Dietmar Bruckner - Academia.edu (original) (raw)
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Papers by Dietmar Bruckner
IFAC Proceedings Volumes, 2009
A method for learning models of a person's behavior in a building is described. The person's move... more A method for learning models of a person's behavior in a building is described. The person's movements and some activities are detected by sensors. From the sensor values a Hidden Markov Model (HMM) is learned. Once a model is built, it allows calculating of predictions of the person's behavior with respect to incoming sensor values. A method for learning HMM structures from sample data is described.
IFIP Advances in Information and Communication Technology, 2011
We propose a novel way for ambient assisted living: a system that with motion detector to observe... more We propose a novel way for ambient assisted living: a system that with motion detector to observe the daily activities of the elderly, build the daily activity model of the user. In case of unusual activities the system send alarm signal to caregiver. The problems with this approach to build such a model: firstly, the activities of the user are random and dynamic distributed, that means the related data is dynamically and with huge count. Secondly, the difficulty and computational burden to get character parameters of hidden Markov model with many "states". To deal with the first problem we take advantage of an easy filter algorithm and translate the huge dynamical data to "state" data. Secondly according the limited output of distinct observation symbols per state, we reduced the work to research the observation symbol probability distribution. Furthermore the forward algorithm used to calculate the probability of observed sequence according the build model.
2010 IEEE International Symposium on Industrial Electronics, 2010
2010 International Conference on Computer Application and System Modeling (ICCASM 2010), 2010
To observe and analyze person's daily activities, and build the activities model is an impor... more To observe and analyze person's daily activities, and build the activities model is an important task in an intelligent environment. In an Ambient Assisted Living (AAL) project we get sensor data from a motion detector. At first we translate and reduce the raw data to state data. Secondly using hidden Markov model, forward algorithm, and Viterbi Algorithm to analyze the data and build the person's daily activity model. Comparing individual observation with the build model to find out best and worst (abnormal) activities.
3rd International Conference on Human System Interaction, 2010
2009 2nd Conference on Human System Interactions, 2009
Lecture Notes in Electrical Engineering, 2011
2009 IEEE Conference on Emerging Technologies & Factory Automation, 2009
2009 35th Annual Conference of IEEE Industrial Electronics, 2009
Today, building automation is advancing from simple monitoring and control tasks of lightning and... more Today, building automation is advancing from simple monitoring and control tasks of lightning and heating towards more and more complex applications that require a dynamic perception and interpretation of different scenes occurring in a building. Current approaches cannot handle these newly upcoming demands. In this article, a bionically inspired approach for multimodal, dynamic scene perception and interpretation is presented, which is based on neuroscientific and neuro-psychological research findings about the perceptual system of the human brain. This approach bases on data from diverse sensory modalities being processed in a so-called neuro-symbolic network. With its parallel structure and with its basic elements being information processing and storing units at the same time, a very efficient method for scene perception is provided overcoming the problems and bottlenecks of classical dynamic scene interpretation systems.
IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, 2012
ABSTRACT In this paper, we propose a human daily activity recognition method that is used for Amb... more ABSTRACT In this paper, we propose a human daily activity recognition method that is used for Ambient Assisted Living. The proposed system is able to learn a user's activities using the data from motion and door sensors. We extract low level features from the sensor data and feed the features to a model that combines support vector machines (SVMs) and conditional random fields (CRFs) to give accurate recognition results. We propose to combine SVM and CRF classifiers in a hierarchical model which results in better accuracies and can also make use of high level features. We conducted experiments and presented the effectiveness and accuracies of the proposed method.
Today, building automation is advancing from simple monitoring and control tasks of lightning and... more Today, building automation is advancing from simple monitoring and control tasks of lightning and heating towards more and more complex applications that require a dynamic perception and interpretation of different scenes occurring in a building. Current approaches cannot handle these newly upcoming demands. In this article, a bionically inspired approach for multimodal, dynamic scene perception and interpretation is presented, which is based on neuroscientific and neuro-psychological research findings about the perceptual system of the human brain. This approach bases on data from diverse sensory modalities being processed in a so-called neuro-symbolic network. With its parallel structure and with its basic elements being information processing and storing units at the same time, a very efficient method for scene perception is provided overcoming the problems and bottlenecks of classical dynamic scene interpretation systems.
