The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Costas S Tzafestas - One of the best experts on this subject based on the ideXlab platform.
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user adaptive human robot formation control for an intelligent robotic walker using augmented human state estimation and Pathological Gait characterization
Intelligent Robots and Systems, 2018Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Petros Maragos, Costas S TzafestasAbstract:In this paper we describe a control strategy for a user-adaptive human-robot system for an intelligent robotic Mobility Assistive Device (MAD)using raw data from a single laser-range-finder (LRF)mounted on the MAD and scanning the walking area. The proposed control architecture consists of three modules. In the first module, a previously proposed methodology (termed IMM-PDA-PF)delivers the augmented human state estimation of the user by providing robust leg tracking and on-line estimation of the human Gait phases. This information is processed at the next module for providing the Pathological Gait parametrization and characterization, by computing specific Gait parameters for each Gait cycle. These Gait parameters form the feature vector that classifies the user in a certain class related to risk of fall. Those are of particular significance to the system, since the Gait parameters and the respective class are used in the third module, i.e. the human-robot formation controller, in order to adapt the desired formation of the human-robot system, by selecting the appropriate control variables. The experimental evaluation comprises Gait data from real patients, and demonstrates the stability of the human-robot formation control, indicating the importance of incorporating an on-line Gait characterization of the user, using non-wearable and non-invasive methods, in the context of a robotic MAD.
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IROS - User-Adaptive Human-Robot Formation Control for an Intelligent Robotic Walker Using Augmented Human State Estimation and Pathological Gait Characterization
2018 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2018Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Petros Maragos, Costas S TzafestasAbstract:In this paper we describe a control strategy for a user-adaptive human-robot system for an intelligent robotic Mobility Assistive Device (MAD)using raw data from a single laser-range-finder (LRF)mounted on the MAD and scanning the walking area. The proposed control architecture consists of three modules. In the first module, a previously proposed methodology (termed IMM-PDA-PF)delivers the augmented human state estimation of the user by providing robust leg tracking and on-line estimation of the human Gait phases. This information is processed at the next module for providing the Pathological Gait parametrization and characterization, by computing specific Gait parameters for each Gait cycle. These Gait parameters form the feature vector that classifies the user in a certain class related to risk of fall. Those are of particular significance to the system, since the Gait parameters and the respective class are used in the third module, i.e. the human-robot formation controller, in order to adapt the desired formation of the human-robot system, by selecting the appropriate control variables. The experimental evaluation comprises Gait data from real patients, and demonstrates the stability of the human-robot formation control, indicating the importance of incorporating an on-line Gait characterization of the user, using non-wearable and non-invasive methods, in the context of a robotic MAD.
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towards a user adaptive context aware robotic walker with a Pathological Gait assessment system first experimental study
Intelligent Robots and Systems, 2017Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Costas S TzafestasAbstract:When designing a user-friendly Mobility Assistive Device (MAD) for mobility constrained people, it is important to take into account the diverse spectrum of disabilities, which results to completely different needs to be covered by the MAD for each specific user. An intelligent adaptive behavior is necessary. In this work we present experimental results, using an in house developed methodology for assessing the Gait of users with different mobility status while interacting with a robotic MAD. We use data from a laser scanner, mounted on the MAD to track the legs using Particle Filters and Probabilistic Data Association (PDA-PF). The legs' states are fed to an HMM-based Pathological Gait cycle recognition system to compute in real-time the Gait parameters that are crucial for the mobility status characterization of the user. We aim to show that a Gait assessment system would be an important feedback for an intelligent MAD. Thus, we use this system to compare the Gaits of the subjects using two different control settings of the MAD and we experimentally validate the ability of our system to recognize the impact of the control designs on the users' walking performance. The results demonstrate that a generic control scheme does not meet every patient's needs, and therefore, an Adaptive Context-Aware MAD (ACA MAD), that can understand the specific needs of the user, is important for enhancing the human-robot physical interaction.
