Human activity monitoring by local and global finite state machines

There are a number of solutions to automate the monotonous task of looking at a monitor to find suspicious behaviors in video surveillance scenarios. Detecting strange objects and intruders, or tracking people and objects, is essential for surveillance and safety in crowded environments. The present work deals with the idea of jointly modeling simple and complex behaviors to report local and global human activities in natural scenes. Modeling human activities with state machines is still common in our days and is the approach offered in this paper. We incorporate knowledge about the problem domain into an expected structure of the activity model. Motion-based image features are linked explicitly to a symbolic notion of hierarchical activity through several layers of more abstract activity descriptions. Atomic actions are detected at a low level and fed to hand-crafted grammars to detect activity patterns of interest. Also, we work with shape and trajectory to indicate the events related to moving objects. In order to validate our proposal we have performed several tests with some CAVIAR test cases

Agent behavior monitoring using optimal action selection and twin gaussian processes

The increasing trend towards delegating complex tasks to autonomous artificial agents in safety-critical socio-technical systems makes agent behavior monitoring of paramount importance. In this work, a probabilistic approach for on-line monitoring using optimal action selection and twin Gaussian processes (TGP) is proposed. A Kullback-Leibler (KL) based metric is proposed to characterize the deviation of an agent behavior (modeled as a controlled stochastic process) to its specification. The optimal behavior specification is obtained using Linearly Solvable Markov Decision Processes (LSMDP) whereby the Bellman equation is made linear through an exponential transformation such that the optimal control policy is obtained in an explicit form.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Agent behavior monitoring using optimal action selection and twin gaussian processes

The increasing trend towards delegating complex tasks to autonomous artificial agents in safety-critical socio-technical systems makes agent behavior monitoring of paramount importance. In this work, a probabilistic approach for on-line monitoring using optimal action selection and twin Gaussian processes (TGP) is proposed. A Kullback-Leibler (KL) based metric is proposed to characterize the deviation of an agent behavior (modeled as a controlled stochastic process) to its specification. The optimal behavior specification is obtained using Linearly Solvable Markov Decision Processes (LSMDP) whereby the Bellman equation is made linear through an exponential transformation such that the optimal control policy is obtained in an explicit form.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Agent behavior monitoring using optimal action selection and twin gaussian processes

The increasing trend towards delegating complex tasks to autonomous artificial agents in safety-critical socio-technical systems makes agent behavior monitoring of paramount importance. In this work, a probabilistic approach for on-line monitoring using optimal action selection and twin Gaussian processes (TGP) is proposed. A Kullback-Leibler (KL) based metric is proposed to characterize the deviation of an agent behavior (modeled as a controlled stochastic process) to its specification. The optimal behavior specification is obtained using Linearly Solvable Markov Decision Processes (LSMDP) whereby the Bellman equation is made linear through an exponential transformation such that the optimal control policy is obtained in an explicit form.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Influence of Tempo and Rhythmic Unit in Musical Emotion Regulation

This article is based on the assumption of musical power to change the listener's mood. The paper studies the outcome of two experiments on the regulation of emotional states in a series of participants who listen to different auditions. The present research focuses on note value, an important musical cue related to rhythm. The influence of two concepts linked to note value is analyzed separately and discussed together. The two musical cues under investigation are tempo and rhythmic unit. The participants are asked to label music fragments by using opposite meaningful words belonging to four semantic scales, namely “Tension” (ranging from Relaxing to Stressing), “Expressiveness” (Expressionless to Expressive), “Amusement” (Boring to Amusing) and “Attractiveness” (Pleasant to Unpleasant). The participants also have to indicate how much they feel certain basic emotions while listening to each music excerpt. The rated emotions are “Happiness,” “Surprise,” and “Sadness.” This study makes it possible to draw some interesting conclusions about the associations between note value and emotions

Combining Users' Activity Survey and Simulators to Evaluate Human Activity Recognition Systems

Open Access articleEvaluating human activity recognition systems usually implies following expensive and time-consuming methodologies,where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a syntheticdatasetgeneratedfollowingtheproposedmethodologyiscomparedtoarealdataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant

Human Activity Recognition System Based-on Sequential Logic Circuits and Statistical Models

this research proposed the human activityrecognition system that described complete flow of processes fromlowest process (dealing with images) to highest process (recognizehuman activity). We proposed human action recognition thatmanage image sequence then recognize human action with simplehuman model by model-based recognition technique. Theexperimental result shows good accuracy which up to 93%correctly recognized. We proposed the human activity processwith 3 methods that consecutive improved. All of those methodscan use the result of action recognition as inputs. First method isFSM recognizer. The human model in Finite State Machine (FSM)recognizer can be modeled by rational condition that make it easyto understand and consume low computation cost but it hard todefine complex activity condition so it is unsuitable method forcomplex activity. The second recognizer applied Hidden MarkovModel (HMM) for activity modeling. The HMM recognizer candealing with much more complex activity and give fair recognitionrate. However, HMM recognizer is not involve feature prioritythat should has effect to accuracy so we proposed the thirdrecognizer that used graph similarity measurement for activitymodeling and activity classification. The third one, GraphSimilarity Measurement (GSM) recognizer involved featurepriority for recognition method then show better result thanHMM in most measurement. GSM recognizer has ~84% accuracyin average. FSM recognizer is suitable for simple activity with lowcomputation cost while HMM is suitable for much more complexactivity and use single feature for recognition process. However,HMM method may not give best result for the activity that usemultiple features. GSM is also suitable for complex activity and,furthermore, give better result than HMM for the activity thattrained from multiple features

A caregiver support platform within the scope of an ambient assisted living ecosystem

The Ambient Assisted Living (AAL) area is in constant evolution, providing new technologies to users and enhancing the level of security and comfort that is ensured by house platforms. The Ambient Assisted Living for All (AAL4ALL) project aims to develop a new AAL concept, supported on a unified ecosystem and certification process that enables a heterogeneous environment. The concepts of Intelligent Environments, Ambient Intelligence, and the foundations of the Ambient Assisted Living are all presented in the framework of this project. In this work, we consider a specific platform developed in the scope of AAL4ALL, called UserAccess. The architecture of the platform and its role within the overall AAL4ALL concept, the implementation of the platform, and the available interfaces are presented. In addition, its feasibility is validated through a series of tests.Project “AAL4ALL”, co-financed by the European Community Fund FEDER, through COMPETE—Programa Operacional Factores de Competitividade (POFC). Foundation for Science and Technology (FCT), Lisbon, Portugal, through Project PEst-C/CTM/LA0025/2013. Project CAMCoF—Context-Aware Multimodal Communication Framework funded by ERDF—European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT—Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980. This work is part-funded by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/201

Generation of human computational models with machine learning

Services in smart environments pursue to increase the quality of people?s lives. The most important issues when developing this kind of environments is testing and validating such services. These tasks usually imply high costs and annoying or unfeasible real-world testing. In such cases, artificial societies may be used to simulate the smart environment (i.e. physical environment, equipment and humans). With this aim, the CHROMUBE methodology guides test engineers when modeling human beings. Such models reproduce behaviors which are highly similar to the real ones. Originally, these models are based on automata whose transitions are governed by random variables. Automaton?s structure and the probability distribution functions of each random variable are determined by a manual test and error process. In this paper, it is presented an alternative extension of this methodology which avoids the said manual process. It is based on learning human behavior patterns automatically from sensor data by using machine learning techniques. The presented approach has been tested on a real scenario, where this extension has given highly accurate human behavior models