The Experts below are selected from a list of 141474 Experts worldwide ranked by ideXlab platform

Roozbeh Jafari - One of the best experts on this subject based on the ideXlab platform.

  • Energy-Efficient Information-Driven Coverage for Physical Movement Monitoring in Body Sensor Networks
    IEEE Journal on Selected Areas in Communications, 2009
    Co-Authors: Hassan Ghasemzadeh, Eric Guenterberg, Roozbeh Jafari
    Abstract:

    Advances in technology have led to the development of various light-weight sensor devices that can be woven into the Physical environment of our daily lives. Such systems enable on-body and mobile health-care monitoring. Our interest particularly lies in the area of Movement-monitoring platforms that operate with inertial sensors. In this paper, we introduce the notion of compatibility graphs and describe how they can be utilized for power optimization. We first formulate an action coverage problem that will consider the sensing coverage from a collaborative signal processing perspective. Our solution is capable of eliminating redundant sensor nodes while maintaining the quality of service. The problem we outline can be transformed into an NP-hard problem. Therefore, we propose an ILP formulation to attain a lower bound on the solution and a fast greedy technique. Moreover, we present a system for dynamically activating and deactivating sensor nodes in real time. We then use our graph representation to develop an efficient formulation for maximum lifetime. This formulation provides sufficient information for finding activation duties for each sensor node. Finally, we demonstrate the effectiveness of our techniques on data collected from several subjects.

  • a phonological expression for Physical Movement monitoring in body sensor networks
    Mobile Adhoc and Sensor Systems, 2008
    Co-Authors: Hassan Ghasemzadeh, Jaime Barnes, Eric Guenterberg, Roozbeh Jafari
    Abstract:

    Monitoring human activities using wearable wireless sensor nodes has the potential to enable many useful applications for everyday situations. The deployment of a compact and computationally efficient grammatical representation of actions reduces the complexities involved in the detection and recognition of human behaviors in a distributed system. In this paper, we introduce a road map to a linguistic framework for the symbolic representation of inertial information for Physical Movement monitoring. Our method for creating phonetic descriptions consists of constructing primitives across the network and assigning certain primitives to each Movement. Our technique exploits the notion of a decision tree to identify atomic actions corresponding to every given Movement. We pose an optimization problem for the fast identification of primitives. We then prove that this problem is NP-Complete and provide a fast greedy algorithm to approximate the solution. Finally, we demonstrate the effectiveness of our phonetic model on data collected from three subjects.

  • MASS - A phonological expression for Physical Movement monitoring in body sensor networks
    2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, 2008
    Co-Authors: Hassan Ghasemzadeh, Jaime Barnes, Eric Guenterberg, Roozbeh Jafari
    Abstract:

    Monitoring human activities using wearable wireless sensor nodes has the potential to enable many useful applications for everyday situations. The deployment of a compact and computationally efficient grammatical representation of actions reduces the complexities involved in the detection and recognition of human behaviors in a distributed system. In this paper, we introduce a road map to a linguistic framework for the symbolic representation of inertial information for Physical Movement monitoring. Our method for creating phonetic descriptions consists of constructing primitives across the network and assigning certain primitives to each Movement. Our technique exploits the notion of a decision tree to identify atomic actions corresponding to every given Movement. We pose an optimization problem for the fast identification of primitives. We then prove that this problem is NP-Complete and provide a fast greedy algorithm to approximate the solution. Finally, we demonstrate the effectiveness of our phonetic model on data collected from three subjects.

  • a resource optimized Physical Movement monitoring scheme for environmental and on body sensor networks
    International Conference on Mobile Systems Applications and Services, 2007
    Co-Authors: Antti Vehkaoja, Mari Zakrzewski, Sameer Iyengar, Ruzena Bajcsy, Steven D Glaser, Shankar Sastry, Jukka Lekkala, Roozbeh Jafari
    Abstract:

    Perhaps the most significant challenge in design of on-body sensors is the wearability concern. This concern requires that the size of the nodes (sensors, processing units and batteries) is minimized. Therefore, the computation and communication executed in on-body nodes must be moderated significantly. In this paper, we propose a collaborative signal processing scheme for Physical Movement monitoring that utilizes on-body and environmental sensors. The environmental sensor nodes perform the bulk of the signal processing and provide feedback to the on-body sensor nodes. This is due to the fact that the environmental sensor nodes have access to more powerful processing units and an unlimited energy supply. The feedback simplifies the signal processing on the on-body nodes significantly. We achieve this by performing a hierarchical classification and introducing a probabilistic measure on likelihood of possible classes for the final level of classification on on-body sensor nodes. The experimental results show the effectiveness of our method. On average the classification accuracy is reduced by 3% while the computational complexity can be scaled down by one order of magnitude compared to a global and comprehensive classification scheme.