2007 2nd Bio-Inspired Models of Network, Information and Computing Systems, 2007
IFAC Proceedings Volumes, 2009
A method for learning models of a person's behavior in a building is described. The person's move... more A method for learning models of a person's behavior in a building is described. The person's movements and some activities are detected by sensors. From the sensor values a Hidden Markov Model (HMM) is learned. Once a model is built, it allows calculating of predictions of the person's behavior with respect to incoming sensor values. A method for learning HMM structures from sample data is described.
IFIP Advances in Information and Communication Technology, 2011
We propose a novel way for ambient assisted living: a system that with motion detector to observe... more We propose a novel way for ambient assisted living: a system that with motion detector to observe the daily activities of the elderly, build the daily activity model of the user. In case of unusual activities the system send alarm signal to caregiver. The problems with this approach to build such a model: firstly, the activities of the user are random and dynamic distributed, that means the related data is dynamically and with huge count. Secondly, the difficulty and computational burden to get character parameters of hidden Markov model with many "states". To deal with the first problem we take advantage of an easy filter algorithm and translate the huge dynamical data to "state" data. Secondly according the limited output of distinct observation symbols per state, we reduced the work to research the observation symbol probability distribution. Furthermore the forward algorithm used to calculate the probability of observed sequence according the build model.
2010 IEEE International Symposium on Industrial Electronics, 2010
2010 International Conference on Computer Application and System Modeling (ICCASM 2010), 2010
To observe and analyze person's daily activities, and build the activities model is an impor... more To observe and analyze person's daily activities, and build the activities model is an important task in an intelligent environment. In an Ambient Assisted Living (AAL) project we get sensor data from a motion detector. At first we translate and reduce the raw data to state data. Secondly using hidden Markov model, forward algorithm, and Viterbi Algorithm to analyze the data and build the person's daily activity model. Comparing individual observation with the build model to find out best and worst (abnormal) activities.
3rd International Conference on Human System Interaction, 2010
2009 2nd Conference on Human System Interactions, 2009
Lecture Notes in Electrical Engineering, 2011
2009 IEEE Conference on Emerging Technologies & Factory Automation, 2009
2009 35th Annual Conference of IEEE Industrial Electronics, 2009
Today, building automation is advancing from simple monitoring and control tasks of lightning and... more Today, building automation is advancing from simple monitoring and control tasks of lightning and heating towards more and more complex applications that require a dynamic perception and interpretation of different scenes occurring in a building. Current approaches cannot handle these newly upcoming demands. In this article, a bionically inspired approach for multimodal, dynamic scene perception and interpretation is presented, which is based on neuroscientific and neuro-psychological research findings about the perceptual system of the human brain. This approach bases on data from diverse sensory modalities being processed in a so-called neuro-symbolic network. With its parallel structure and with its basic elements being information processing and storing units at the same time, a very efficient method for scene perception is provided overcoming the problems and bottlenecks of classical dynamic scene interpretation systems.
IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, 2012
ABSTRACT In this paper, we propose a human daily activity recognition method that is used for Amb... more ABSTRACT In this paper, we propose a human daily activity recognition method that is used for Ambient Assisted Living. The proposed system is able to learn a user's activities using the data from motion and door sensors. We extract low level features from the sensor data and feed the features to a model that combines support vector machines (SVMs) and conditional random fields (CRFs) to give accurate recognition results. We propose to combine SVM and CRF classifiers in a hierarchical model which results in better accuracies and can also make use of high level features. We conducted experiments and presented the effectiveness and accuracies of the proposed method.
Today, building automation is advancing from simple monitoring and control tasks of lightning and... more Today, building automation is advancing from simple monitoring and control tasks of lightning and heating towards more and more complex applications that require a dynamic perception and interpretation of different scenes occurring in a building. Current approaches cannot handle these newly upcoming demands. In this article, a bionically inspired approach for multimodal, dynamic scene perception and interpretation is presented, which is based on neuroscientific and neuro-psychological research findings about the perceptual system of the human brain. This approach bases on data from diverse sensory modalities being processed in a so-called neuro-symbolic network. With its parallel structure and with its basic elements being information processing and storing units at the same time, a very efficient method for scene perception is provided overcoming the problems and bottlenecks of classical dynamic scene interpretation systems.
2007 2nd Bio-Inspired Models of Network, Information and Computing Systems, 2007