Development of an autonomous framework for emotion recognition

An approach of development of an autonomous emotion re-cognition system for creating of an intelligent e-health care environment is described. The process of emotion recognition is based on measurements of very small physiological signals taken from electrodes noninvasively attached on human body. The amplified ECG, EDA, and human’s body temperature si-gnals are used in the model for emotion recognition. The ECG, EDA, and human’s body temperature data acquisition module is designed based on usage of AD620 type instrumentation ampli-fier and ATmega16 microcontroller. To realizing actual emotion recognition process, the following data pre-processing steps are described in this paper: the phases of amplifying, discrimina-tion, filtering, recording, and storing into database of the sys-tem. The steps of development of recently proposed of new experimental environment for digital data acquisition and repre-sentation, the multi-chanel oscilloscope called as Atmega Osci-lografas are described in this paper. The original 10 bit data transferring algorithm has been discussed based on incremental usage of 8 bit data for effectively realizing of USART protocol of Atmel microcontrollers

Daugelio agentų paskatos mokytis komponentais grindžiamas aplinkos apšvietimo valdiklis

The paper presents a vision of sustainable eco-social laboratory, the ESLab which might be used to speed up the process of development of the recently proposed by authors of the Smart Eco-Social Apartment. It is presented the multi-agent model of the ambient comfort measurement and environment control system to be used for the development of the ESLab. The human Ambient Lighting Affect Reward index, the ALAR index is proposed at the first time used for development of the Reinforcement Learning Based Ambient Comfort Controller, the RLBACC for the ESLab. The ALAR index is dependent on human physiological parameters: the temperature, the ECG- electrocardiogram and the EDA-electro-dermal activity. The fuzzy logic is used to approximate the ALAR index function by defining two fuzzy inference systems: the Arousal-Valence System, and the Ambient Lighting Affect Reward (ALAR) System. The goal of the RLBACC is to find such the environmental state characteristics that create an optimal comfort for people affected by this environment. The Radial Basis Neural Network is used as the main component of the RLBACC to performing of two roles - the policy structure, known as the Actor, used to select actions, and the estimated value function, known as the Critic that criticizes the actions made by the Actor. The Critic in this paper was used as a value function approximation of the continuous learning tasks of the RLBACC

Cooperative context data acquisition and dissemination for situation identification in vehicular communication networks

One of the most interesting recent topics in pervasive computing is the smart adaptive service support systems. These systems are impossible without knowing the context with the entity at the moment, what context was in the past and what is possible in the near future. The context alone is not so important in human oriented services provided in connected vehicles. In this environment we need to know not only the exact context but also the higher level information—situation. The situation awareness can be increased by exchanging it to other participating nodes—vehicles. Because the vehicular communication network is a very dynamic environment, it is essential to adequately respond to the user’s needs and to provide all the needed services in the right place at the right moment and in the right way. In this work we present our developed cooperative context data acquisition and dissemination model for situation identification in vehicular communication networks. Our solution is different from others as it uses additional virtual context information source—information from other vehicles is weighted and exchanged using the utility function. The proposed decision support system decides if the message should be transmitted to other vehicles, sent to the cloud, saved locally or dismissed. The simulation results show the promising context exchange rate between vehicles and huge saving on channel utilization.Web of Science851624

2010),The Reinforcement Framework of a Decision Support System for the Localization and Monitoring

This paper analyses the possibilities of the integration of different technological and knowledge representation techniques for the development of reinforcement frameworks for the remote control of multiple agents such as wheelchair-type robots. Some technological solutions are discussed regarding the recognition of localization of moving objects by using mobile technologies. Large-scale multi-dimensional recognitions of emotional diagnoses of disabled persons often generate large amounts of multidimensional data with complex recognition mechanisms, based on the integration of different knowledge representation techniques and complex inference models. The problem is to reveal the main components of a diagnosis as well as to construct flexible decision making models. Sensors can help to record primary data for monitoring objects; however the recognition of abnormal situations, the clustering of emotional stages and resolutions for certain types of diagnoses is an oncoming issue for bio-robot constructors. The prediction criteria of the diagnosis of the emotional situation of disabled persons are described using knowledge based models of neural networks. The research results present the development of a multi-layered framework architecture with the integration of artificial agents and support components for diagnosis recognition and control, or further actions, by using mobile technologies. The method of fuzzy neural network control of the speed of wheelchair-type robots working in real time by providing movement support for disabled individuals is presented. The fuzzy reasoning by using fuzzy logical Petri nets is described in order to define the physiological state of disabled individuals through recognizing their emotions during their different activities. Some new possibilities of the recognition of moving object location are introduced in the system

Advanced Approach of Multiagent Based Buoy Communication

Usually, a hydrometeorological information system is faced with great data flows, but the data levels are often excessive, depending on the observed region of the water. The paper presents advanced buoy communication technologies based on multiagent interaction and data exchange between several monitoring system nodes. The proposed management of buoy communication is based on a clustering algorithm, which enables the performance of the hydrometeorological information system to be enhanced. The experiment is based on the design and analysis of the inexpensive but reliable Baltic Sea autonomous monitoring network (buoys), which would be able to continuously monitor and collect temperature, waviness, and other required data. The proposed approach of multiagent based buoy communication enables all the data from the costal-based station to be monitored with limited transition speed by setting different tasks for the agent-based buoy system according to the clustering information