, ICAISC 2016, pp. 137–146 (2016)

    Google Scholar 

  14. Zoha, A., Gluhak, A., Imran, M.A., Rajasegarar, S.: Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey. Sensors 12, 16838–16866 (2012)

    Article  Google Scholar 

  15. Przybylo, J., Kantoch, E., Jablonski, M., Augustyniak, P.: Distant measurement of plethysmographic signal in various lighting conditions using configurable frame-rate camera. Metrol. Meas. Syst. 23(4), 579–592 (2016)

    Article  Google Scholar 

  16. Wang, W., den Brinker, A.C., Stuijk, S., de Haan, G.: Robust heart rate from fitness videos. Physiol. Meas. 38(6), 1023–1044 (2017)

    Article  Google Scholar 

  17. Kantoch, E.: Recognition of sedentary behavior by machine learning analysis of wearable sensors during activities of daily living for telemedical assessment of cardiovascular risk. Sensors 18, 3219 (2018). https://doi.org/10.3390/s18103219

    Article  Google Scholar 

  18. Kim, H., Kim, S., Van Helleputte, N., Artes, A., Konijnenburg, M., Huisken, J., Van Hoof, C., Yazicioglu, R.F.: A configurable and low-power mixed signal SoC for portable ECG monitoring applications. IEEE Trans. Biomed. Circuits Syst. 8(2), 257–267 (2014)

    Article  Google Scholar 

  19. Augustyniak, P.: Remotely programmable architecture of a multi-purpose physiological recorder. Microprocess. Microsyst. 46, 55–66 (2016)

    Article  Google Scholar 

  20. Padgette, J., Scarfone, K., Chen, L.: Security guide to Bluetooth - Recommendations of the National Institute of Standards and Technology, Special Publication 800-121 Rev. 1 (2012)

    Google Scholar 

  21. https://www.ti.com/product/CC3100MOD. Accessed 23 Mar 2019

  22. Crossman, J., Wray, R.E., Jones, R.M., Lebiere, C.: A high level symbolic representation for behavior modeling. http://cc.ist.psu.edu/BRIMS/archives/2004/Papers/04-BRIMS-051.pdf. Accessed 23 Mar 2019

  23. Wei, R., Liu, W., Xing W.: A symbolic representation of motion capture data for behavioral segmentation. In: Proceedings of 21st International Conference on Distributed Multimedia Systems, DMS 2015, pp. 78–84 (2015)

    Google Scholar 

  24. Kelley, R., Tavakkoli, A., King, C., Nicolescu, M., Bebis, G.: Understanding human intentions via hidden Markov models in autonomous mobile robots. In: Proceedings of Conference on Human Robot Interaction, Amsterdam, Netherlands (2008)

    Google Scholar 

  25. Pirsiavash, H., Ramanan, D.: Detecting activities of daily living in first-person camera views. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2847–2854 (2012)

    Google Scholar 

  26. Soran, B., Farhadi, A., Shapiro, L.: Generating notifications for missing actions: don’t forget to turn the lights off!. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4669–4677 (2015)

    Google Scholar 

  27. Wang, H., Yang, W., Yuan, C., Ling, H., Hu, W.: Human activity prediction using temporally-weighted generalized time warping. Neurocomputing 225, 139–147 (2017)

    Article  Google Scholar 

  28. Hassan, M.M., Uddin, M.Z., Mohamed, A., Almogren, A.: A robust human activity recognition system using smartphone sensors and deep learning. Future Gener. Comput. Syst. 81, 307–313 (2018)

    Article  Google Scholar 

  29. Kim, J.-M., Jeon, M.-J., Park, H.-K., Bae, S.-H., Bang, S.-H., Park, Y.-T.: An approach for recognition of human’s daily living patterns using intention ontology and event calculus. Expert Syst. Appl. 132, 256–270 (2019)

    Article  Google Scholar 

  30. Augustyniak, P., Slusarczyk, G.: Graph-based representation of behavior in detection and prediction of daily living activities. Comput. Biol. Med. 95, 261–270 (2018)

    Article  Google Scholar 

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Acknowledgment

Research supported by the AGH University of Science and Technology in year 2019 from the subvention granted by the Polish Ministry of Science and Higher Education.

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Authors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Piotr Augustyniak

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  1. Piotr Augustyniak
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Correspondence to Piotr Augustyniak .

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Editors and Affiliations

  1. Institute of Control and Computation Engineering, University of Zielona Gora, Zielona Góra, Poland

    Józef Korbicz

  2. Committee of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland

    Roman Maniewski

  3. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Krzysztof Patan

  4. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Marek Kowal

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Augustyniak, P. (2020). Multimodal Measurement Systems for Health and Behavior Analysis in Living Environment. In: Korbicz, J., Maniewski, R., Patan, K., Kowal, M. (eds) Current Trends in Biomedical Engineering and Bioimages Analysis. PCBEE 2019. Advances in Intelligent Systems and Computing, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-29885-2_18

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  • DOI: https://doi.org/10.1007/978-3-030-29885-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29884-5

  • Online ISBN: 978-3-030-29885-2

  • eBook Packages: EngineeringEngineering (R0)

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