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Modelling Reminder System for Dementia by Reinforcement Learning

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Sensor- and Video-Based Activity and Behavior Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 291))

Abstract

Prospective memory refers to preparing, remembering and recalling plans that have been conceived in an intended manner. Various busyness and distractions can make people forget the activities that must be done the next time, especially for people with cognitive memory problems such as dementia. In this paper, we propose a reminder system with the idea of taking time and response into consideration to assist in remembering activities. Using the reinforcement learning method, this idea predicts the right time to remind users through notifications on smartphones. The notification delivery time will be adjusted to the user’s response history, which becomes feedback at any available time. Thus, users will get notifications based on the ideal time for each individual either, either with repetition or without repetition, so as not to miss the planned activity. By evaluating the dataset, the results show that our proposed modelling is able to optimize the time to send notifications. The eight alternative times to send notifications can be optimized to get the best time to notify the user with dementia. This implies that our algorithm propose can adjust to individual personality characteristics, which might be a stumbling block in dementia patient care, and solve multi-routine plan problems. Our propose can be useful for users with dementia because we can remind very well that the execution time of notifications is right on target, so it can prevent users with dementia from stressing out over a lot of notifications, but those who miss notifications can receive them back at a later time step, with the result that information on activities to be completed is still available.

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References

  1. Abdul Razak, F.H., Sulo, R., Wan Adnan, W.A.: Elderly mental model of reminder system. In: Proceedings of the 10th Asia Pacific Conference on Computer Human Interaction, pp. 193–200 (2012)

    Google Scholar 

  2. Ahmed, Q., Mujib, S.: Activity recognition using smartphone accelerometer and gyroscope sensors supporting context-based reminder systems. In: Context Aware Reminder System, Faculty of Computing at Blekinge Institute of Technology (2014)

    Google Scholar 

  3. Alharbi, S., Altamimi, A., Al-Qahtani, F., Aljofi, B., Alsmadi, M., Alshabanah, M., Alrajhi, D., Almarashdeh, I.: Analyzing and implementing a mobile reminder system for alzheimer’s patients. In: Alharbi, S., Altamimi, A., Al-Qahtani, F., Aljofi, B., Alsmadi, MK, Alshabanah, M., Alrajhi, D., Almarashdeh, I., pp. 444–454 (2019)

    Google Scholar 

  4. Asghar, I., Cang, S., Yu, H.: A systematic mapping study on assitive technologies for people with dementia. In: 2015 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pp. 1–8. IEEE (2015)

    Google Scholar 

  5. Botella, C., Etchemendy, E., Castilla, D., Baños, R.M., García-Palacios, A., Quero, S., Alcaniz, M., Lozano, J.A.: An e-health system for the elderly (butler project): a pilot study on acceptance and satisfaction. CyberPsychology Behav. 12(3), 255–262 (2009)

    Article  Google Scholar 

  6. Brown, E.L., Ruggiano, N., Li, J., Clarke, P.J., Kay, E.S., Hristidis, V.: Smartphone-based health technologies for dementia care: opportunities, challenges, and current practices. J. Appl. Gerontol. 38(1), 73–91 (2019)

    Article  Google Scholar 

  7. Chaminda, H.T., Klyuev, V., Naruse, K.: A smart reminder system for complex human activities. In: 2012 14th international conference on advanced communication technology (ICACT), pp. 235–240. IEEE (2012)

    Google Scholar 

  8. Chen, H., Soh, Y.C.: A cooking assistance system for patients with alzheimers disease using reinforcement learning. Int. J. Inf. Technol. 23(2) (2017)

    Google Scholar 

  9. Czerwinski, M., Cutrell, E., Horvitz, E.: Instant messaging and interruption: Influence of task type on performance. In: OZCHI 2000 Conference Proceedings, vol. 356, pp. 361–367. Citeseer (2000)

    Google Scholar 

  10. Du, K., Zhang, D., Zhou, X., Mokhtari, M., Hariz, M., Qin, W.: Hycare: A hybrid context-aware reminding framework for elders with mild dementia. In: International Conference On Smart homes and health Telematics, pp. 9–17. Springer (2008)

    Google Scholar 

  11. Fikry, M.: Requirements analysis for reminder system in daily activity recognition dementia: Phd forum abstract. In: Proceedings of the 18th Conference on Embedded Networked Sensor Systems, pp. 815–816 (2020)

    Google Scholar 

  12. Fikry, M., Hamdhana, D., Lago, P., Inoue, S.: Activity recognition for assisting people with dementia. Contactless Hum. Act. Anal. 200, 271 (2021)

    Article  Google Scholar 

  13. Hayakawa, M., Uchimura, Y., Omae, K., Waki, K., Fujita, H., Ohe, K.: A smartphone-based medication self-management system with real-time medication monitoring. Appl. Clin. Inf. 4(01), 37–52 (2013)

    Article  Google Scholar 

  14. Ho, B.J., Balaji, B., Koseoglu, M., Sandha, S., Pei, S., Srivastava, M.: Quick question: interrupting users for microtasks with reinforcement learning. arXiv preprint arXiv:2007.09515 (2020)

  15. Horvitz, E., Apacible, J., Subramani, M.: Balancing awareness and interruption: investigation of notification deferral policies. In: International Conference on User Modeling, pp. 433–437. Springer (2005)

    Google Scholar 

  16. Horvitz, E.C.M.C.E.: Notification, disruption, and memory: effects of messaging interruptions on memory and performance. In: Human-Computer Interaction: INTERACT, vol. 1, p. 263 (2001)

