On the possibility of identifying human subjects using behavioural complexity analyses

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.

Graphical abstract

Keywords

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

References

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AUTHORS’ CONTRIBUTIONS

PK developed the mathematical framework as well as the computational eco-system, Cassiopee, to compute and to analyse the behavioural complexity indices yielding the possibility of “behavioural fingerprinting”. AvG provided initial impulse to use our previous work [5] to address the possibility of the behavioural fingerprinting. He also provided the clinical expertise to test our approach.

ACKNOWLEDGEMENTS

The authors were supported in part by Biovotion, AG, that provided the sensors and access to the raw sensory data. Our activities were supported in part by the Service de Psychiatrie de l'Age Avance that is part of Centre hospitalier universitaire Vaudois and by University of Lausanne, Switzerland.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Petr Kloucek and Armin von Gunten declare that they have no competing interests.

The authors declare that

(i) The study was carried out in accordance with the Declaration of Helsinki and CIOM;

(ii) All subjects gave their written informed consent.

The authors declare that the presented research includes observational research only with no video or participation by the authors of this study. Consequently, this communication is exempt from an Institutional review board review. They refer to Code of Federal Regulations of the United States of America, Part 46, Protection of Human Subjects, and Revised on January 15, 2009.

Funding

RIGHTS & PERMISSIONS

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg