Ecological eHealth Platforms
Hospitals and Nursing Homes Ward Reduced Stressors Designs
Objective indexing of neruro-pathologies
Our approach is based on the assumption, confirmed by our prior research, that indexing of behavioral “micro-patterns” provides objective and quantitative measurements of diseases as
well as response to drugs and other treatments. The concept itself stems from and relies on mathematical frameworks yielding macroscopic characterizations of physical systems based on their
microscopic properties. This type of coarse-graining approach has been used many times in fluids or smart materials with remarkable success throughout the past century.
We adopt an approach in which the coarse-graining is provided by macroscopic surrogate behavioral patterns in place of the direct EEG of fMRI scanning of brain activities.
The microscopic level is represented by tiny changes in physiological readings that we map onto the Hurst index, equivalently onto the Hausdorff-Besicovitch dimension, that can be thought of as a macroscopic system parameter. In analogy, this macroscopic system parameter would correspond to the diffusion parameter in fluids. There are fundamental differences though compared to fluids. While the Brownian motion and its macroscopic model given by diffusion is driven by a single mechanism, namely the molecular collisions, giving raise to, e.g., free-path or cardinality, the behavioral complexity indices are macroscopic pronunciations due to a variety of actions sustained by different neural subnets driven by both internal and external parameters. There are similarities, too. The indices we introduce to objectively measure various physiological effects can be thought of as being subsumable and describable through one single measure. Similar to, say, the entropy or temperature. Consider temperature. Temperature is a macroscopic statistical quantifier and so are the indices we consider. Temperature itself cannot indicate a specific disease or etiology, but it is routinely used as a forefront diagnostic tool to judge physiological states. The indices we introduce are also macroscopic statistical quantities that can be thought of a generalization of the temperature analogy to gauge physiological states.
Athletes' performance, improvement and health risk indexing
We introduce an approach to monitor athletic training patterns using Neural Networks, a part of the Deep Learning and Artificial Intelligence frameworks, applied to subjective yet consistent monitoring, in order to optimize training effectiveness, and to preserve health.
One of the most common problems within a suboptimally designed training program is the occurrence of the overtraining syndrome. The definition of this syndrome is currently accepted by both the European College of Sport Science and the American College of Sports Medicine. The current recommendations concerning training monitoring and early detection of the overtraining syndrome advise to carry out psychological monitoring using validated questionnaires (such as the Profile of Mood State (POMS)), a monitoring of the training load perceived by the athlete (e.g. session rating of perceived exertion (RPE), a health problem monitoring tool, and a performance monitoring tool. The blood parameters and hormonal follow-up is not advised because of cost and feasability reasons, and also, because the current scientific literature has not reached consensus about the usefulness of specific biomarkers. In order to achieve such type of training monitoring, the development of easy-to-use electronic tools allows the collection of athlete’s data in a standarised manner.
Our approach allows for identification of recovery and training parameters patterns among young swimmers which are linked with performance improvement or otherwise. We acheive these goals by considering six different combinations of different subjective data that are projected on three dimensional Euclidian space. We select one of the three reading to serve a role of binary parameter by measuring a particular datum distance from the selected data mean. We then use Neural Networks based analysis to construct separation hyperlanes. These planes indicate two predictive distinct areas of performance.
We use our propritery Geometric Activity Performance Index (GAPI) and Spectral Performance Ranking (SPR). These indices provide significant and both quantitative and qualitative inside into the performance and improvements assessments.
Objective indexing of stress
The Human Stress is a system responce to various perceived external and/or internal stimuli. The initial response is governed by the sympathetic nervous system while the return to system equilibrium might be controlled by a convex feedback loop provided by homestasis or non-convex control due to time-local energy expenditure yielding allometric feedback responce.
The reason to consider the allometry is that it correlates amount and availability of resources in fight-or-flight responce. More importantly allometric feedback implies self-similarity of the underlying microscopic behavioural time-series. The self-similarity has a fundamental role in the presented approach. The more complex nonlinear control mechanisms can be characterised by existence of the allometric power law. The existence of multiple stable behavioural states, including both health and diseased states, is not anticipated by homeostatic linear descriptions of behavioural control mechanisms. Multiple stable states are, however, predicted by allometric based models in which the resistance to perturbation derives from feedback loop interactions among controlling subnetworks, that is just an another pronunciation of self-similarity.
From the phenomenological prospective, we view human stress as a state when either external or internal demands exceed the regulatory capabilities of the autonomous part of the Central Nervous System. We use our proprietary mathematical constitutive model that can be used in clinical settings to objectively quantify a level of stress a human subject would be experiencing.
