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A Machine Learning Approach for Recognition of Elders’ Activities Using Passive Sensors

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Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops (AIAI 2021)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 628))

Abstract

The increasing ageing population around the world, is calling for technological innovations that can improve the lives of elderly people. Real time monitoring their activities is imperative in order to mitigate the detrimental occurrences and dangerous events like falls. The aim of this research is to develop and test a Machine Learning model, capable to determine the activity performed by the elderly in their everyday environment. Data for this research was acquired by setting up two fully monitored rooms, equipped with Radio Frequency Identification (RFID) antennas, while subjects who participated in the experiment were wearing a Wearable Wireless Identification and Sensing Platform (W2ISP) tag. The dataset consisted of 14 healthy elders, who would perform four activities namely: sitting on the bed, sitting on a chair, lying in bed and ambulating. Nine independent variables were used, eight of which were obtained by the sensors as raw data vectors and the ninth is the gender. The final data set includes 75,128 records. Totally, 25 Classification Algorithms were used in an effort to determine the more efficient model. The best performance has been achieved by employing the Bagged Trees algorithm. A combination of 10-fold Cross validation and Grid Search was used in order to tune the values of the hyperparameters and to avoid any form of overfitting or underfitting. The accuracy and the generalization ability of the optimal model, have been proved by the high values of all performance indices, with a very small deviation for the case of the fourth activity. Thus, this approach can be reliably used (with low cost) by caregivers, hospital staff or anyone else in charge, to watch for potentially dangerous situations for the elders.

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References

  1. Ahmed, M.U., Loutfi, A.: Physical activity identification using supervised machine learning and based on pulse rate. Int. J. Adv. Comput. Sci. Appl. 4(7), 210–217 (2013)

    Google Scholar 

  2. Becker, C., Rapp, K.: Fall prevention in nursing homes. Clin. Geriatr. Med. 26(4), 693–704 (2010)

    Article  Google Scholar 

  3. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(1), 281–305 (2012)

    MathSciNet  MATH  Google Scholar 

  4. Boukhechba, M., Cai, L., Wu, C., Barnes, L.E.: ActiPPG: Using deep neural networks for activity recognition from wrist-worn photoplethysmography (PPG) sensors. Smart Health 14, (2019)

    Article  Google Scholar 

  5. Breiman, L.: Arcing the edge. Technical report 486, Statistics Department, University of California at Berkeley (1997)

    Google Scholar 

  6. Breiman, L.: Bagging predictors. Technical report 421, Department of Statistics, University of California at Berkeley (1994)

    Google Scholar 

  7. Brophy, E., Veiga, J.J.D., Wang, Z., Smeaton, A. F., Ward, T.E.: An interpretable machine vision approach to human activity recognition using photoplethysmograph sensor data. arXiv preprint arXiv:1812.00668

  8. Buettner, M., Greenstein, B., Sample, A., Smith, J. R., Wetherall, D.: Revisiting smart dust with RFID sensor networks. In: Proceedings of the 7th ACM Workshop on Hot Topics in Networks (HotNets-VII) (2008)

    Google Scholar 

  9. Casale, P., Pujol, O., Radeva, P.: Human activity recognition from accelerometer data using a wearable device. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 289–296. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21257-4_36

    Chapter  Google Scholar 

  10. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

  11. Chen, L., Wei, H., Ferryman, J.: A survey of human motion analysis using depth imagery. Pattern Recogn. Lett. 34(15), 1995–2006 (2013)

    Article  Google Scholar 

  12. Chen, W., Wei, D., Zhu, X., Uchida, M., Ding, S., Cohen, M.: A mobile phone-based wearable vital signs monitoring system. In The Fifth International Conference on Computer and Information Technology (CIT 2005), pp. 950–955. IEEE (2005)

    Google Scholar 

  13. Eitrich, T., Lang, B.: Efficient optimization of support vector machine learn-ing parameters for unbalanced datasets. J. Comput. Appl. Math. 196(2), 425–436 (2006)

