(+30) 210-772-1528
- gchal@mail.ntua.gr
- Office 2.1.12
Research Interests
Human Detection & Tracking, Assistive Robotics, Motion Planning, Target Tracking, Pattern Recognition, Human-Robot Interaction, Trajectory Planning
Awards and Distinctions
Best Paper Award: Georgia Chalvatzaki, Xanthi S. Papageorgiou, Costas S. Tzafestas, “Gait Modelling for a Context-Aware User-Adaptive Robotic Assistant Platform”, Proceedings of the 8th International Conference on Integrated Modeling and Analysis in Applied Control and Automation, Bergeggi, Italy, September 21 – 23, 2015.
Biosketch
Georgia Chalvatzaki received her Diploma degree in Electrical and Computer Engineering from NTUA in June 2012. Since October 2012, she is a PhD Candidate, under the supervision of Asst. Prof. Costas Tzafestas at the School of Electrical and Computer Engineering of NTUA, and serves as a research assistant within the Intelligent Robotics and Automation Lab (IRAL) of ICCS. Her research interests and expertise include human detection and tracking with applications in the field of assistive robotics. She has especially worked in the development of algorithms for robust on-line human gait tracking and non-intrusive assessment from laser rangefinder data using stochastic estimation techniques for the control of an intelligent robotic walker device for the elderly, in the frames of the MOBOT EU research project. She is currently working as a research assistant in the BabyRobot (H2020) project. She is a member of the Technical Chamber of Greece since 2013. She is, also, a member of IEEE, IEEE Robotics & Automation Society, IEEE Young Professionals, IEEE Women in Engineering, IEEE Robotics and Automation Society, IEEE Systems, Man, and Cybernetics Society.
Publications
2019 |
Georgia Chalvatzaki, Xanthi S Papageorgiou, Petros Maragos, Costas S Tzafestas Learn to adapt to human walking: A Model-based Reinforcement Learning Approach for a Robotic Assistant Rollator Journal Article IEEE Robotics and Automation Letters (with IROS option), 4 (4), pp. 3774–3781, 2019. @article{Chalvatzaki2019b, title = {Learn to adapt to human walking: A Model-based Reinforcement Learning Approach for a Robotic Assistant Rollator}, author = {Georgia Chalvatzaki and Xanthi S Papageorgiou and Petros Maragos and Costas S Tzafestas}, url = {http://robotics.ntua.gr/wp-content/uploads/sites/2/2019_ChalvatzakiEtAl_LearnToAdaptHumanWalk-RobotRollator_ieeeRAL.pdf}, year = {2019}, date = {2019-12-31}, journal = {IEEE Robotics and Automation Letters (with IROS option)}, volume = {4}, number = {4}, pages = {3774--3781}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2018 |
X S Papageorgiou, G Chalvatzaki, A Dometios, C S Tzafestas Human-Centered Service Robotic Systems for Assisted Living Conference Proceedings of the 27th International Conference on Robotics in Alpe-Adria Danube Region (RAAD 2018), 2018. Abstract | BibTeX | Links: [PDF] @conference{RAAD2018, title = {Human-Centered Service Robotic Systems for Assisted Living}, author = {X S Papageorgiou and G Chalvatzaki and A Dometios and C S Tzafestas}, url = {http://robotics.ntua.gr/wp-content/publications/RAAD2018.pdf}, year = {2018}, date = {2018-06-01}, booktitle = {Proceedings of the 27th International Conference on Robotics in Alpe-Adria Danube Region (RAAD 2018)}, abstract = {Mobility impairment is a common problem for the elderly population which relates to difficulties in performing Activities of Daily Living (ADLs) and consequently leads to restrictions and the degradation of the living standards of the elders. When designing a user-friendly assistive device for mobility constrained people, the variable spectrum of disabilities is a factor that should affect the design process, since people with different impairments have different needs to be covered by the device, thus an adaptive behavior of those systems is necessary. Also, the performance of bathing activities includes several challenges for the elderly people, since such tasks require body flexibility. In this paper, we present current frameworks and solutions for intelligent robotic systems for assistive living involving human robot interaction in a natural interface. Our aim is to build such systems, in order to increase the independence and safety of these procedures. To achieve human - robot interaction in a natural way, we have to adapt the expertise of carers regarding bathing motions and walking assistance. The main goal of this work is to present recent research results towards the development of two real-life use cases incorporating intelligent robotic systems, aiming to support mobility and bathing activities for the elderly in order to provide context-aware and user-adaptive assistance.