access icon openaccess Monitoring changes in behaviour from multi-sensor systems

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.

Inspec keywords: patient monitoring; behavioural sciences computing; sensor fusion; medical signal processing

Other keywords: automated behaviour-monitoring systems; data analysis method; multisensor systems; behavioural patterns; health status

Subjects: Biology and medical computing; Biomedical measurement and imaging; Social and behavioural sciences computing; Signal processing and detection; Sensor fusion; Biomedical engineering

References

    1. 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. 359370.
    2. 2)
    3. 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. 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. 817824.
    5. 5)
      • 9. Amor, J.D.: ‘Detecting and monitoring behavioural change through personalised ambient monitoring’ (University of Southampton, 2011).
    6. 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. 7)
      • 2. Basco, M.R., Rush, A.J.: ‘Cognitive-behavioral therapy for bipolar disorder’ (Guilford Press, 1996).
    8. 8)
    9. 9)
    10. 10)
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