  • HealthNet - A resource optimized Physical Movement monitoring scheme for environmental and on-body sensor networks
    Proceedings of the 1st ACM SIGMOBILE international workshop on Systems and networking support for healthcare and assisted living environments - Health, 2007
    Co-Authors: Antti Vehkaoja, Mari Zakrzewski, Sameer Iyengar, Ruzena Bajcsy, Steven D Glaser, Shankar Sastry, Jukka Lekkala, Roozbeh Jafari
    Abstract:

    Perhaps the most significant challenge in design of on-body sensors is the wearability concern. This concern requires that the size of the nodes (sensors, processing units and batteries) is minimized. Therefore, the computation and communication executed in on-body nodes must be moderated significantly. In this paper, we propose a collaborative signal processing scheme for Physical Movement monitoring that utilizes on-body and environmental sensors. The environmental sensor nodes perform the bulk of the signal processing and provide feedback to the on-body sensor nodes. This is due to the fact that the environmental sensor nodes have access to more powerful processing units and an unlimited energy supply. The feedback simplifies the signal processing on the on-body nodes significantly. We achieve this by performing a hierarchical classification and introducing a probabilistic measure on likelihood of possible classes for the final level of classification on on-body sensor nodes. The experimental results show the effectiveness of our method. On average the classification accuracy is reduced by 3% while the computational complexity can be scaled down by one order of magnitude compared to a global and comprehensive classification scheme.

Antti Vehkaoja - One of the best experts on this subject based on the ideXlab platform.

  • a resource optimized Physical Movement monitoring scheme for environmental and on body sensor networks
    International Conference on Mobile Systems Applications and Services, 2007
    Co-Authors: Antti Vehkaoja, Mari Zakrzewski, Sameer Iyengar, Ruzena Bajcsy, Steven D Glaser, Shankar Sastry, Jukka Lekkala, Roozbeh Jafari
    Abstract:

    Perhaps the most significant challenge in design of on-body sensors is the wearability concern. This concern requires that the size of the nodes (sensors, processing units and batteries) is minimized. Therefore, the computation and communication executed in on-body nodes must be moderated significantly. In this paper, we propose a collaborative signal processing scheme for Physical Movement monitoring that utilizes on-body and environmental sensors. The environmental sensor nodes perform the bulk of the signal processing and provide feedback to the on-body sensor nodes. This is due to the fact that the environmental sensor nodes have access to more powerful processing units and an unlimited energy supply. The feedback simplifies the signal processing on the on-body nodes significantly. We achieve this by performing a hierarchical classification and introducing a probabilistic measure on likelihood of possible classes for the final level of classification on on-body sensor nodes. The experimental results show the effectiveness of our method. On average the classification accuracy is reduced by 3% while the computational complexity can be scaled down by one order of magnitude compared to a global and comprehensive classification scheme.

  • HealthNet - A resource optimized Physical Movement monitoring scheme for environmental and on-body sensor networks
    Proceedings of the 1st ACM SIGMOBILE international workshop on Systems and networking support for healthcare and assisted living environments - Health, 2007
    Co-Authors: Antti Vehkaoja, Mari Zakrzewski, Sameer Iyengar, Ruzena Bajcsy, Steven D Glaser, Shankar Sastry, Jukka Lekkala, Roozbeh Jafari
    Abstract:

    Perhaps the most significant challenge in design of on-body sensors is the wearability concern. This concern requires that the size of the nodes (sensors, processing units and batteries) is minimized. Therefore, the computation and communication executed in on-body nodes must be moderated significantly. In this paper, we propose a collaborative signal processing scheme for Physical Movement monitoring that utilizes on-body and environmental sensors. The environmental sensor nodes perform the bulk of the signal processing and provide feedback to the on-body sensor nodes. This is due to the fact that the environmental sensor nodes have access to more powerful processing units and an unlimited energy supply. The feedback simplifies the signal processing on the on-body nodes significantly. We achieve this by performing a hierarchical classification and introducing a probabilistic measure on likelihood of possible classes for the final level of classification on on-body sensor nodes. The experimental results show the effectiveness of our method. On average the classification accuracy is reduced by 3% while the computational complexity can be scaled down by one order of magnitude compared to a global and comprehensive classification scheme.

