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 a...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.展开更多
Applications of a constitutive framework providing compound complexity analysis and indexing of coarse-grained self-similar time series representing behavioural data are presented. A notion of behavioural entropy and ...Applications of a constitutive framework providing compound complexity analysis and indexing of coarse-grained self-similar time series representing behavioural data are presented. A notion of behavioural entropy and hysteresis is introduced as two different forms of compound measures. These measures provide clinically applicable complexity analysis of behavioural patterns yielding scalar characterisation of time-varying behaviours registered over an extended period of time. The behavioural data are obtained using body attached sensors providing non-invasive readings of heart rate, skin blood perfusion, blood oxygenation, skin temperature, movement and steps frequency. The results using compound measures of behavioural patterns of fifteen healthy individuals are presented. The application of the compound measures is shown to correlate with complexity analysis. The correlation is demonstrated using two healthy subjects compared against a control group. This indicates a possibility to use these measures in place of fractional dimensions to provide a finer characterisation of behavioural patterns observed using sensory data acquired over a long period of time.展开更多
The complexation behaviours of trivalent rare earth elements (La, Ce, Ho and Yb) by two types of humic acids were investigated under a specified set of conditions. Humic acids show quite different complexation capaci...The complexation behaviours of trivalent rare earth elements (La, Ce, Ho and Yb) by two types of humic acids were investigated under a specified set of conditions. Humic acids show quite different complexation capacities an conditional formation constants with the REEs. Apparently there are two types of binding sites in the functional groups of humic acid, in which the first binding sites have stronger ability than the second. Cerium shows the largest complexation capacities and highest formation constants among the four REEs with two humic acids, this anomaly may be relative to the distribution pattern of the REEs in seawater. The experimental results were comparable to the values of other metals reported and provided the basic data for environmental geochemistry of rare earth elements.展开更多
The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently.While most studies work with one dimensional output with disjun...The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently.While most studies work with one dimensional output with disjunct behaviour categories,more accurate prediction can still be achieved by including complex movements and enriching the sensor algorithm to detect multi-dimensional movements,i.e.,more than one movement occurring simultaneously.This paper presents such a machine-learning method for analysing overlapping independent movements.The output of the method consists of automatically recognized complex behaviour patterns that can be used for measuring animal welfare,predicting calving,or detecting early signs of diseases.This study combines automated motion sensors(ie.,halter and pedometer)for ruminants known as RumiwWatch mounted on a Charolais fattening bull and camera observation.Fourteen types of complex movements were identified,ie.,defecating-urinating,eating,drinking,getting up,head movement,licking,lying down,lying,playingaggression,rubbing,ruminating,sleeping,standing,and stepping.As multiple parallel binary classificators were used,the system was able to recognize parallel behavioural patterns with high fidelity.Two types of machine learning,i.e.,Support Vector Classification(SvC)and RandomForest were used to recognize different general and non-general forms of movement.Results from these two supervised learning systems were compared.A continuous forty-eight hours of video were annotated to train the systems and validate their predictions.The successrate of both classifiers in recognizing special movements from both sensors or separately in different settings(i.e.,window and padding)was examined.Although the two classifiers produced different results,the ideal settings showed that all forms of movement in the subject animal were successfully recognized with high accuracy.More studies using more individual animals and different ruminants would increase our knowledge on enhancing the system's performance and accuracy.展开更多
文摘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.
文摘Applications of a constitutive framework providing compound complexity analysis and indexing of coarse-grained self-similar time series representing behavioural data are presented. A notion of behavioural entropy and hysteresis is introduced as two different forms of compound measures. These measures provide clinically applicable complexity analysis of behavioural patterns yielding scalar characterisation of time-varying behaviours registered over an extended period of time. The behavioural data are obtained using body attached sensors providing non-invasive readings of heart rate, skin blood perfusion, blood oxygenation, skin temperature, movement and steps frequency. The results using compound measures of behavioural patterns of fifteen healthy individuals are presented. The application of the compound measures is shown to correlate with complexity analysis. The correlation is demonstrated using two healthy subjects compared against a control group. This indicates a possibility to use these measures in place of fractional dimensions to provide a finer characterisation of behavioural patterns observed using sensory data acquired over a long period of time.
文摘The complexation behaviours of trivalent rare earth elements (La, Ce, Ho and Yb) by two types of humic acids were investigated under a specified set of conditions. Humic acids show quite different complexation capacities an conditional formation constants with the REEs. Apparently there are two types of binding sites in the functional groups of humic acid, in which the first binding sites have stronger ability than the second. Cerium shows the largest complexation capacities and highest formation constants among the four REEs with two humic acids, this anomaly may be relative to the distribution pattern of the REEs in seawater. The experimental results were comparable to the values of other metals reported and provided the basic data for environmental geochemistry of rare earth elements.
基金supported by the Hungarian National Rural Network (Magyar Nemzeti Videki Halozat-MNVH):www.videkihalozat.eu,grant number [VP-20.2.-16-2016-0001].
文摘The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently.While most studies work with one dimensional output with disjunct behaviour categories,more accurate prediction can still be achieved by including complex movements and enriching the sensor algorithm to detect multi-dimensional movements,i.e.,more than one movement occurring simultaneously.This paper presents such a machine-learning method for analysing overlapping independent movements.The output of the method consists of automatically recognized complex behaviour patterns that can be used for measuring animal welfare,predicting calving,or detecting early signs of diseases.This study combines automated motion sensors(ie.,halter and pedometer)for ruminants known as RumiwWatch mounted on a Charolais fattening bull and camera observation.Fourteen types of complex movements were identified,ie.,defecating-urinating,eating,drinking,getting up,head movement,licking,lying down,lying,playingaggression,rubbing,ruminating,sleeping,standing,and stepping.As multiple parallel binary classificators were used,the system was able to recognize parallel behavioural patterns with high fidelity.Two types of machine learning,i.e.,Support Vector Classification(SvC)and RandomForest were used to recognize different general and non-general forms of movement.Results from these two supervised learning systems were compared.A continuous forty-eight hours of video were annotated to train the systems and validate their predictions.The successrate of both classifiers in recognizing special movements from both sensors or separately in different settings(i.e.,window and padding)was examined.Although the two classifiers produced different results,the ideal settings showed that all forms of movement in the subject animal were successfully recognized with high accuracy.More studies using more individual animals and different ruminants would increase our knowledge on enhancing the system's performance and accuracy.