    Article  MathSciNet  Google Scholar 

  14. Gövercin, M., et al.: Defining the user requirements for wearable and optical fall prediction and fall detection devices for home use. Inform. Health Soc. Care 35(3–4), 177–187 (2010)

    Article  Google Scholar 

  15. Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification (2003)

    Google Scholar 

  16. Junker, H., Amft, O., Lukowicz, P., Tröster, G.: Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recogn. 41(6), 2010–2024 (2008)

    Article  Google Scholar 

  17. Kaufmann, T., Ranasinghe, D.C., Zhou, M., Fumeaux, C.: Wearable quarter-wave folded microstrip antenna for passive UHF RFID applications. Int. J. Antennas and Propag. (2013)

    Google Scholar 

  18. Li, X., et al.: Activity recognition for medical teamwork based on passive RFID. In: 2016 IEEE International Conference on RFID (RFID), pp. 1–9. IEEE (2016)

    Google Scholar 

  19. Miskelly, F.: A novel system of electronic tagging in patients with dementia and wandering. Age Ageing 33(3), 304–306 (2004)

    Article  Google Scholar 

  20. Nehmer, J., Becker, M., Karshmer, A., Lamm, R.: Living assistance systems: an ambient intelligence approach. In: Proceedings of the 28th International Conference on Software Engineering, pp. 43–50 (2006)

    Google Scholar 

  21. Oliver, D.: Prevention of falls in hospital inpatients. Agendas for research and practice. Age Ageing 33, 328–330 (2004)

    Article  Google Scholar 

  22. Pantelopoulos, A., Bourbakis, N.G.: A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(1), 1–12 (2009)

    Google Scholar 

  23. Pirttikangas, S., Fujinami, K., Nakajima, T.: Feature selection and activity recognition from wearable sensors. In: Youn, H.Y., Kim, M., Morikawa, H. (eds.) UCS 2006. LNCS, vol. 4239, pp. 516–527. Springer, Heidelberg (2006). https://doi.org/10.1007/11890348_39

    Chapter  Google Scholar 

  24. Psathas, A.P., Papaleonidas, A., Iliadis, L.: Machine learning modeling of human activity using PPG signals. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds.) ICCCI 2020. LNCS (LNAI), vol. 12496, pp. 543–557. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63007-2_42

    Chapter  Google Scholar 

  25. Psathas, A.P., Papaleonidas, A., Papathanassiou, G., Valkaniotis, S., Iliadis, L.: Classification of coseismic landslides using fuzzy and machine learning techniques. In: Iliadis, L., Angelov, P.P., Jayne, C., Pimenidis, E. (eds.) EANN 2020. PINNS, vol. 2, pp. 15–31. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-48791-1_2

    Chapter  Google Scholar 

  26. Ramasamy Ramamurthy, S., Roy, N.: Recent trends in machine learning for human activity recognition—a survey. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 8(4), (2018)

    Google Scholar 

  27. Ranasinghe, D.C., Sheng, M., Zeadally, S.: Unique radio innovation for the 21st Century: building scalable and global RFID networks. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-03462-6

    Book  Google Scholar 

  28. Rifkin, R., Klautau, A.: In defense of one-vs-all classification. J. Mach. Learn. Res. 5(Jan), 101–141 (2004)

    MathSciNet  MATH  Google Scholar 

  29. Robinovitch, S.N., et al.: Video capture of the circumstances of falls in elderly people residing in long-term care: an observational study. Lancet 381(9860), 47–54 (2013)

    Article  Google Scholar 

  30. Ryoo, J., Karimi, Y., Athalye, A., Stanaćević, M., Das, S. R., Djurić, P.: Barnet: towards activity recognition using passive backscattering tag-to-tag network. In: Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, pp. 414–427 (2018)

    Google Scholar 

  31. Sammut, C., Webb, G.I.: Encyclopedia of Machine Learning and Data Mining. Springer, Boston (2017). https://doi.org/10.1007/978-1-4899-7687-1

