Advertisement

Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis

  • Ivan Miguel PiresEmail author
  • Nuno M. Garcia
  • Nuno Pombo
  • Francisco Flórez-Revuelta
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 476)

Abstract

This paper presents a PhD project related to the identification of a set of Activities of Daily Living (ADLs) using different techniques applied to the sensors available in off-the-shelf mobile devices. This project consists on the creation of new methodologies, to identify ADLs, and to present some concepts, such as definition of the set of ADLs relevant to be identified, the mobile device as a multi-sensor system, review of the best techniques for data acquisition, data processing, data validation, data imputation, and data fusion processes, and creation of the methods for the identification of ADLs with data mining, pattern recognition and/or machine learning techniques. However, mobile devices present several limitations, therefore techniques at each stage have to be adapted. As result of this study, we presented a brief review of the state-of-the-art related to the several parts of a mobile-system for the identification of the ADLs. Currently, the main focus consists on the study for the creation of a new method based on the analysis of audio fingerprinting samples in some Ambient Assisted Living (AAL) scenarios.

Keywords

Sensors Data fusion Mobile devices Activities of Daily Living Data acquisition Data processing Data imputation Audio fingerprinting Pattern recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pires, I.M.S.: Aplicação móvel e plataforma Web para suporte à estimação do gasto energético em atividade física, in Informatics Department. University of Beira Interior, Covilhã (2012)Google Scholar
  2. 2.
    Pires, I.M., Garcia, N.M., Canavarro Teixeira, M.C.: Calculation of jump flight time using a mobile device. In: Proceedings of the HEALTHINF 2015 8th International Conference on Health Informatics, Lisbon, Portugal (2015)Google Scholar
  3. 3.
    Pires, I.M., et al.: Measurement of heel-rise test results using a mobile device. In: Proceedings of PhyCS 2015, 2nd International Conference on Physiological Computing Systems, Angers, France (2015)Google Scholar
  4. 4.
    Pires, I.M., Garcia, N.M., Flórez-Revuelta, F.: Multi-sensor data fusion techniques for the identification of activities of daily living using mobile devices. In: Proceedings of the ECMLPKDD 2015 Doctoral Consortium, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal (2015)Google Scholar
  5. 5.
    Garcia, N.M.: A Roadmap to the Design of a Personal Digital Life Coach (2016)Google Scholar
  6. 6.
    Morgan, S.: Activities of daily living. In: Community Mental Health, pp. 141–158. Springer, Heidelberg (1993)Google Scholar
  7. 7.
    Kuspa, K., Pratkanis, T.: Classification of Mobile Device Accelerometer Data for Unique Activity Identification (2013)Google Scholar
  8. 8.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter 12(2), 74 (2011)CrossRefGoogle Scholar
  9. 9.
    Siirtola, P., Röning, J.: Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data. International Journal of Interactive Multimedia and Artificial Intelligence 1(5), 38 (2012)CrossRefGoogle Scholar
  10. 10.
    Dernbach, S., et al.: Simple and complex activity recognition through smart phones. In: 2012 8th International Conference on Intelligent Environments (IE). IEEE, Guanajuato (2012)Google Scholar
  11. 11.
    Salazar, L.H.A., et al.: A Systematic Literature Review on Usability Heuristics for Mobile Phones. International Journal of Mobile Human Computer Interaction 5(2), 50–61 (2013)CrossRefGoogle Scholar
  12. 12.
    White, R.M.: A Sensor Classification Scheme. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 34(2), 124–126 (1987)CrossRefGoogle Scholar
  13. 13.
    Xu, R., He, L.: GACEM: Genetic Algorithm Based Classifier Ensemble in a Multi-sensor System. Sensors 8(10), 6203–6224 (2008)CrossRefGoogle Scholar
  14. 14.
    Mo, L., et al.: Multi-Sensor Ensemble Classifier for Activity Recognition. Journal of Software Engineering and Applications 5(12), 113–116 (2012)CrossRefGoogle Scholar
  15. 15.
    Scalvini, S., et al.: Information and communication technology in chronic diseases: a patient’s opportunity. Journal of Medicine and the Person 12(3), 91–95 (2013)CrossRefGoogle Scholar
  16. 16.
    Bersch, S.D., et al.: Sensor data acquisition and processing parameters for human activity classification. Sensors (Basel) 14(3), 4239–4270 (2014)CrossRefGoogle Scholar
