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Activity tracking and monitoring of patients with alzheimer’s disease

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Abstract

In this paper, by applying motion detection and machine learning technologies, we have designed and developed an activity tracking and monitoring system, called SmartMind, to help Alzheimer’s Disease (AD) patients to live independently within their living rooms while providing emergency assistances and supports when necessary. Allowing AD patients to handle their daily activities not only can release the burdens on their families and caregivers, it is also highly important to help them regain confidence towards a healthy life. The daily activities of a patient captured from SmartMind can also serve as an important indicator to describe his/her normal living habit (NLH). By checking NLH, the patient’s current health status can be estimated on a daily basis. In the testing experiments of SmartMind, we have demonstrated the accuracy of SmartMind in activity detection and investigated its performance when different machine learning algorithms were adopted for posture detection. The performance results indicate that both support vector machine (SVM) and naive bayes (NB) can achieve an accuracy of higher than 97 % while the random forrests (RF) only gives an accuracy of around 73 %.

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References

  1. Alzheimer’s Society (2014) http://alzheimers.org.uk/

  2. Bardram JE, Frost M, Szántó K, Faurholt-Jepsen M, Vinberg M, Kessing LV (2013) Designing mobile health technology for bipolar disorder: a field trial of the monarca system. In: SIGCHI Conference on Human Factors in Computing Systems, pp 2627–2636

  3. Bian Z-P, Hou J, Chau L-P, Magnenat-Thalmann N (2014) Fall detection based on body part tracking using a depth camera. IEEE J Biomed Health Inf 19(2)

  4. Bianchi F, Redmond S, Narayanan M, Cerutti S, Lovell N (2010) Barometric pressure and triaxial accelerometry-based falls event detection. IEEE Trans Neural Syst Rehabil Eng 18(6):619– 627

    Article  Google Scholar 

  5. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  6. Browning RC, Baker EA, Herron JA, Kram R (2006) Effects of obesity and sex on the energetic cost and preferred speed of walking. J Appl Physiol 100(2):390–398

    Article  Google Scholar 

  7. Chang C-C, Lin C-J Libsvm – a library for support vector machines. http://ntucsu.csie.ntu.edu.tw/cjlin/libsvm/

  8. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  9. Cottone P, Maida B, Morana M (2014) User activity recognition via kinect in an ambient intelligence scenario. In: International Conference on Appplied Computing, Computer Science and Computer Engineering, pp 49–54

  10. Dawadi PN, Cook DJ, Schmitter-Edgecombe M (2013) Automated cognitive health assessment using smart home monitoring of complex tasks. IEEE Trans Syst, Man Cybern: Syst 43(6):1302– 1313

    Article  Google Scholar 

  11. De Ipina KL, Alonso J, Travieso C, Egiraun H, Ecay M, Ezeiza A, Barroso N, Martinez-Lage P (2013) Automatic analysis of emotional response based on non-linear speech modeling oriented to alzheimer disease diagnosis. In: 2013 IEEE 17th International Conference on Intelligent Engineering Systems (INES), pp 61–64

  12. De Urturi Breton ZS, Zapirain BG, Zorrilla AM (2012) Kimentia: Kinect based tool to help cognitive stimulation for individuals with dementia. In: IEEE International Conference on e-Health Networking, Applications and Services, pp 325–328

  13. Diaz-Ramirez A, Murrieta FN, Atempa JA, Bonino FA (2013) Non-intrusive tracking of patients with dementia using a wireless sensor network. In: IEEE International Conference on Distributed Computing in Sensor Systems, pp 460–465

  14. Dickerson RF, Gorlin EI, Stankovic JA (2011) Empath: a continuous remote emotional health monitoring system for depressive illness. In: Wireless Health, p 5

  15. Fosty B, Crispim-Junior CF, Badie J, Bremond F, Thonnat M (2013) Event recgnition system for older people monitoring using rgb-d camera. In: Workshop on Assistance and Service Robotics in a Human Environment, Tokyo, Japan

  16. Gaggioli A, Pioggia G, Tartarisco G, Baldus G, Corda D, Cipresso P, Riva G (2013) A mobile data collection platform for mental health research. Person Ubiquit Comput 17(2):241–251

    Article  Google Scholar 

  17. Hong Kong Hospital Authority (2014) Smart patient website - dementia, hospital authority. http://www21.ha.org.hk/smartpatient/en/chronicdiseaseszone/details.html?id=174

  18. Jaiantilal A Random forest (regression, classification and clustering) implementation for matlab. http://code.google.com/p/randomforest-matlab/

  19. Khan W, Xiang Y, Aalsalem M, Arshad Q (2013) Mobile phone sensing systems: A survey. IEEE Commun Surv Tutorials 15(1):402–427

    Article  Google Scholar 

  20. Lai C-F, Chang S-Y, Chao H-C, Huang Y-M (2011) Detection of cognitive injured body region using multiple triaxial accelerometers for elderly falling. IEEE Sensors J 11(3):763–770

    Article  Google Scholar 

  21. Lane N, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell A (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150

    Article  Google Scholar 

  22. Lin Q, Zhang D, Huang X, Ni H, Zhou X (2012) Detecting wandering behavior based on gps traces for elders with dementia. In: 2012 12th International Conference on Control Automation Robotics Vision (ICARCV), pp 672–677

  23. Low L-S, Maddage M, Lech M, Sheeber L, Allen N (2011) Detection of clinical depression in adolescent’s speech during family interactions. IEEE Trans Biomed Eng 58(3):574–586

    Article  Google Scholar 

  24. Ma Y, Xu B, Bai Y, Sun G, Zhu R (2012) Daily mood assessment based on mobile phone sensing. In: International Conference on Wearable and Implantable Body Sensor Networks, pp 142–147

  25. Mace NL, Rabins PV (2012) The 36-Hour Day: A Family Guide to Caring for People Who Have Alzheimer Disease, Related Dementias, and Memory Loss. Grand Central Life & Style, 5th edn.

