On the possibility of identifying human subjects using behavioural complexity analyses

作     者：Petr Kloucek Armin von Gunten 无

作者机构：CAMPsyN SUPAA HSpital de Cery Route de Cery Lausanne University Hospital CH-1008 Prilly Lausanne Switzerland SUPAA Hopital de Cery Route de Cery Lausanne University Hospital CH-1008 Prilly Lausanne Switzerland 

出 版 物：《Frontiers of Electrical and Electronic Engineering in China》 (中国电气与电子工程前沿（英文版）)

年 卷 期：2016年第4卷第4期

页      面：261-269页

核心收录：

学科分类：0710[理学-生物学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 070104[理学-应用数学] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported in part by Biovotion, AG 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 

主　　题：behavioural complexity indexing behavioural fingerprinting behavioural hysteresis non-disruptivepersonalized medicine 

摘      要：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 scaring 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.