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IROS - Towards a user-adaptive context-aware robotic walker with a Pathological Gait assessment system: First experimental study
2017 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2017Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Costas S TzafestasAbstract:When designing a user-friendly Mobility Assistive Device (MAD) for mobility constrained people, it is important to take into account the diverse spectrum of disabilities, which results to completely different needs to be covered by the MAD for each specific user. An intelligent adaptive behavior is necessary. In this work we present experimental results, using an in house developed methodology for assessing the Gait of users with different mobility status while interacting with a robotic MAD. We use data from a laser scanner, mounted on the MAD to track the legs using Particle Filters and Probabilistic Data Association (PDA-PF). The legs' states are fed to an HMM-based Pathological Gait cycle recognition system to compute in real-time the Gait parameters that are crucial for the mobility status characterization of the user. We aim to show that a Gait assessment system would be an important feedback for an intelligent MAD. Thus, we use this system to compare the Gaits of the subjects using two different control settings of the MAD and we experimentally validate the ability of our system to recognize the impact of the control designs on the users' walking performance. The results demonstrate that a generic control scheme does not meet every patient's needs, and therefore, an Adaptive Context-Aware MAD (ACA MAD), that can understand the specific needs of the user, is important for enhancing the human-robot physical interaction.
Georgia Chalvatzaki - One of the best experts on this subject based on the ideXlab platform.
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user adaptive human robot formation control for an intelligent robotic walker using augmented human state estimation and Pathological Gait characterization
Intelligent Robots and Systems, 2018Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Petros Maragos, Costas S TzafestasAbstract:In this paper we describe a control strategy for a user-adaptive human-robot system for an intelligent robotic Mobility Assistive Device (MAD)using raw data from a single laser-range-finder (LRF)mounted on the MAD and scanning the walking area. The proposed control architecture consists of three modules. In the first module, a previously proposed methodology (termed IMM-PDA-PF)delivers the augmented human state estimation of the user by providing robust leg tracking and on-line estimation of the human Gait phases. This information is processed at the next module for providing the Pathological Gait parametrization and characterization, by computing specific Gait parameters for each Gait cycle. These Gait parameters form the feature vector that classifies the user in a certain class related to risk of fall. Those are of particular significance to the system, since the Gait parameters and the respective class are used in the third module, i.e. the human-robot formation controller, in order to adapt the desired formation of the human-robot system, by selecting the appropriate control variables. The experimental evaluation comprises Gait data from real patients, and demonstrates the stability of the human-robot formation control, indicating the importance of incorporating an on-line Gait characterization of the user, using non-wearable and non-invasive methods, in the context of a robotic MAD.
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IROS - User-Adaptive Human-Robot Formation Control for an Intelligent Robotic Walker Using Augmented Human State Estimation and Pathological Gait Characterization
2018 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2018Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Petros Maragos, Costas S TzafestasAbstract:In this paper we describe a control strategy for a user-adaptive human-robot system for an intelligent robotic Mobility Assistive Device (MAD)using raw data from a single laser-range-finder (LRF)mounted on the MAD and scanning the walking area. The proposed control architecture consists of three modules. In the first module, a previously proposed methodology (termed IMM-PDA-PF)delivers the augmented human state estimation of the user by providing robust leg tracking and on-line estimation of the human Gait phases. This information is processed at the next module for providing the Pathological Gait parametrization and characterization, by computing specific Gait parameters for each Gait cycle. These Gait parameters form the feature vector that classifies the user in a certain class related to risk of fall. Those are of particular significance to the system, since the Gait parameters and the respective class are used in the third module, i.e. the human-robot formation controller, in order to adapt the desired formation of the human-robot system, by selecting the appropriate control variables. The experimental evaluation comprises Gait data from real patients, and demonstrates the stability of the human-robot formation control, indicating the importance of incorporating an on-line Gait characterization of the user, using non-wearable and non-invasive methods, in the context of a robotic MAD.
