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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
  • 104 Downloads

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Keywords

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

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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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