Hassan Ghasemzadeh - One of the best experts on this subject based on the ideXlab platform.

  • Energy-Efficient Information-Driven Coverage for Physical Movement Monitoring in Body Sensor Networks
    IEEE Journal on Selected Areas in Communications, 2009
    Co-Authors: Hassan Ghasemzadeh, Eric Guenterberg, Roozbeh Jafari
    Abstract:

    Advances in technology have led to the development of various light-weight sensor devices that can be woven into the Physical environment of our daily lives. Such systems enable on-body and mobile health-care monitoring. Our interest particularly lies in the area of Movement-monitoring platforms that operate with inertial sensors. In this paper, we introduce the notion of compatibility graphs and describe how they can be utilized for power optimization. We first formulate an action coverage problem that will consider the sensing coverage from a collaborative signal processing perspective. Our solution is capable of eliminating redundant sensor nodes while maintaining the quality of service. The problem we outline can be transformed into an NP-hard problem. Therefore, we propose an ILP formulation to attain a lower bound on the solution and a fast greedy technique. Moreover, we present a system for dynamically activating and deactivating sensor nodes in real time. We then use our graph representation to develop an efficient formulation for maximum lifetime. This formulation provides sufficient information for finding activation duties for each sensor node. Finally, we demonstrate the effectiveness of our techniques on data collected from several subjects.

  • a phonological expression for Physical Movement monitoring in body sensor networks
    Mobile Adhoc and Sensor Systems, 2008
    Co-Authors: Hassan Ghasemzadeh, Jaime Barnes, Eric Guenterberg, Roozbeh Jafari
    Abstract:

    Monitoring human activities using wearable wireless sensor nodes has the potential to enable many useful applications for everyday situations. The deployment of a compact and computationally efficient grammatical representation of actions reduces the complexities involved in the detection and recognition of human behaviors in a distributed system. In this paper, we introduce a road map to a linguistic framework for the symbolic representation of inertial information for Physical Movement monitoring. Our method for creating phonetic descriptions consists of constructing primitives across the network and assigning certain primitives to each Movement. Our technique exploits the notion of a decision tree to identify atomic actions corresponding to every given Movement. We pose an optimization problem for the fast identification of primitives. We then prove that this problem is NP-Complete and provide a fast greedy algorithm to approximate the solution. Finally, we demonstrate the effectiveness of our phonetic model on data collected from three subjects.

  • MASS - A phonological expression for Physical Movement monitoring in body sensor networks
    2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, 2008
    Co-Authors: Hassan Ghasemzadeh, Jaime Barnes, Eric Guenterberg, Roozbeh Jafari
    Abstract:

    Monitoring human activities using wearable wireless sensor nodes has the potential to enable many useful applications for everyday situations. The deployment of a compact and computationally efficient grammatical representation of actions reduces the complexities involved in the detection and recognition of human behaviors in a distributed system. In this paper, we introduce a road map to a linguistic framework for the symbolic representation of inertial information for Physical Movement monitoring. Our method for creating phonetic descriptions consists of constructing primitives across the network and assigning certain primitives to each Movement. Our technique exploits the notion of a decision tree to identify atomic actions corresponding to every given Movement. We pose an optimization problem for the fast identification of primitives. We then prove that this problem is NP-Complete and provide a fast greedy algorithm to approximate the solution. Finally, we demonstrate the effectiveness of our phonetic model on data collected from three subjects.

Shankar Sastry - One of the best experts on this subject based on the ideXlab platform.

  • a resource optimized Physical Movement monitoring scheme for environmental and on body sensor networks
    International Conference on Mobile Systems Applications and Services, 2007
    Co-Authors: Antti Vehkaoja, Mari Zakrzewski, Sameer Iyengar, Ruzena Bajcsy, Steven D Glaser, Shankar Sastry, Jukka Lekkala, Roozbeh Jafari
    Abstract:

    Perhaps the most significant challenge in design of on-body sensors is the wearability concern. This concern requires that the size of the nodes (sensors, processing units and batteries) is minimized. Therefore, the computation and communication executed in on-body nodes must be moderated significantly. In this paper, we propose a collaborative signal processing scheme for Physical Movement monitoring that utilizes on-body and environmental sensors. The environmental sensor nodes perform the bulk of the signal processing and provide feedback to the on-body sensor nodes. This is due to the fact that the environmental sensor nodes have access to more powerful processing units and an unlimited energy supply. The feedback simplifies the signal processing on the on-body nodes significantly. We achieve this by performing a hierarchical classification and introducing a probabilistic measure on likelihood of possible classes for the final level of classification on on-body sensor nodes. The experimental results show the effectiveness of our method. On average the classification accuracy is reduced by 3% while the computational complexity can be scaled down by one order of magnitude compared to a global and comprehensive classification scheme.

