Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients

ACM International Conference Proceeding Series

Volumn , Issue , 2014, Pages

  Gruenerbl, Agnes ^a   Osmani, Venet ^b   Bahle, Gernot ^a   Carrasco, Jose C ^b   Oehler, Stefan ^c   Mayora, Oscar ^b   Haring, Christian ^c   Lukowicz, Paul ^a  

^a GERMAN RESEARCH CENTER FOR ARTIFICIAL INTELLIGENCE DFKI   (Germany)

^b CREATE NET   (Italy)

^c Tilak Hall in Tirol ^*  (Austria)

Author keywords

Bipolar disorder; Real life study; Smart phone; State change detection; State recognition

Indexed keywords

DIAGNOSIS; RURAL AREAS; SIGNAL DETECTION; STATE ESTIMATION;

BIPOLAR DISORDER; CHANGE DETECTION; DATA COLLECTION; INERTIAL SENSOR; MEASUREMENT DEVICE; REAL-LIFE STUDY; RECOGNITION METHODS; STATE RECOGNITION;

SMARTPHONES;

EID: 84899797533     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2582051.2582089     Document Type: Conference Paper

References (23)

1. Bardram, J. E. Pervasive healthcare as a scientific discipline. Methods of information in medicine 47, 3 (2008), 178-185.
2. Bonato, P. Clinical applications of wearable technology. Conference Proceedings of the International Conference of IEEE Engineering in Medicine and Biology Society (2009), 6580-6583.
3. Bopp, J., Miklowitz, D., Goodwin, G., Stevens, W., Rendell, J., and JR., G. The longitudinal course of bipolar disorder as revealed through weekly text messaging: a feasibility study. Bipolar Disord 12, 3 (2010), 327-34.
4. Burns, M. N., Begale, M., Duffecy, J., Gergle, D., Karr, C. J., Giangrande, E., and Mohr, D. C. Harnessing context sensing to develop a mobile intervention for depression. Journal of Medical Internet Research 13, 3 (2011).
5. Fisher, R. A. The use of multiple measurements in taxonomic problems. 179-188.
6. Frost, M., Doryab, A., Faurholt-Jepsen, M., Kessing, L. V., and Bardram, J. E. Supporting disease insight through data analysis: refinements of the monarca self-Assessment system. In UbiComp, ACM (2013), 133-142.
7. Gruenerbl, A., Bahle, G., Lukowicz, P., and Hanser, F. Using indoor location to assess the state of dementia patients: Results and experience report from a long term, real world study. In Intelligent Environments, IEEE (2011), 32-39.
8. Gruenerbl, A., Bahle, G., Weppner, J., Oleksy, P., Haring, C., and Lukowicz, P. Towards smart phone based monitoring of bipolar disorder. In Proc. of the Second ACM Workshop on Mobile Systems, Applications, and Services for HealthCare, ACM (2012), 3:1-3:6.
9. Kullback, S., and Leibler, R. On information and sufficiency. 79-86.
10. LiKamWa, R., Liu, Y., Lane, N. D., and Zhong, L. Can your smartphone infer your mood? In 9th ACM Conference on Embedded Networked Sensor Systems (SenSys 2011), ACM (Seattle, WA, USA, 2011).
11. Lobban, F. Enhanced relaps prevention for bipolar disorder, 2007. BMC Psychiatry.
12. Lukowicz, P. Wearable computing and artificial intelligence for healthcare applications. Artificial Intelligence in Medicine 42, 2 (2008), 95-98.
13. Massey, T., Marfia, G., Potkonjak, M., and Sarrafzadeh, M. Experimental analysis of a mobile health system for mood disorders. IEEE Transactions on Information Technology in Biomedicine 14, 2 (2010), 241-247.
14. of Waikato, U. Data mining with open source mashine lerning software. Available at: http://www.cs.waikato.ac.nz/ml/weka/.
15. Optimism. Optimism apps, 2013. Available: www.findingoptimism.com. [Accessed: 09-Jul-2013].
16. Orwat, C., Graefe, A., and Faulwasser, T. Towards pervasive computing in health care-A literature review. BMC Medical Informatics and Decision Making 8, 26 (2008).
17. Osmani, V., Maxhuni, A., Gruenerbl, A., Lukowicz, P., C., H., and Mayora, O. Monitoring activity of paitents with bipolar disorder using smart phones. In Proceedings of MoMM (2013).
18. Pollack, M. E. The use of ai to assist elders with cognitive impairment for an aging population. AI Magazine 26, 2 (2005), 9-24.
19. Simpson, T. L., Kivlahan, D. R., Bush, K. R., and McFall, M. E. Telephone self-monitoring among alcohol use disorder patients in early recovery: a randomized study of feasibility and measurement reactivity. Drug and alcohol dependence 79, 2 (2005), 241-50.
20. Tacconi, D., Mayora, O., Lukowicz, P., Arnrich, B., Kappeler-Setz, C., Tröster, G., and Haring, C. Activity and emotion recognition to support early diagnosis of psychiatric diseases. In Proc. of 2nd Int. Conf. on Pervasive Computing Technologies for Healthcare (2008), 100-102.
21. Teng, X.-F., Zhang, Y.-T., Poon, C. C. Y., and Bonato, P. Wearable medical systems for p-health, 2008.
22. Westeyn, T. L., Abowd, G. D., Starner, T. E., Johnson, J. M., Presti, P. W., and Weaver, K. A. Monitoring children's developmental progress using augmented toys and activity recognition. Personal Ubiquitous Comput. 16, 2 (February 2012), 169-191.
23. Yun, T.-J., Jeong, H. Y., Hill, T. D., Lesnick, B., Brown, R., Abowd, G. D., and Arriaga, R. I. Using sms to provide continuous assessment and improve health outcomes for children with asthma. In Proc. of the 2nd International Health Informatics Symposium, ACM (2012), 621-630.