Advertisement

Heterogeneous Non Obtrusive Platform to Monitor, Assist and Provide Recommendations to Elders at Home: The MoveCare Platform

  • N. A. BorgheseEmail author
  • M. Bulgheroni
  • F. Miralles
  • A. Savanovic
  • S. Ferrante
  • T. Kounoudes
  • M. Cid Gala
  • A. Loutfi
  • A. Cangelosi
  • J. Gonzalez-Jimenez
  • A. Ianes
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 540)

Abstract

MoveCare develops and field tests an innovative multi-actor platform that supports the independent living of the elder at home by monitoring, assist and promoting activities to counteract decline and social exclusion. It is being developed under H2020 framework and it comprises 3 hierarchical layers: (1) A service layer provides monitoring and intervention. It endows objects of everyday use with advanced processing capabilities and integrates them in a distributed pervasive monitoring system to derive degradation indexes linked to decline. (2) A context-aware Virtual Caregiver, embodied into a service robot, is the core layer. It uses artificial intelligence and machine learning to propose to the elder a personalized mix of physical/cognitive/social activities as exer-games. It evaluates the elder status, detects risky conditions, sends alerts and assists in critical tasks, in therapy and diet adherence. (3) The users’ community strongly promotes socialization acting as a bridge towards the elders’ ecosystem: other elders, clinicians, caregivers and family. Gamification glues together monitoring, lifestyle, activities and assistance inside a motivating and rewarding experience. More information can be found at http://www.movecare-project.eu.

Notes

Acknowledgements

This work has been funded by EC grant N. 732158, MoveCare, under the call H2020-ICT-26b-2016 System abilities, development and pilot installations.