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Mobility impairment is a common problem for the elderly population which relates to difficulties in performing Activities of Daily Living (ADLs) and consequently leads to restrictions and the degradation of the living standards of the elders. When designing a user-friendly assistive device for mobility constrained people, the variable spectrum of disabilities is a factor that should affect the design process, since people with different impairments have different needs to be covered by the device, thus an adaptive behavior of those systems is necessary. Also, the performance of bathing activities includes several challenges for the elderly people, since such tasks require body flexibility. In this paper, we present current frameworks and solutions for intelligent robotic systems for assistive living involving human robot interaction in a natural interface. Our aim is to build such systems, in order to increase the independence and safety of these procedures. To achieve human - robot interaction in a natural way, we have to adapt the expertise of carers regarding bathing motions and walking assistance. The main goal of this work is to present recent research results towards the development of two real-life use cases incorporating intelligent robotic systems, aiming to support mobility and bathing activities for the elderly in order to provide context-aware and user-adaptive assistance. |
2017 |
G Chalvatzaki, X S Papageorgiou, C S Tzafestas, P Maragos HMM-based Pathological Gait Analyzer for a User-Adaptive Intelligent Robotic Walker Conference Proc. 25th European Conf.(EUSIPCO-17) Workshop: "MultiLearn 2017 - Multimodal processing, modeling and learning for human-computer/robot interaction applications", Kos, Greece, 2017. Abstract | BibTeX | Links: [PDF] @conference{CPTM_WML17, title = {HMM-based Pathological Gait Analyzer for a User-Adaptive Intelligent Robotic Walker}, author = {G Chalvatzaki and X S Papageorgiou and C S Tzafestas and P Maragos}, url = {http://robotics.ntua.gr/wp-content/publications/CPTM_WML17.pdf}, year = {2017}, date = {2017-09-01}, booktitle = {Proc. 25th European Conf.(EUSIPCO-17) Workshop: "MultiLearn 2017 - Multimodal processing, modeling and learning for human-computer/robot interaction applications"}, address = {Kos, Greece}, abstract = {During the past decade, robotic technology has evolved considerably towards the development of cognitive robotic systems that enable close interaction with humans. Application fields of such novel robotic technologies are now wide spreading covering a variety of human assistance function- alities, aiming in particular at supporting the needs of human beings experiencing various forms of mobility or cognitive impairments. Mobility impairments are prevalent in the elderly population and constitute one of the main causes related to difficulties in performing Activities of Daily Living (ADLs) and consequent reduction of quality of life. This paper re- ports current research work related to the development of a pathological gait analyzer for intelligent robotic rollator aiming to be an input to a user-adaptive and context-aware robot control architecture. Specifically, we present a novel method for human leg tracking using Particle Filters and Probablistic Data Association from a laser scanner, constituting a non- wearable and non-intrusive approach. The tracked positions and velocities of the user’s legs are the observables of an HMM, which provides the gait phases of the detected gait cycles. Given those phases we compute specific gait parameters, which are used for medical diagnosis. The results of our pathological gait analyzer are validated using ground truth data from a GAITRite system. The results presented in this paper demonstrate that the proposed human data analysis scheme has the potential to provide the necessary methodological (modeling, inference, and learning) framework for a cognitive behavior- based robot control system.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } During the past decade, robotic technology has evolved considerably towards the development of cognitive robotic systems that enable close interaction with humans. Application fields of such novel robotic technologies are now wide spreading covering a variety of human assistance function- alities, aiming in particular at supporting the needs of human beings experiencing various forms of mobility or cognitive impairments. Mobility impairments are prevalent in the elderly population and constitute one of the main causes related to difficulties in performing Activities of Daily Living (ADLs) and consequent reduction of quality of life. This paper re- ports current research work related to the development of a pathological gait analyzer for intelligent robotic rollator aiming to be an input to a user-adaptive and context-aware robot control architecture. Specifically, we present a novel method for human leg tracking using Particle Filters and Probablistic Data Association from a laser scanner, constituting a non- wearable and non-intrusive approach. The tracked positions and velocities of the user’s legs are the observables of an HMM, which provides the gait phases of the detected gait cycles. Given those phases we compute specific gait parameters, which are used for medical diagnosis. The results of our pathological gait analyzer are validated using ground truth data from a GAITRite system. The results presented in this paper demonstrate that the proposed human data analysis scheme has the potential to provide the necessary methodological (modeling, inference, and learning) framework for a cognitive behavior- based robot control system. |