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towards a user adaptive context aware robotic walker with a Pathological Gait assessment system first experimental study
Intelligent Robots and Systems, 2017Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Costas S TzafestasAbstract:When designing a user-friendly Mobility Assistive Device (MAD) for mobility constrained people, it is important to take into account the diverse spectrum of disabilities, which results to completely different needs to be covered by the MAD for each specific user. An intelligent adaptive behavior is necessary. In this work we present experimental results, using an in house developed methodology for assessing the Gait of users with different mobility status while interacting with a robotic MAD. We use data from a laser scanner, mounted on the MAD to track the legs using Particle Filters and Probabilistic Data Association (PDA-PF). The legs' states are fed to an HMM-based Pathological Gait cycle recognition system to compute in real-time the Gait parameters that are crucial for the mobility status characterization of the user. We aim to show that a Gait assessment system would be an important feedback for an intelligent MAD. Thus, we use this system to compare the Gaits of the subjects using two different control settings of the MAD and we experimentally validate the ability of our system to recognize the impact of the control designs on the users' walking performance. The results demonstrate that a generic control scheme does not meet every patient's needs, and therefore, an Adaptive Context-Aware MAD (ACA MAD), that can understand the specific needs of the user, is important for enhancing the human-robot physical interaction.
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IROS - Towards a user-adaptive context-aware robotic walker with a Pathological Gait assessment system: First experimental study
2017 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2017Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Costas S TzafestasAbstract:When designing a user-friendly Mobility Assistive Device (MAD) for mobility constrained people, it is important to take into account the diverse spectrum of disabilities, which results to completely different needs to be covered by the MAD for each specific user. An intelligent adaptive behavior is necessary. In this work we present experimental results, using an in house developed methodology for assessing the Gait of users with different mobility status while interacting with a robotic MAD. We use data from a laser scanner, mounted on the MAD to track the legs using Particle Filters and Probabilistic Data Association (PDA-PF). The legs' states are fed to an HMM-based Pathological Gait cycle recognition system to compute in real-time the Gait parameters that are crucial for the mobility status characterization of the user. We aim to show that a Gait assessment system would be an important feedback for an intelligent MAD. Thus, we use this system to compare the Gaits of the subjects using two different control settings of the MAD and we experimentally validate the ability of our system to recognize the impact of the control designs on the users' walking performance. The results demonstrate that a generic control scheme does not meet every patient's needs, and therefore, an Adaptive Context-Aware MAD (ACA MAD), that can understand the specific needs of the user, is important for enhancing the human-robot physical interaction.
Xanthi S Papageorgiou - One of the best experts on this subject based on the ideXlab platform.
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user adaptive human robot formation control for an intelligent robotic walker using augmented human state estimation and Pathological Gait characterization
Intelligent Robots and Systems, 2018Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Petros Maragos, Costas S TzafestasAbstract:In this paper we describe a control strategy for a user-adaptive human-robot system for an intelligent robotic Mobility Assistive Device (MAD)using raw data from a single laser-range-finder (LRF)mounted on the MAD and scanning the walking area. The proposed control architecture consists of three modules. In the first module, a previously proposed methodology (termed IMM-PDA-PF)delivers the augmented human state estimation of the user by providing robust leg tracking and on-line estimation of the human Gait phases. This information is processed at the next module for providing the Pathological Gait parametrization and characterization, by computing specific Gait parameters for each Gait cycle. These Gait parameters form the feature vector that classifies the user in a certain class related to risk of fall. Those are of particular significance to the system, since the Gait parameters and the respective class are used in the third module, i.e. the human-robot formation controller, in order to adapt the desired formation of the human-robot system, by selecting the appropriate control variables. The experimental evaluation comprises Gait data from real patients, and demonstrates the stability of the human-robot formation control, indicating the importance of incorporating an on-line Gait characterization of the user, using non-wearable and non-invasive methods, in the context of a robotic MAD.