    Book  MATH  Google Scholar 

  32. Sample, A.P., Yeager, D.J., Powledge, P.S., Mamishev, A.V., Smith, J.R.: Design of an RFID-based battery-free programmable sensing platform. IEEE Trans. Instrum. Meas. 57(11), 2608–2615 (2008)

    Article  Google Scholar 

  33. Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S.: Boosting the margin: a new explanation for the effectiveness of voting methods. Ann. Stat. 26(5), 1651–1686 (1998)

    MathSciNet  MATH  Google Scholar 

  34. Shinmoto Torres, R.L., Visvanathan, R., Hoskins, S., Van den Hengel, A., Ranasinghe, D.C.: Effectiveness of a batteryless and wireless wearable sensor system for identifying bed and chair exits in healthy elders. Sensors 16(4), 546 (2016)

    Article  Google Scholar 

  35. Smith, R.E., Zane, N.W., Smoll, F.L.: Behavioral assessment in youth sports: coaching. Med. Sci. Sports Exerc. 15(3), 208–214 (1983)

    Article  Google Scholar 

  36. Su, Y., Wickramasinghe, A., Ranasinghe, D.C.: Investigating sensor data retrieval schemes for multi-sensor passive RFID tags. In: 2015 IEEE International Conference on RFID (RFID), pp. 158–165. IEEE (2015)

    Google Scholar 

  37. Shinmoto Torres, R.L., Ranasinghe, Damith C., Shi, Q.: Evaluation of wearable sensor tag data segmentation approaches for real time activity classification in elderly. In: Stojmenovic, I., Cheng, Z., Guo, S. (eds.) MindCare 2014. LNICST, vol. 131, pp. 384–395. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11569-6_30

    Chapter  Google Scholar 

  38. Torres, R.L.S., Ranasinghe, D.C., Shi, Q., Sample, A.P.: Sensor enabled wearable RFID technology for mitigating the risk of falls near beds. In 2013 IEEE International Conference on RFID (RFID), pp. 191–198. IEEE (2013)

    Google Scholar 

  39. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/Activity+recognition+with+healthy+older+people+using+a+batteryless+wearable+sensor. Accessed 21 Mar 2021

  40. Wang, L., Gu, T., Xie, H., Tao, X., Lu, J., Huang, Yu.: A wearable RFID system for real-time activity recognition using radio patterns. In: Stojmenovic, I., Cheng, Z., Guo, S. (eds.) MindCare 2014. LNICST, vol. 131, pp. 370–383. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11569-6_29

    Chapter  Google Scholar 

  41. Wickramasinghe, A., Ranasinghe, D.C.: Recognising activities in real time using body worn passive sensors with sparse data streams: to interpolate or not to interpolate? In: proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services on 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, pp. 21–30 (2016)

    Google Scholar 

  42. Wickramasinghe, A., Ranasinghe, D.C., Fumeaux, C., Hill, K.D., Visvanathan, R.: Sequence learning with passive RFID sensors for real-time bed-egress recognition in elders. IEEE J. Biomed. Health Inform. 21(4), 917–929 (2016)

    Article  Google Scholar 

  43. Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemometr. Intell. Lab. Syst. 2(1–3), 37–52 (1987)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Democritus University of Thrace, 67100, Xanthi, Greece

    Anastasios Panagiotis Psathas, Antonios Papaleonidas & Lazaros Iliadis

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  1. Anastasios Panagiotis Psathas
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  2. Antonios Papaleonidas
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  3. Lazaros Iliadis
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Correspondence to Anastasios Panagiotis Psathas or Antonios Papaleonidas .

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Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. University of Sunderland, Sunderland, UK

    John Macintyre

  3. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

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Psathas, A.P., Papaleonidas, A., Iliadis, L. (2021). A Machine Learning Approach for Recognition of Elders’ Activities Using Passive Sensors. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 628. Springer, Cham. https://doi.org/10.1007/978-3-030-79157-5_14

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  • DOI: https://doi.org/10.1007/978-3-030-79157-5_14

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