We rely on body-attached sensors recording certain behavioral surrogate data. In other words, we compute an objective measure of stress severity using a combination of sensing, posteriorly mathematical analysis, and constitutive assessments to arrive at both the Compound Spectral Stress Index and Geometric Stress Index.
Analysis of stressors
The hospitalisation-associated disability might be due to several reasons, e.g. immobilisation, reduced activity, stress, poor sleep, nutritional problems, medication, etc. Of course, the prehospitalisation functional capacity reserve is also important: a patient with a larger reserve can decrease more in his function before becoming disabled. Older adults may lose about one kilogram of lean muscle mass per ten day of bed rest. Muscle protein catabolism is increased by muscular inactivity (e.g. immobilisation, bed-rest, increased bed-time) and the stress response with an increase in counterregulatory hormone concentrations. Inflammation is another factor associated with muscle weakness and also nutrition plays a role during hospitalisation.
There is an urgent need for health care owing to a high number of hospitalized older patients presenting several symptoms of frailty and other geriatric syndromes to early monitor hospitalized adverse events. Some studies have observed a strong relationship between cognitive and physical decline, as manifested by an elevated number of hospital-related falls. Above all, hospitalization itself should be considered as an important delirium and frailty risk factor. Interdisciplinary preventive care strategies focusing on limiting physical and cognitive decline during hospitalization by early monitoring using technology may be effective in averting delirium and frailty and other poor posthospitalization outcomes. Health care reforms focusing on building more effective and efficient care models for older inpatients, integrating newly developed ehealth devices, are urgently needed to develop “senior-friendly” hospitals, including specialized geriatric care units.
Unique identification of humans using behavioural complexity patterns
We provide an analytic technology using complexity of behavioural variables to identify human subjects. The identification is made by projection of self-similar and normally distributed behavioural data onto Euclidian product spaces based on a non-disruptive sensing. We introduce platform combining data acquisitions using body attached sensing devices, mathematical analysis of complexity of the acquired signals, and subsequent projections onto Euclidian spaces representing behavioural states.
Externally applied wearable sensors al-low for collecting self-similar time-series of behavioural or physiological parame-ters such as heart rate, blood oxygena-tion, skin temperature, steps frequency, and others. The measurements of the behavioural parameters represent surro-gate data that characterize patterns through segmentation and a subsequent complexity analysis using data pertaining to expended time spans. Behavioural surrogate data are segmented and analysed to extract meaningful patterns hidden in the measured time se-ries of behavioural data. The word segmentation is used to indicate a connection between behavioural temporal segmentation and image spatial segmentation. This approach is similar to the concepts of the Gestalt school and psycho-physiologists analysing perceptions of images in the retina in the first instants of their arrival. Segmentation of captured images is aimed at and allows for recognition of well-defined objects from discrete patterns.
We use complexity indexing to recognize well-defined behaviours that can differentiate various subjects using time-discrete behavioural time-series. We build geometrical images of behavioural states evolving over time that can be measured and analysed using mathematical tools based on complexity indexing. Our approach shares some fundamental underpinnings with image segmentations, in particular the use of a measure generating dimension.
Cassiopee Computational Ecosystem
CASSIOPEE is unique and the worlds first complete analytical diagnosis tool providing complexity analysis of behavioral and physiological data. It detects scale invariance and it provides a measure of the long range dependence of the behaviors and and the underlying micro-patterns as well as other physiological recordings.
The complexity analysis of the behavioral and physiological data is the key objective measurements of effects of drugs, detection and/or analysis of progression of a disease and, consequently, an objective evaluation of applicability of a particular drug for treatment.The CASSIOPEE system is fully compatible with number of behavioral and physiological data acquisition systems producing space-time and physiological recordings originating from either an animal or a human in customizable form of a multi-dimensional data file.
Technically speaking, the CASSIOPEE system provides analysis of cardinality, free-path, departing angles and the overall locomotion in addition to physiological readings exhibiting scale-invariant behavior. The system provides estimate of the minimum amount of recordings and their stroboscopic resolution needed to deliver reliable analysis.
Principles of complexity analysis
Characterization of behavioral patterns and in particular the locomotion and its micro-patterns using either the non-integer Hausdorff-Besicovitch dimension or the Hurst exponent pertain to the analysis of the complexity. The higher these two measures are the less complex are the behavioral patterns and vise versa. There are two underlying assumptions that have to be valid to apply our approach: (1) the data representing both the locomotion itself and its micro-patterns have to be stochastic processes themselves exhibiting self-similarity, (2) and that such patterns can be modeled by fractional Brownian motions/functions.Technically, the first of the requirements is verified by computing the joint probability density function that have to be nearly identical over a range of the coarse-grained discrete recordings. The identification of the self-similarity scaling index, the Hurst exponent, is obtained using maximum likelihood estimate of the process identification procedure.Our overall approach is based on the overhelming scientiic evidence that indexing of behavioral micro-patterns provides objective and quantitative measurements of drugs, treatments or injury pronunciations. The basic scheme is shown below. It is important to realize that the direction of deviation from the Base Line indices in either of the two directions shown must be interpreted with respect to the expected outcome of a drug application, treatment or injury type.