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IROS - User-Adaptive Human-Robot Formation Control for an Intelligent Robotic Walker Using Augmented Human State Estimation and Pathological Gait Characterization
2018 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2018Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Petros Maragos, Costas S TzafestasAbstract:In this paper we describe a control strategy for a user-adaptive human-robot system for an intelligent robotic Mobility Assistive Device (MAD)using raw data from a single laser-range-finder (LRF)mounted on the MAD and scanning the walking area. The proposed control architecture consists of three modules. In the first module, a previously proposed methodology (termed IMM-PDA-PF)delivers the augmented human state estimation of the user by providing robust leg tracking and on-line estimation of the human Gait phases. This information is processed at the next module for providing the Pathological Gait parametrization and characterization, by computing specific Gait parameters for each Gait cycle. These Gait parameters form the feature vector that classifies the user in a certain class related to risk of fall. Those are of particular significance to the system, since the Gait parameters and the respective class are used in the third module, i.e. the human-robot formation controller, in order to adapt the desired formation of the human-robot system, by selecting the appropriate control variables. The experimental evaluation comprises Gait data from real patients, and demonstrates the stability of the human-robot formation control, indicating the importance of incorporating an on-line Gait characterization of the user, using non-wearable and non-invasive methods, in the context of a robotic MAD.
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towards a user adaptive context aware robotic walker with a Pathological Gait assessment system first experimental study
Intelligent Robots and Systems, 2017Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Costas S TzafestasAbstract:When designing a user-friendly Mobility Assistive Device (MAD) for mobility constrained people, it is important to take into account the diverse spectrum of disabilities, which results to completely different needs to be covered by the MAD for each specific user. An intelligent adaptive behavior is necessary. In this work we present experimental results, using an in house developed methodology for assessing the Gait of users with different mobility status while interacting with a robotic MAD. We use data from a laser scanner, mounted on the MAD to track the legs using Particle Filters and Probabilistic Data Association (PDA-PF). The legs' states are fed to an HMM-based Pathological Gait cycle recognition system to compute in real-time the Gait parameters that are crucial for the mobility status characterization of the user. We aim to show that a Gait assessment system would be an important feedback for an intelligent MAD. Thus, we use this system to compare the Gaits of the subjects using two different control settings of the MAD and we experimentally validate the ability of our system to recognize the impact of the control designs on the users' walking performance. The results demonstrate that a generic control scheme does not meet every patient's needs, and therefore, an Adaptive Context-Aware MAD (ACA MAD), that can understand the specific needs of the user, is important for enhancing the human-robot physical interaction.
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IROS - Towards a user-adaptive context-aware robotic walker with a Pathological Gait assessment system: First experimental study
2017 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2017Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Costas S TzafestasAbstract:When designing a user-friendly Mobility Assistive Device (MAD) for mobility constrained people, it is important to take into account the diverse spectrum of disabilities, which results to completely different needs to be covered by the MAD for each specific user. An intelligent adaptive behavior is necessary. In this work we present experimental results, using an in house developed methodology for assessing the Gait of users with different mobility status while interacting with a robotic MAD. We use data from a laser scanner, mounted on the MAD to track the legs using Particle Filters and Probabilistic Data Association (PDA-PF). The legs' states are fed to an HMM-based Pathological Gait cycle recognition system to compute in real-time the Gait parameters that are crucial for the mobility status characterization of the user. We aim to show that a Gait assessment system would be an important feedback for an intelligent MAD. Thus, we use this system to compare the Gaits of the subjects using two different control settings of the MAD and we experimentally validate the ability of our system to recognize the impact of the control designs on the users' walking performance. The results demonstrate that a generic control scheme does not meet every patient's needs, and therefore, an Adaptive Context-Aware MAD (ACA MAD), that can understand the specific needs of the user, is important for enhancing the human-robot physical interaction.
Toshinari Kamakura - One of the best experts on this subject based on the ideXlab platform.