G Chalvatzaki, X S Papageorgiou, C S Tzafestas Towards a user-adaptive context-aware robotic walker with a pathological gait assessment system: First experimental study Conference IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017. Abstract | BibTeX | Links: [PDF] @conference{CPT17, title = {Towards a user-adaptive context-aware robotic walker with a pathological gait assessment system: First experimental study}, author = {G Chalvatzaki and X S Papageorgiou and C S Tzafestas}, url = {http://robotics.ntua.gr/wp-content/publications/CPT17.pdf}, doi = {10.1109/IROS.2017.8206388}, year = {2017}, date = {2017-09-01}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, pages = {5037-5042}, abstract = {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.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } 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. |
G Chalvatzaki, X S Papageorgiou, C S Tzafestas, P Maragos Estimating double support in pathological gaits using an HMM-based analyzer for an intelligent robotic walker Conference IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2017. Abstract | BibTeX | Links: [PDF] @conference{CPTM_ROMAN17, title = {Estimating double support in pathological gaits using an HMM-based analyzer for an intelligent robotic walker}, author = {G Chalvatzaki and X S Papageorgiou and C S Tzafestas and P Maragos}, url = {http://robotics.ntua.gr/wp-content/publications/CPTM_ROMAN17.pdf}, doi = {10.1109/ROMAN.2017.8172287}, year = {2017}, date = {2017-08-01}, booktitle = {IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)}, pages = {101-106}, abstract = {For a robotic walker designed to assist mobility constrained people, it is important to take into account the different spectrum of pathological walking patterns, which result into completely different needs to be covered for each specific user. For a deployable intelligent assistant robot it is necessary to have a precise gait analysis system, providing real-time monitoring of the user and extracting specific gait parameters, which are associated with the rehabilitation progress and the risk of fall. In this paper, we present a completely non-invasive framework for the on-line analysis of pathological human gait and the recognition of specific gait phases and events. The performance of this gait analysis system is assessed, in particular, as related to the estimation of double support phases, which are typically difficult to extract reliably, especially when applying non-wearable and non-intrusive technologies. Furthermore, the duration of double support phases constitutes an important gait parameter and a critical indicator in pathological gait patterns. The performance of this framework is assessed using real data collected from an ensemble of elderly persons with different pathologies. The estimated gait parameters are experimentally validated using ground truth data provided by a Motion Capture system. The results obtained and presented in this paper demonstrate that the proposed human data analysis (modeling, learning and inference) framework has the potential to support efficient detection and classification of specific walking pathologies, as needed to empower a cognitive robotic mobility-assistance device with user-adaptive and context-aware functionalities.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } For a robotic walker designed to assist mobility constrained people, it is important to take into account the different spectrum of pathological walking patterns, which result into completely different needs to be covered for each specific user. For a deployable intelligent assistant robot it is necessary to have a precise gait analysis system, providing real-time monitoring of the user and extracting specific gait parameters, which are associated with the rehabilitation progress and the risk of fall. In this paper, we present a completely non-invasive framework for the on-line analysis of pathological human gait and the recognition of specific gait phases and events. The performance of this gait analysis system is assessed, in particular, as related to the estimation of double support phases, which are typically difficult to extract reliably, especially when applying non-wearable and non-intrusive technologies. Furthermore, the duration of double support phases constitutes an important gait parameter and a critical indicator in pathological gait patterns. The performance of this framework is assessed using real data collected from an ensemble of elderly persons with different pathologies. The estimated gait parameters are experimentally validated using ground truth data provided by a Motion Capture system. The results obtained and presented in this paper demonstrate that the proposed human data analysis (modeling, learning and inference) framework has the potential to support efficient detection and classification of specific walking pathologies, as needed to empower a cognitive robotic mobility-assistance device with user-adaptive and context-aware functionalities. |
