Quantitative Biology

, Volume 4, Issue 4, pp 261–269 | Cite as

On the possibility of identifying human subjects using behavioural complexity analyses

  • Petr Kloucek
  • Armin von Gunten
Research Article



Identification of human subjects using a geometric approach to complexity analysis of behavioural data is designed to provide a basis for a more precise diagnosis leading towards personalised medicine.


The approach is based on capturing behavioural time-series that can be characterized by a fractional dimension using non-invasive longer-time acquisitions of heart rate, perfusion, blood oxygenation, skin temperature, relative movement and steps frequency. The geometry based approach consists in the analysis of the area and centroid of convex hulls encapsulating the behavioural data represented in Euclidian index spaces based on the scaling properties of the self-similar normally distributed behavioural time-series of the above mentioned quantities.


An example demonstrating the presented approach of behavioural fingerprinting is provided using sensory data of eight healthy human subjects based on approximately fifteen hours of data acquisition. Our results show that healthy subjects can be factorized to different similarity groups based on a particular choice of a convex hull in the corresponding Euclidian space. One of the results indicates that healthy subjects share only a small part of the convex hull pertaining to a highly trained individual from the geometric comparison point of view. Similarly, the presented pair-wise individual geometric similarity measure indicates large differences among the subjects suggesting the possibility of neuro-fingerprinting.


Recently introduced multi-channel body-attached sensors provide a possibility to acquire behavioural time-series that can be mathematically analysed to obtain various objective measures of behavioural patterns yielding behavioural diagnoses favouring personalised treatments of, e.g., neuropathologies or aging.


behavioural complexity indexing behavioural fingerprinting behavioural hysteresis non-disruptive personalized medicine 


  1. 1.
    Koenderink, J. J. (1984) The structure of images. Biol. Cybern., 50, 363–370CrossRefPubMedGoogle Scholar
  2. 2.
    Morel, J. M. and Solimini, S. (1995) Variational Methods in Image Segmentation. USA: Birkhauser Boston IncCrossRefGoogle Scholar
  3. 3.
    Julesz, B. (1980) Spatial nonlinearities in the instantaneous perception of textures with identical power spactra. Philos. Trans. R. Soc. Lond., 290, 83–94CrossRefGoogle Scholar
  4. 4.
    Julesz, B. (1981) Textons, the elements of texture perception, and their interactions. Nature, 290, 91–97CrossRefPubMedGoogle Scholar
  5. 5.
    Peitgen, H.-O., Jrgens, H. and Saupe, D. (1992) Chaos and Fractals. New York: Springer-VerlagCrossRefGoogle Scholar
  6. 6.
    Ness, M. V. (1968) Fractional Brownian motions, fractional noise and application. SIAM Rev., 10, 422–437CrossRefGoogle Scholar
  7. 7.
    Mandelbrot, B. B. (1997) Fractals, Form, Chance and Dimension. San Francisco: W. H. Freeman and CompanyGoogle Scholar
  8. 8.
    Bassingthwaighte, J. B., Liebovitch, L. S. andWest, B. J. (1994) Fractal Physiology. New York: Oxford University PressCrossRefGoogle Scholar
  9. 9.
    West, B. J. (2010) Fractal physiology and the fractional calculus: a perspective. Front. Physiol., 1, 12PubMedPubMedCentralGoogle Scholar
  10. 10.
    Preiss, D. (1987) Geometry of measures in Rn: distribution, rectifiliability and densities. Ann. Math., 125, 537–643CrossRefGoogle Scholar
  11. 11.
    Hassabis, D. and E. A. Maguire, (2009) The construction system of the brain. Phil. Trans. R. Soc. B. Biol. Sci., 364, 1263–1271CrossRefGoogle Scholar
  12. 12.
    Kloucek, P., P. Zakharov, and A. von Gunten, Indexing of Behavioural Complexity Using Self-similar Surrogate Data. Preprint, 2016Google Scholar
  13. 13.
    Morters, P. and Peres, Y. (2010) Brownian Motion. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge: Cambridge University PressGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH 2016

Authors and Affiliations

  1. 1.CAMPsyN, SUPAA, Hôpital de Cery, Route de CeryLausanne University HospitalPrilly, LausanneSwitzerland
  2. 2.SUPAA, Hôpital de Cery, Route de CeryLausanne University HospitalPrilly, LausanneSwitzerland

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