    Google Scholar 

  17. Hsu, H.H., Lee, C.N., Chen, Y.F.: An rfid-based reminder system for smart home. In: 2011 IEEE International Conference on Advanced Information Networking and Applications, pp. 264–269. IEEE (2011)

    Google Scholar 

  18. Koumakis, L., Chatzaki, C., Kazantzaki, E., Maniadi, E., Tsiknakis, M.: Dementia care frameworks and assistive technologies for their implementation: a review. IEEE Rev. Biomed. Eng. 12, 4–18 (2019)

    Article  Google Scholar 

  19. Leiva, L., Böhmer, M., Gehring, S., Krüger, A.: Back to the app: the costs of mobile application interruptions. In: Proceedings of the 14th International Conference on Human-computer Interaction with Mobile Devices and Services, pp. 291–294 (2012)

    Google Scholar 

  20. Lester, R.T., Ritvo, P., Mills, E.J., Kariri, A., Karanja, S., Chung, M.H., Jack, W., Habyarimana, J., Sadatsafavi, M., Najafzadeh, M., et al.: Effects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (Weltel Kenya1): a randomised trial. The Lancet 376(9755), 1838–1845 (2010)

    Article  Google Scholar 

  21. McDaniel, M.A., Einstein, G.O.: The neuropsychology of prospective memory in normal aging: a componential approach. Neuropsychologia 49(8), 2147–2155 (2011)

    Article  Google Scholar 

  22. McGee-Lennon, M.R., Brewster, S.: Reminders that make sense: designing multimodal notifications for the home. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, pp. 495–501. IEEE (2011)

    Google Scholar 

  23. Mehrotra, A., Hendley, R., Musolesi, M.: Notifymehere: Intelligent notification delivery in multi-device environments. In: Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, pp. 103–111 (2019)

    Google Scholar 

  24. Mehrotra, A., Musolesi, M., Hendley, R., Pejovic, V.: Designing content-driven intelligent notification mechanisms for mobile applications. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 813–824 (2015)

    Google Scholar 

  25. Meiland, F.J., Reinersmann, A., Sävenstedt, S., Bergvall-Kåreborn, B., Hettinga, M., Craig, D., Andersson, A.L., Dröes, R.M.: User-participatory development of assistive technology for people with dementia-from needs to functional requirements. first results of the cogknow project. Dementia, pp. 71–91 (2012)

    Google Scholar 

  26. Morrison, L.G., Hargood, C., Pejovic, V., Geraghty, A.W., Lloyd, S., Goodman, N., Michaelides, D.T., Weston, A., Musolesi, M., Weal, M.J., et al.: The effect of timing and frequency of push notifications on usage of a smartphone-based stress management intervention: an exploratory trial. PloS one 12(1), e0169162 (2017)

    Article  Google Scholar 

  27. Obert, J., Shia, A.: Optimizing dynamic timing analysis with reinforcement learning. Tech. rep., Sandia National Lab.(SNL-NM), Albuquerque, NM (United States) (2019)

    Google Scholar 

  28. Pielot, M., Church, K., De Oliveira, R.: An in-situ study of mobile phone notifications. In: Proceedings of the 16th International Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 233–242 (2014)

    Google Scholar 

  29. Pollack, M.E., Brown, L., Colbry, D., McCarthy, C.E., Orosz, C., Peintner, B., Ramakrishnan, S., Tsamardinos, I.: Autominder: an intelligent cognitive orthotic system for people with memory impairment. Robot. Auton. Syst. 44(3–4), 273–282 (2003)

    Article  Google Scholar 

  30. Ramljak, M.: Smart home medication reminder system. In: 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1–5. IEEE (2017)

    Google Scholar 

  31. Sanchez, V.G., Pfeiffer, C.F., Skeie, N.O.: A review of smart house analysis methods for assisting older people living alone. J. Sens. Actuator Netw. 6(3), 11 (2017)

    Article  Google Scholar 

  32. Sutton, R.S., Barto, A.G.: Reinforcement learning: an introduction. MIT press (2018)

    Google Scholar 

  33. Tarawneh, R., Holtzman, D.M.: The clinical problem of symptomatic Alzheimer disease and mild cognitive impairment. Cold Spring Harb. Perspect. Med. 2(5), a006148 (2012)

    Article  Google Scholar 

  34. Vogt, J., Luyten, K., Van den Bergh, J., Coninx, K., Meier, A.: Putting dementia into context. In: International Conference on Human-Centred Software Engineering, pp. 181–198. Springer (2012)

    Google Scholar 

  35. Wang, H.J., Shi, Y., Zhao, D., Liu, A., Yang, C.: Automatic reminder system of medical orders based on bluetooth. In: 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1–4. IEEE (2011)

    Google Scholar 

  36. Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3–4), 279–292 (1992)

    MATH  Google Scholar 

  37. Wu, Y.H., Wrobel, J., Cristancho-Lacroix, V., Kamali, L., Chetouani, M., Duhaut, D., Le Pévédic, B., Jost, C., Dupourque, V., Ghrissi, M., et al.: Designing an assistive robot for older adults: the ROBADOM project. Irbm 34(2), 119–123 (2013)

    Article  Google Scholar 

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Correspondence to Muhammad Fikry .

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Fikry, M., Mairittha, N., Inoue, S. (2022). Modelling Reminder System for Dementia by Reinforcement Learning. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Sensor- and Video-Based Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-19-0361-8_9

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