During his carrier, Petr Kloucek has been active in the field of applied, computational and numerical mathematics, materials science and biomedical research. His current active focuses on indexing of sports, constitutive models of
neuro-pathologies, and stressors. He produced over eighty refereed publications as the main author. He obtained three awards for his scientific work.
He brought up four Ph.D. students and he has been working with and supporting two post-doctoral associates.
Three of his doctoral students and one of his post-doctoral associate hold professorial appointments in various Universities in the United States.
Petr Kloucek has been working for over ten years in the area of “Smart Materials”, such as alloys, bio-active composites or composite ultra thin films. The underlying emphasis on these materials resides with their ability to change one form of energy into another by means of a phase transition on or below 500 nano-meter scale. He has been on the forefront of developing mathematics and mathematical tools and their application in active development of the design and analysis of “smart-nano-devices”. He has an extensive record of working with various industries and national agencies working on problems with an immediate impact on the current state of advanced technologies. Some of the most advanced applications include targeted drug deliviries for cancer tratments of glioblastoma and pancreatic cancer.
He developed a feedback control software for ALCAN to control the production of bubbles during the aluminum electrolysis increasing the production efficiency by about 2%. He also codeveloped the second generation of the RCM code for NASA modeling the oscillations of the Earth magnetosphere. The code is use during the launches of space vehicles to compute the optimal trajectory avoiding strong magnetic gradients. He also participated in the design the third protection layer of the NASA’s ISS based on the NiTi/Kevlar woven. In addition he developed optimization micro-fluidic based software for the production of the multi-phase flow focusing devices producing sub-micron encapsulation of nano-particles for DeBioPharm, SA., Switzerland.
Recently, he co-founded CAMPsyN, the Center for Clinically Applicable Mathematical Psychiatric Neuroscience. The center is focused on design and implementations of objective measures of neuro-pathologies using high-level mathematical tools including Measure theory, Fractional apperent diffusion, Artificial Inteligenece, and Deep learning frameworks. The commercial applications of the CAMPsyN research are provided by Cassiopee Applied Solutions, Sarl.
Investment executive with more than 25 years of experience and direct transferable skills in, e.g., Managing businesses on a both tactical and operational level with full financial responsibility, Developing and structuring products and services, with reliable and recurrent results, Conducting business across cultures through an open and honest management style, Encouragimg client appreciation through effective relationship management
Jorgen has been active in various areas.
Providing HNWI's and family owned companies with advisory services on business and investment strategies, optimal usage of assets, risk management, deal structuring and funding related issues.
Pursued through longer stays in Hong Kong and Singapore to develop and sell family office services in the region mainly focusing on Mainland Chinese.
He co-founded Pulse Capital Management, a quantitative driven sector specific hedge fund, concentrating on listed private equity.
A product creation for HNWI’s to simplify and focus their investment approach into Private Equity. This method produced annual returns of 28.9% in the above‐mentioned time period.
Jorgen headed an bequity group, which managed a large proportion of a multi-billion dollar investment portfolio, for the investment company of large privately owned Scandinavian multinational. Structured and employed an investment style, which focused on global sectors allocation through innovative usage of sector ETF’s and quantitative supported stock picking. Performance in line with benchmark. Increased the diversification of external managed mandates through extended exposure to alternative investments, via dedicated Hedge and Private Equity funds.
As the Chief European Investment Officer he directed and controlled the European equity operation, consisting of 15 European portfolio managers and a support team in Germany as well as the group's convertible bond team in Paris. Restructured and enlarged the team. Instigated a focused and systematic investment process. Participated at the executive level in directing the group's aspiration for building a Global Asset Management operation.
Jorgen served as head of International Equities that is institutionalized and expanded the international equity business with a 7 person investment team. He directed and supervised the implementation of one of the first pan‐European sector approaches in Germany. Based on quantitative modeling, combining active sector allocation and stock picking. The process was launched with a € 25 million funding. Assets allocation to this activity reached at its peek more than € 2bn. Ranked 3rd by Russell's in 1999 among 43 different European competitors. Advised a range of domestic institutions on the investments in Global equities.