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
  9. 9.
  10. 10.
  11. 11.
  12. 12.
  13. 13.
  14. 14.
  15. 15.
  16. 16.
    Stuck, A. E., Siu, A. L., et al. (1993). Comprehensive geriatric assessment: A meta-analysis of controlled trials. The Lancet, 342, 1032.CrossRefGoogle Scholar
  17. 17.
    Action Plan on “Prevention and early diagnosis of frailty and functional decline, both physical and cognitive, in older people” of the European Innovation Partnership on Active and Healthy Ageing (Bruxelles, November 6, 2012).Google Scholar
  18. 18.
    Apóstolo, J., Cooke, R., et al. (2016). Effectiveness of the interventions in preventing the progression of pre-frailty and frailty in older adults: A systematic review protocol. JBI Database of Systematic Reviews and Implementation Reports, 14(1), 4–19.CrossRefGoogle Scholar
  19. 19.
  20. 20.
  21. 21.
    Pirovano, M., Mainetti, R., Baud-Bovy, G., Lanzi, P. L., & Borghese, N. A. (2016). IGER—Intelligent game engine for rehabilitation. IEEE Transactions on CIAIG, 8(1), 43–55.Google Scholar
  22. 22.
    Polinder, S., & The EUROCOST Reference Group. (2005). Cost estimation of injury-related hospital admissions in 10 European Countries. Journal of Trauma, 59(6), 1283–1291.Google Scholar
  23. 23.
    Howcroft, J., et al. (2013). Review of fall risk assessment in geriatric populations using inertial sensors. Journal of Neuroengineering and Rehabilitation, 10, 91.CrossRefGoogle Scholar
  24. 24.
    Riva, F., et al. (2013). Orbital stability analysis in biomechanics: A systematic review of a non linear technique to detect instability of motor tasks. Gait & Posture, 37, 1–11.CrossRefGoogle Scholar
  25. 25.
  26. 26.
  27. 27.
  28. 28.
  29. 29.
  30. 30.
    Das, R., & Kumar, N. (2015). Investigations on postural stability and spatiotemporal parameters of human gait using developed wearable smart insole. Journal of Medical Engineering & Technology, 39(1), 75–78.MathSciNetCrossRefGoogle Scholar
  31. 31.
  32. 32.
    Howcroft, J.D., Lemaire, E.D., Kofman, J., & McIlroy, W.E. (2014). Analysis of dual-task elderly gait using wearable plantar-pressure insoles and accelerometer. In Proceedings of IEEE Engineering Medical and Biology Society Conference (pp. 5003–5006).Google Scholar
  33. 33.
    Hassan, M., et al. (2014). Wearable gait measurement system with an instrumented cane for exoskeleton control. Sensors, 14, 1705–1722.CrossRefGoogle Scholar
  34. 34.
    Fellows, R. P., Dahmen, J., Cook, D., & Schmitter-Edgecombe, M. (2017). Multicomponent analysis of a digital trail making test. The Clinical Neuropsychologist, 31(1), 154–167.CrossRefGoogle Scholar
  35. 35.
    Gauthier, L., Dehaut, F., & Joanette, Y. (1989). The bells test: A quantitative and qualitative test for visual neglect. International Journal of Clinical Neuropsychology, 11, 49–54.Google Scholar
  36. 36.
    Jacobsen, E., & Lyons, R. (2003). The sliding DFT. Signal Processing Magazine, 20(2), 74–80.CrossRefGoogle Scholar
  37. 37.
    Naylor, P. A., Kounoudes, A., Gudnason, J., & Brookes, M. (2007). Estimation of glottal closure instants in voice speech using the DYPSA algorithm. IEEE Transactions on Audio, Speech and Language Processing, 15(1), 34–43.CrossRefGoogle Scholar
  38. 38.
    Busso, C., Lee, S., & Narayanan, S. (2009). Analysis of emotionally salient aspects of fundamental frequency for emotion detection. IEEE Transactions on Audio, Speech, and Language Processing, 17(4), 582–596.CrossRefGoogle Scholar
  39. 39.
    Mower, E., Mataric, M. J., & Narayanan, S. (2011). A framework for automatic human emotion classification using emotion profiles. IEEE Transactions on Audio, Speech, and Language Processing, 19(5), 1057–1070.CrossRefGoogle Scholar
  40. 40.
    Dallaert, F., Polzin, T., & Waibe, A. (1996). Recognizing emotion in speech. In Proceedings of International Conference on Spoken Language LP 96.Google Scholar
  41. 41.
    König, A., Satt, A., Sorin, A., et al. (2014). Automatic speech analysis for the assessment of pre-demented and Alzheimer patients. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring Journal, 1(1), 112–124.Google Scholar
  42. 42.
    Dixit, V., Mittal, V., & Sharma, Y. (2014). Voice parameter analysis for the disease detection. IOSR Journal of Electronics and Communication Engineering, 9(3), 48–55.CrossRefGoogle Scholar
  43. 43.
    Meilan, D., et al. (2014). Speech in Alzheimer’s disease: Can temporal and acoustic parameters discriminate dementia? Dementia and Geriatric Cognitive Disorders, 37, 327–334.CrossRefGoogle Scholar
  44. 44.
    Bickmore, T., & Picard, R. (2005). Establishing and maintaining long-term human-computer relationships. ACM Transactions CHI, 12(2), 293–327.Google Scholar