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young and elderly normal and Pathological Gait analysis using frontal view Gait video data based on the statistical registration of spatiotemporal relationship
International Conference on Human-Computer Interaction, 2016Co-Authors: Kosuke Okusa, Toshinari KamakuraAbstract:We study the problem of analyzing and classifying frontal view Gait video data. In this study, we focus on the shape scale changing in the frontal view human Gait, we estimate scale parameters using the statistical registration and modeling on a video data. To demonstrate the effectiveness of our method, we apply our model to young and elderly, normal and Pathological Gait analysis. As a result, our model shows good performance for the scale estimation and Gait analysis.
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HCI (11) - Young and Elderly, Normal and Pathological Gait Analysis Using Frontal View Gait Video Data Based on the Statistical Registration of Spatiotemporal Relationship
Cross-Cultural Design, 2016Co-Authors: Kosuke Okusa, Toshinari KamakuraAbstract:We study the problem of analyzing and classifying frontal view Gait video data. In this study, we focus on the shape scale changing in the frontal view human Gait, we estimate scale parameters using the statistical registration and modeling on a video data. To demonstrate the effectiveness of our method, we apply our model to young and elderly, normal and Pathological Gait analysis. As a result, our model shows good performance for the scale estimation and Gait analysis.
Petros Maragos - One of the best experts on this subject based on the ideXlab platform.
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user adaptive human robot formation control for an intelligent robotic walker using augmented human state estimation and Pathological Gait characterization
Intelligent Robots and Systems, 2018Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Petros Maragos, Costas S TzafestasAbstract:In this paper we describe a control strategy for a user-adaptive human-robot system for an intelligent robotic Mobility Assistive Device (MAD)using raw data from a single laser-range-finder (LRF)mounted on the MAD and scanning the walking area. The proposed control architecture consists of three modules. In the first module, a previously proposed methodology (termed IMM-PDA-PF)delivers the augmented human state estimation of the user by providing robust leg tracking and on-line estimation of the human Gait phases. This information is processed at the next module for providing the Pathological Gait parametrization and characterization, by computing specific Gait parameters for each Gait cycle. These Gait parameters form the feature vector that classifies the user in a certain class related to risk of fall. Those are of particular significance to the system, since the Gait parameters and the respective class are used in the third module, i.e. the human-robot formation controller, in order to adapt the desired formation of the human-robot system, by selecting the appropriate control variables. The experimental evaluation comprises Gait data from real patients, and demonstrates the stability of the human-robot formation control, indicating the importance of incorporating an on-line Gait characterization of the user, using non-wearable and non-invasive methods, in the context of a robotic MAD.
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IROS - User-Adaptive Human-Robot Formation Control for an Intelligent Robotic Walker Using Augmented Human State Estimation and Pathological Gait Characterization
2018 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2018Co-Authors: Georgia Chalvatzaki, Xanthi S Papageorgiou, Petros Maragos, Costas S TzafestasAbstract:In this paper we describe a control strategy for a user-adaptive human-robot system for an intelligent robotic Mobility Assistive Device (MAD)using raw data from a single laser-range-finder (LRF)mounted on the MAD and scanning the walking area. The proposed control architecture consists of three modules. In the first module, a previously proposed methodology (termed IMM-PDA-PF)delivers the augmented human state estimation of the user by providing robust leg tracking and on-line estimation of the human Gait phases. This information is processed at the next module for providing the Pathological Gait parametrization and characterization, by computing specific Gait parameters for each Gait cycle. These Gait parameters form the feature vector that classifies the user in a certain class related to risk of fall. Those are of particular significance to the system, since the Gait parameters and the respective class are used in the third module, i.e. the human-robot formation controller, in order to adapt the desired formation of the human-robot system, by selecting the appropriate control variables. The experimental evaluation comprises Gait data from real patients, and demonstrates the stability of the human-robot formation control, indicating the importance of incorporating an on-line Gait characterization of the user, using non-wearable and non-invasive methods, in the context of a robotic MAD.