Behavioural patterns are important indicators of health status in a number of conditions and changes in behaviour can often indicate a change in health status. Currently, limited behaviour monitoring is carried out using paper-based assessment techniques. As technology becomes more prevalent and low-cost, there is an increasing movement towards automated behaviour-monitoring systems. These systems typically make use of a multi-sensor environment to gather data. Large data volumes are produced in this way, which poses a significant problem in terms of extracting useful indicators. Presented is a novel method for detecting behavioural patterns and calculating a metric for quantifying behavioural change in multi-sensor environments. The data analysis method is shown and an experimental validation of the method is presented which shows that it is possible to detect the difference between weekdays and weekend days. Two participants are analysed, with different sensor configurations and test environments and in both cases, the results show that the behavioural change metric for weekdays and weekend days is significantly different at 95% confidence level, using the methods presented.
References
-
-
1)
-
6. Berndt, D.J., James, C.: ‘Using dynamic time warping to find patterns in time series’. KDD workshop, 1994, Vol. 10, No. 16, pp. 359–370.
-
2)
-
1. Thomas Bodenheimer, M.D., Kate Lorig, R.N., Halsted Holman, M.D., Kevin Grumbach, M.D.: ‘Patient self-management of chronic disease in primary care’, J. Am. Med. Associat., 2002, 288, (19), pp. 2469–2475 (doi: 10.1001/jama.288.19.2469).
-
3)
-
5. James, C.J., Crowe, J., Magill, E., et al: ‘Personalised ambient monitoring (PAM) of the mentally ill’. In Fourth European Conf. of the Int. Federation for Medical and Biological Engineering, Vol. 22, 2009.
-
4)
-
10. Listgarten, J., Neal, R.M., Roweis, S.T., Emili, A.: ‘Multiple alignment of continuous time series’. Advances in Neural Information Processing Systems 17, Cambridge, MA, USA, 2005, pp. 817–824.
-
5)
-
9. Amor, J.D.: ‘Detecting and monitoring behavioural change through personalised ambient monitoring’ (University of Southampton, 2011).
-
6)
-
8. Amor, J.D., James, C.J.: ‘Behavioral pattern detection from personalized ambient monitoring’. Proc. of the 32nd Annual Int. Conf. of the IEEE EMBS, Buenos Aires, Argentina, September 2010.
-
7)
-
2. Basco, M.R., Rush, A.J.: ‘Cognitive-behavioral therapy for bipolar disorder’ (Guilford Press, 1996).
-
8)
-
3. Nijs, J., Paul, L., Wallman, K.: ‘Chronic fatigue syndrome: an approach combining self-management with graded exercise to avoid exacerbations’, J. Rehabilitation Med., 2008, 40, (4), pp. 241–247(7) (doi: 10.2340/16501977-0185).
-
9)
-
4. Lotfi, A., Langensiepen, C., Mahmoud, S.M., Akhlaghinia, M.J.: ‘Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behavior’, J. Ambient Intell. Humanized Comput., 2012, 3, (3), pp. 205–218 (doi: 10.1007/s12652-010-0043-x).
-
10)
-
7. Barger, T.S., Brown, D.E., Alwan, M.: ‘Health-status monitoring through analysis of behavioral patterns’, IEEE Trans. Syst. Man Cybern. A, Syst. Humans, 2005, 35, (1), pp. 22–27 (doi: 10.1109/TSMCA.2004.838474).
http://iet.metastore.ingenta.com/content/journals/10.1049/htl.2014.0089
Related content
content/journals/10.1049/htl.2014.0089
pub_keyword,iet_inspecKeyword,pub_concept
6
6