  17. 17.
    Lim, L., Misra, A., Mo, T.: Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams. Distributed and Parallel Databases 31(2), 321–351 (2012)CrossRefGoogle Scholar
  18. 18.
    Dolui, K., Mukherjee, S., Datta, S.K.: Smart device sensing architectures and applications. In: 2013 International Computer Science and Engineering Conference (Icsec), pp. 91–96 (2013)Google Scholar
  19. 19.
    Sun, S., et al.: Literature review for data validation methods. Science and Technology 47(2), 95–102 (2011)Google Scholar
  20. 20.
    Garza-Ulloa, J., Yu, H., Sarkodie-Gyan, T.: A mathematical model for the validation of the ground reaction force sensor in human gait analysis. Measurement 45(4), 755–762 (2012)CrossRefGoogle Scholar
  21. 21.
    Vateekul, P., Sarinnapakorn, K.: Tree-based approach to missing data imputation. In: 2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009, pp. 70–75. IEEE, MiamiGoogle Scholar
  22. 22.
    Patrician, P.A.: Multiple imputation for missing data. Res. Nurs. Health 25(1), 76–84 (2002)CrossRefGoogle Scholar
  23. 23.
    Ling, W., Dong-Mei, F.: Estimation of missing values using a weighted k-nearest neighbors algorithm. In: International Conference on Environmental Science and Information Application Technology, ESIAT 2009, pp. 660–663. IEEE, WuhanGoogle Scholar
  24. 24.
    Jeffery, S.R., et al.: Declarative Support for Sensor Data Cleaning 3968, 83–100 (2006)Google Scholar
  25. 25.
    Banos, O., et al.: On the use of sensor fusion to reduce the impact of rotational and additive noise in human activity recognition. Sensors (Basel) 12(6), 8039–8054 (2012)CrossRefGoogle Scholar
  26. 26.
    Pombo, N., et al.: Medical decision-making inspired from aerospace multisensor data fusion concepts. Inform. Health Soc. Care 40(3), 185–197 (2015)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Tanveer, F., Waheed, O.T., ur Rehman, A.: Design and Development of a Sensor Fusion based Low Cost Attitude Estimator. Journal of Space Technology 1(1), 45–50 (2011)Google Scholar
  28. 28.
    Bujari, A., Licar, B., Palazzi, C.E.: Movement pattern recognition through smartphone’s accelerometer. In: 2012 IEEE Consumer Communications and Networking Conference (CCNC). IEEE, Las Vegas (2012)Google Scholar
  29. 29.
    Chikhaoui, B., Wang, S., Pigot, H.: A frequent pattern mining approach for ADLs recognition in smart environments. In: 2011 IEEE International Conference on Advanced Information Networking and Applications (AINA), IEEE, Biopolis (2011)Google Scholar
  30. 30.
    Kouris, I., Koutsouris, D.: A comparative study of pattern recognition classifiers to predict physical activities using smartphones and wearable body sensors. Technol. Health Care 20(4), 263–275 (2012)Google Scholar
  31. 31.
    Zhu, C., Sheng, W.: Realtime recognition of complex human daily activities using human motion and location data. IEEE Trans. Biomed. Eng. 59(9), 2422–2430 (2012)CrossRefGoogle Scholar
  32. 32.
    Farrahi, K., Gatica-Perez, D.: Daily routine classification from mobile phone data. In: Machine Learning for Multimodal Interaction, pp. 173–184. Springer, Heidelberg (2008)Google Scholar
  33. 33.
    Hong, J.-H., et al.: An activity recognition system for ambient assisted living environments. In: Evaluating AAL Systems Through Competitive Benchmarking, pp. 148–158. Springer, Heidelberg (2013)Google Scholar
  34. 34.
    Phithakkitnukoon, S., et al.: Activity-aware map: identifying human daily activity pattern using mobile phone data. In: Human Behavior Understanding, pp. 14–25. Springer, Heidelberg (2010)Google Scholar
  35. 35.
    Nam, Y., Rho, S., Lee, C.: Physical activity recognition using multiple sensors embedded in a wearable device. ACM Transactions on Embedded Computing Systems 12(2), 1–14 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ivan Miguel Pires
    • 1
    • 2
    • 3
    Email author
  • Nuno M. Garcia
    • 1
    • 3
    • 4
  • Nuno Pombo
    • 1
    • 3
  • Francisco Flórez-Revuelta
    • 5
  1. 1.Instituto de TelecomunicaçõesUniversity of Beira InteriorCovilhãPortugal
  2. 2.AltranportugalLisbonPortugal
  3. 3.ALLab - Assisted Living Computing and Telecommunications Laboratory, Department of InformaticsUniversity of Beira InteriorCovilhãPortugal
  4. 4.ECATIUniversidade Lusófona de Humanidades e TecnologiasLisbonPortugal
  5. 5.Faculty of Science, Engineering and ComputingKingston UniversityKingston upon ThamesUK

Personalised recommendations