  45. 45.
    Fasola, J., & Mataric, M. J. (2012). Using socially assistive human-robot interaction to motivate physical exercise for older adults. Proceedings of IEEE, 100(8), 2512–2526.CrossRefGoogle Scholar
  46. 46.
    Seyama, J., & Nagayama, R. S. (2007). The uncanny valley: Effect of realism on the impression of artificial human faces. Presence, 16(4), 337–351.CrossRefGoogle Scholar
  47. 47.
    Ullberg, J., Loutfi, A., & Pecora, F. (2014). A customizable approach for monitoring activities of elderly users in their homes. Activity monitoring by multiple distributed sensing (pp. 13–25). Berlin: Springer International Publishing.Google Scholar
  48. 48.
    van Beek, P., & Manchak, D. W. (1996). The design and experimental analysis of algorithms for temporal reasoning. Journal of Artificial Intelligence Research, 4, 1–18.CrossRefGoogle Scholar
  49. 49.
    Bellocchio, F., Ferrari, S., Piuri, V., & Borghese, N. A. (2010). A hierarchical RBF online learning algorithm for real-time 3-D scanner. IEEE Transactions on Neural Networks, 21(2), 275–285.CrossRefGoogle Scholar
  50. 50.
    Bellocchio, F., Ferrari, S., Piuri, V., & Borghese, N. A. (2012). Hierarchical approach for multiscale support vector regression. IEEE Transactions on Neural Networks and Learning Systems, 23(9), 1448–1460.CrossRefGoogle Scholar
  51. 51.
    Coradeschi, S., Cesta, A., Cortellessa G., et al. (2014). GiraffPlus: A system for monitoring activities and physiological parameters and promoting social interaction for elderly. In Human-computer systems interaction: Backgrounds and applications (Vol. 3, pp. 261–271). Berlin: Springer International Publishing.Google Scholar
  52. 52.
    Kahler, O., Prisacariu, V.A., & Murray, D. (2016). Real-time large-scale dense 3D reconstruction with loop closure. In Proceeding of ECCV 2016.CrossRefGoogle Scholar
  53. 53.
    Jaimez, M., Blanco, J. L., & Gonzalez-Jimenez, J. (2015). Efficient reactive navigation with exact collision determination for 3D robot shapes. International Journal of Advanced Robotic Systems, 12(5), 63.CrossRefGoogle Scholar
  54. 54.
    Ren, C.Y., Prisacariu, V.A., Kahler, O., Murray, D.W., & Reid, I.D. (2016). Dense reconstruction and tracking of multiple 3D objects from depth-colour imagery. International Journal of Computer Vision.Google Scholar
  55. 55.
    Breazeal, C., et al. (2005). Effects of nonverbal communication on efficiency and robustness in human-robot teamwork. In Intelligent Robots and Systems (pp. 708–713).Google Scholar
  56. 56.
  57. 57.
  58. 58.
  59. 59.
  60. 60.
    Borghese, N.A., Mainetti, R., Essenziale, J., Cavalli, E., Mancon, E.M., & Pajardi, G. (2017). Hand rehabilitation with toys with embedded sensors. In J. Ibáñez, J. González-Vargas, J. María Azorín, M. Akay, & J.L. Pons (Eds.), Converging clinical and engineering research on neurorehabilitation II, Proceeding of. ICNR2017 (pp. 426–430).Google Scholar
  61. 61.
    Bevan, N. (2009). Extending quality in use to provide a framework for usability measurement. In HCD 09: Proceeding of First International Conference on Human Centered Design (pp. 13–22). Springer.Google Scholar
  62. 62.
    Hassenzahl, M. (2013). The thing and I: Understanding the relationship between user and product. In M.A. Blythe & K. Verbeeke (Eds.), Engineering computers (Vol. 29, pp. 359–373).Google Scholar
  63. 63.
    Monk, A. F. (Ed.). (2004). Funology: From usability to enjoyment (human–computer interaction series) (pp. 31–42). Norwell: Kluwer Academic Publishers.Google Scholar
  64. 64.
    Steinfeld, A., Fong, T., et al. (2006). Common metrics for human-robot interaction. In Proceeding of the 1st ACM/IEEE International Conference on Human Robot Interaction.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • N. A. Borghese
    • 1
    Email author
  • M. Bulgheroni
    • 2
  • F. Miralles
    • 3
  • A. Savanovic
    • 4
  • S. Ferrante
    • 5
  • T. Kounoudes
    • 6
  • M. Cid Gala
    • 7
  • A. Loutfi
    • 8
  • A. Cangelosi
    • 9
  • J. Gonzalez-Jimenez
    • 10
  • A. Ianes
    • 11
  1. 1.Applied Intelligent Systems-Laboratory, Department of Computer ScienceUniversità degli StudiMilanItaly
  2. 2.Ab.Acus srlMilanItaly
  3. 3.EURECATBarcelonaSpain
  4. 4.Smart ComLjubljanaSlovenia
  5. 5.NearLabPolitecnico di MilanoMilanItaly
  6. 6.Signal Generix LTDLimassolCyprus
  7. 7.SEPAD, Consejería de Sanidad y Políticas Sociales, Junta de ExtremaduraMeridaSpain
  8. 8.Orebro UniversityÖrebroSweden
  9. 9.Plymouth UniversityPlymouthUK
  10. 10.Universidad de MalagaMálagaSpain
  11. 11.Korian ItaliaMilanItaly

Personalised recommendations