  26. Microsoft Crop (2014) Kinect for windows. http://www.microsoft.com/en-us/kinectforwindows/

  27. Mubashir M, Shao L, Seed L (2013) A survey on fall detection: Principles and approaches. Neurocomputing 100:144–152

    Article  Google Scholar 

  28. Naranjo-Hernandez D, Roa LM, Reina-Tosina J, Estudillo-Valderrama MA (2012) Som: A smart sensor for human activity monitoring and assisted healthy ageing. IEEE Trans Biomed Eng 59(11-2):3177–3184

    Article  Google Scholar 

  29. Navarro RF, Rodrígues MD, Favela J (2014) Intervention tailoring in augmented cognition systems for elders with dementia. IEEE J Biomed Health Inf 18 (1):361–367

    Article  Google Scholar 

  30. Newport MT (2013) Alzheimers Disease: What If There Was a Cure? Basic Health Publications, 2nd edn.

  31. Norvig SJRP (2003) Artificial intelligence : a modern approach. Pearson Education

  32. Nyan M, Tay FE, Murugasu E (2008) A wearable system for pre-impact fall detection. J Biomech 41:34753481

    Google Scholar 

  33. Opperman C, Hancke G (2011) A generic NFC-enabled measurement system for remote monitoring and control of client-side equipment. In: Workshop on Near Field Communication, pp 44–49

  34. Poh M-Z, Kim K, Goessling A, Swenson N, Picard R (2012) Cardiovascular monitoring using earphones and a mobile device. IEEE Pervasive Comput 11(4):18–26

    Article  Google Scholar 

  35. Rantz MJ, Banerjee TS, Cattoor E, Scott SD, Skubic M, Popescu M (2014) Automated fall detection with quality improvment rewind to reduce falls in hospital. J Gerontol Nurs 40(1):13–17

    Article  Google Scholar 

  36. Roark B, Mitchell M, Hosom J-P, Hollongshead K, Kaye J (2011) Spoken lanugage derived measures for detecting mild cognitive impairment. IEEE Trans Audio, Speech Lang Process 19(7):2081– 2090

    Article  Google Scholar 

  37. Rougier C, Meunier J, St-Arnaud A, Rousseau J (2011) Robust video surveillance for fall detection based on human shape deformation. IEEE Trans Circ Syst Video Technol 21(5):611–622

    Article  Google Scholar 

  38. Scarff SK, Zultner AK (2012) Dementia: The Journey Ahead - A Practical Guide for In-Home Caregivers, 1st edn. Langdon Street Press

  39. Scully C, Lee J, Meyer J, Gorbach A, Granquist-Fraser D, Mendelson Y, Chon K (2012) Physiological parameter monitoring from optical recordings with a mobile phone. IEEE Trans Biomed Eng 59(2):303–306

    Article  Google Scholar 

  40. Shi G, Chan CS, Li WJ, Leung K-S, Zou Y, Jin Y (2009) Mobile human airbag system for fall protection using mems sensors and embedded svm classifier. IEEE Sensors J 9(5):495–503

    Article  Google Scholar 

  41. Simplicio MA, Iwaya LH, Barros BM, Carvalho T. CMB, Näslund M (2015) SecourHealth: a delay-tolerant security framework for mobile health data collection. IEEE J Biomed Health Inf 19(2):761–772

    Article  Google Scholar 

  42. Stone E, Skubic M (2011) Evaluation of an inexpensive depth camera for in-home gait assessment. J Ambient Intell Smart Environ 3(4):349–361

    Google Scholar 

  43. Taub DM, Leeb SB, Lupton EC, Hinman RT, Zeisel J, Blackler S (2011) The escort system: a safety monitor for people living with alzheimers disease. Pervasive Computing, pp 68–76

  44. Xiao Z, Fu B, Xiao Y, Chen CP, Liang W (2013) A review of geni authentication and access control mechanisms. Int J Secur Netw 8(1):40–60

    Article  Google Scholar 

  45. Zmily A, Abu-Saymeh D (2013) Alzheimers disease rehabilitation using smartphones to improve patients quality of life. In: 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops

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Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Kam-Yiu Lam, Nelson Wai-Hung Tsang & Ajit Nath

  2. Department of Computer Science and Engineering, University of Connecticut, Connecticut, USA

    Song Han

  3. Polytechnic School, Ira A. Fulton Schools of Engineering, Arizona State University, Arizona, USA

    Wenlong Zhang

  4. Department of Computing, Baptist University of Hong Kong, Kowloon Tong, Hong Kong

    Joseph Kee-Yin Ng

Authors
  1. Kam-Yiu Lam
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  2. Nelson Wai-Hung Tsang
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  3. Song Han
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  4. Wenlong Zhang
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  5. Joseph Kee-Yin Ng
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  6. Ajit Nath
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Corresponding author

Correspondence to Kam-Yiu Lam.

Additional information

A preliminary version of the paper is appearred in The 29th IEEE International Conference on Advanced Information Networking and Applications (AINA-2015), Gwangju, Korea, March 2015.

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Lam, KY., Tsang, N.WH., Han, S. et al. Activity tracking and monitoring of patients with alzheimer’s disease. Multimed Tools Appl 76, 489–521 (2017). https://doi.org/10.1007/s11042-015-3047-x

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  • DOI: https://doi.org/10.1007/s11042-015-3047-x

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