An Efficient Probability Proto form Summary Technique for Finding Mental Health Condition

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2018 by IJETT Journal
Volume-58 Number-3
Year of Publication : 2018
Authors : Boddepalli Sai Rajeswari, P. Mohana Roopa


Boddepalli Sai Rajeswari, P. Mohana Roopa "An Efficient Probability Proto form Summary Technique for Finding Mental Health Condition", International Journal of Engineering Trends and Technology (IJETT), V58(3),142-145 April 2018. ISSN:2231-5381. published by seventh sense research group

Now a day’s to every person is give an importance to monitor our life style in every day or weekly or monthly. By performing monitoring process we can user sensors which record our walking habits and smart watches which along motion can measure heartbeat. After measuring those information can be taken into data format and used those data for finding changes in certain activity patterns may signal changes in person’s physical or mental health condition. Recognizing these changes ahead of time may help in preventing forthcoming health problems. A common way to understand and extract information from this data and make decisions based on them. Several studies to test the hypothesis that health problems lead to changes in sensor data pattern. In this paper showed that mental illness such as depression and dementia influences the patterns recorded by sensors monitoring in-home activity and the amount of time residents spend outside the apartments. By finding mental illness in this paper we are implement probability proto form summary technique. By implementing this technique we can find out mental ill ness of person.

[1] P. Glascock and D. M. Kutzik, “Behavioral telemedicine: A new approach to continuous nonintrusive monitoring of activities of daily living,” Telemed. J., vol. 6, no. 1, pp. 33–44, 2000.
[2] K. Z. Haigh, L. M. Kiff, and G. Ho, “Independent lifestyle assistant: Lessons learned,” Assist. Technol., vol. 18, pp. 87–106, 2006.
[3] M. Ogawa, R. Suzuki, S. Otake, T. Izutsu, T. Iwaya, and T. Togawa, “Longterm remote behavioural monitoring of the elderly using sensors installed in domestic houses,” in Proc. 2nd Joint EMBS/BMES Conf., Houston, TX, 2002, pp. 1853–1854.
[4] R. Beckwith, “Designing for ubiquity: The perception of privacy,” Pervasive Comput., pp. 40–46, Apr./Jun. 2003.
[5] T. S. Barger, D. E. Brown, and M. Alwan, “Health-status monitoring through analysis of behavioral patterns,” IEEE Trans. Syst., Man, Cybern. A, vol. 35, no. 1, pp. 22–27, Jan. 2005.
[6] J. Lundell, T. L. Hayes, S. Vurgun, U. Ozertem, J. Kimel, J. Kaye, F. Guilak, and M. Pavel, “Continuous activity monitoring and intelligent contextual prompting to improve medication adherence,” presented at the Int. Conf. IEEE Engineering in Medicine Biology Soc., Lyon, France, Aug. 23–26, 2007.
[7] P. Cuddihy, R. T. Hinman, A. Avestruz, E. C. Lupton, G. Livshin, J. L. Rodriguez, S. B. Leeb, C. M. Clark, K. J. Horvath, L. Volicer, B. Landfeldt, J. Kay, R. Kummerfeld, A. Quigley, D. West, T. Apted, G. Sinclair, D. J. Haniff, R. Kalawsky, D. Atkins, M. Lewin, S. J. Brown, N. Shahmehri, J. Aberg, D. Maciuszek, and I. Chisalita, “Successful aging,” Pervasive Comput., vol. 3, no. 2, pp. 48–50, Apr.–Jun. 2004.
[8] J. Kaye, S. Maxwell, N. Mattek, T. Hayes, H. Dodge, M. Pavel, H. Jimison, K. Wild, L. Boise, and T. Zitzelberger, “Intelligent systems for assessing aging changes: Home-based, unobtrusive and continuous assessment of aging,” J. Geron. B Psych. Sci., vol. 66B, pp. 180–190, 2011.
[9] H. Pigot, B. Lefebvre, J. G. Meunier, B. Kerherve, A. Mayers, and ´ S. Giroux, “The role of intelligent habitats in upholding elders in residence,” presented at the Int. Conf. Simulations in Biomedicine, Ljubljana, Slovenia, Apr. 2003.
[10] P. Paavilainen, I. Korhonen, and M. Partinen, “Telemetric activity monitoring as an indicator of long-term changes in health and well-being of the elderly,” Gerontechnology, vol. 4, pp. 77–85, 2005.
[11] J. Howell, B. M. Strong, J. Weisenberg, A. Kakade, Q. Gao, P. Cuddihy, S. Delisle, S. Kachnowski, and M. S. Maurer, “Maximum daily 6 minutes of activity: An index of functional capacity derived from actigraphy and its application to older adults with heart failure,” J. Amer. Geriatr. Soc., vol. 58, no. 5, pp. 931–936, May 2010.
[12] M. Philipose, K. P. Fishkin, M. Perkowitz, D. J. Patterson, D. Fox, H. Kautz, and D. Hhnel, “Inferring activities from interactions with objects,” Pervasive Comput., vol. 3, pp. 50–57, 2004.

Mental Health, Periodic Sensor Data, Training data set, Quantitative.