  • HealthNet - A resource optimized Physical Movement monitoring scheme for environmental and on-body sensor networks
    Proceedings of the 1st ACM SIGMOBILE international workshop on Systems and networking support for healthcare and assisted living environments - Health, 2007
    Co-Authors: Antti Vehkaoja, Mari Zakrzewski, Sameer Iyengar, Ruzena Bajcsy, Steven D Glaser, Shankar Sastry, Jukka Lekkala, Roozbeh Jafari
    Abstract:

    Perhaps the most significant challenge in design of on-body sensors is the wearability concern. This concern requires that the size of the nodes (sensors, processing units and batteries) is minimized. Therefore, the computation and communication executed in on-body nodes must be moderated significantly. In this paper, we propose a collaborative signal processing scheme for Physical Movement monitoring that utilizes on-body and environmental sensors. The environmental sensor nodes perform the bulk of the signal processing and provide feedback to the on-body sensor nodes. This is due to the fact that the environmental sensor nodes have access to more powerful processing units and an unlimited energy supply. The feedback simplifies the signal processing on the on-body nodes significantly. We achieve this by performing a hierarchical classification and introducing a probabilistic measure on likelihood of possible classes for the final level of classification on on-body sensor nodes. The experimental results show the effectiveness of our method. On average the classification accuracy is reduced by 3% while the computational complexity can be scaled down by one order of magnitude compared to a global and comprehensive classification scheme.

Jukka Lekkala - One of the best experts on this subject based on the ideXlab platform.

  • a resource optimized Physical Movement monitoring scheme for environmental and on body sensor networks
    International Conference on Mobile Systems Applications and Services, 2007
    Co-Authors: Antti Vehkaoja, Mari Zakrzewski, Sameer Iyengar, Ruzena Bajcsy, Steven D Glaser, Shankar Sastry, Jukka Lekkala, Roozbeh Jafari
    Abstract:

    Perhaps the most significant challenge in design of on-body sensors is the wearability concern. This concern requires that the size of the nodes (sensors, processing units and batteries) is minimized. Therefore, the computation and communication executed in on-body nodes must be moderated significantly. In this paper, we propose a collaborative signal processing scheme for Physical Movement monitoring that utilizes on-body and environmental sensors. The environmental sensor nodes perform the bulk of the signal processing and provide feedback to the on-body sensor nodes. This is due to the fact that the environmental sensor nodes have access to more powerful processing units and an unlimited energy supply. The feedback simplifies the signal processing on the on-body nodes significantly. We achieve this by performing a hierarchical classification and introducing a probabilistic measure on likelihood of possible classes for the final level of classification on on-body sensor nodes. The experimental results show the effectiveness of our method. On average the classification accuracy is reduced by 3% while the computational complexity can be scaled down by one order of magnitude compared to a global and comprehensive classification scheme.

  • HealthNet - A resource optimized Physical Movement monitoring scheme for environmental and on-body sensor networks
    Proceedings of the 1st ACM SIGMOBILE international workshop on Systems and networking support for healthcare and assisted living environments - Health, 2007
    Co-Authors: Antti Vehkaoja, Mari Zakrzewski, Sameer Iyengar, Ruzena Bajcsy, Steven D Glaser, Shankar Sastry, Jukka Lekkala, Roozbeh Jafari
    Abstract:

    Perhaps the most significant challenge in design of on-body sensors is the wearability concern. This concern requires that the size of the nodes (sensors, processing units and batteries) is minimized. Therefore, the computation and communication executed in on-body nodes must be moderated significantly. In this paper, we propose a collaborative signal processing scheme for Physical Movement monitoring that utilizes on-body and environmental sensors. The environmental sensor nodes perform the bulk of the signal processing and provide feedback to the on-body sensor nodes. This is due to the fact that the environmental sensor nodes have access to more powerful processing units and an unlimited energy supply. The feedback simplifies the signal processing on the on-body nodes significantly. We achieve this by performing a hierarchical classification and introducing a probabilistic measure on likelihood of possible classes for the final level of classification on on-body sensor nodes. The experimental results show the effectiveness of our method. On average the classification accuracy is reduced by 3% while the computational complexity can be scaled down by one order of magnitude compared to a global and comprehensive classification scheme.