G Chalvatzaki, X S Papageorgiou, C S Tzafestas, P Maragos Comparative experimental validation of human gait tracking algorithms for an intelligent robotic rollator Conference IEEE International Conference on Robotics and Automation (ICRA), 2017. Abstract | BibTeX | Links: [PDF] @conference{CPTM_ICRA17, title = {Comparative experimental validation of human gait tracking algorithms for an intelligent robotic rollator}, author = {G Chalvatzaki and X S Papageorgiou and C S Tzafestas and P Maragos}, url = {http://robotics.ntua.gr/wp-content/publications/CPTM_ICRA17.pdf}, doi = {10.1109/ICRA.2017.7989713}, year = {2017}, date = {2017-05-01}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, pages = {6026-6031}, abstract = {Tracking human gait accurately and robustly constitutes a key factor for a smart robotic walker, aiming to provide assistance to patients with different mobility impairment. A context-aware assistive robot needs constant knowledge of the user's kinematic state to assess the gait status and adjust its movement properly to provide optimal assistance. In this work, we experimentally validate the performance of two gait tracking algorithms using data from elderly patients; the first algorithm employs a Kalman Filter (KF), while the second one tracks the user legs separately using two probabilistically associated Particle Filters (PFs). The algorithms are compared according to their accuracy and robustness, using data captured from real experiments, where elderly subjects performed specific walking scenarios with physical assistance from a prototype Robotic Rollator. Sensorial data were provided by a laser rangefinder mounted on the robotic platform recording the movement of the user's legs. The accuracy of the proposed algorithms is analysed and validated with respect to ground truth data provided by a Motion Capture system tracking a set of visual markers worn by the patients. The robustness of the two tracking algorithms is also analysed comparatively in a complex maneuvering scenario. Current experimental findings demonstrate the superior performance of the PFs in difficult cases of occlusions and clutter, where KF tracking often fails.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Tracking human gait accurately and robustly constitutes a key factor for a smart robotic walker, aiming to provide assistance to patients with different mobility impairment. A context-aware assistive robot needs constant knowledge of the user's kinematic state to assess the gait status and adjust its movement properly to provide optimal assistance. In this work, we experimentally validate the performance of two gait tracking algorithms using data from elderly patients; the first algorithm employs a Kalman Filter (KF), while the second one tracks the user legs separately using two probabilistically associated Particle Filters (PFs). The algorithms are compared according to their accuracy and robustness, using data captured from real experiments, where elderly subjects performed specific walking scenarios with physical assistance from a prototype Robotic Rollator. Sensorial data were provided by a laser rangefinder mounted on the robotic platform recording the movement of the user's legs. The accuracy of the proposed algorithms is analysed and validated with respect to ground truth data provided by a Motion Capture system tracking a set of visual markers worn by the patients. The robustness of the two tracking algorithms is also analysed comparatively in a complex maneuvering scenario. Current experimental findings demonstrate the superior performance of the PFs in difficult cases of occlusions and clutter, where KF tracking often fails. |
X S Papageorgiou, G Chalvatzaki, A Dometios, C S Tzafestas, P Maragos Intelligent Assistive Robotic Systems for the Elderly: Two Real-life Use Cases Conference C_PETRA, ACM, Island of Rhodes, Greece, 2017, ISBN: 978-1-4503-5227-7. Abstract | BibTeX | Links: [PDF] @conference{PETRA2017, title = {Intelligent Assistive Robotic Systems for the Elderly: Two Real-life Use Cases}, author = {X S Papageorgiou and G Chalvatzaki and A Dometios and C S Tzafestas and P Maragos}, url = {http://robotics.ntua.gr/wp-content/publications/PETRA2017.pdf}, doi = {10.1145/3056540.3076184}, isbn = {978-1-4503-5227-7}, year = {2017}, date = {2017-01-01}, booktitle = {C_PETRA}, pages = {360--365}, publisher = {ACM}, address = {Island of Rhodes, Greece}, abstract = {Mobility impairments are prevalent in the elderly population and constitute one of the main causes related to difficulties in performing Activities of Daily Living (ADLs) and consequent reduction of quality of life. When designing a user-friendly assistive device for mobility constrained people, it is important to take into account the diverse spectrum of disabilities, which results into completely different needs to be covered by the device for each specific user. An intelligent adaptive behavior is necessary for the deployment of such systems. Also, elderly people have particular needs in specific case of performing bathing activities, since these tasks require body flexibility. We explore new aspects of assistive living via intelligent assistive robotic systems involving human robot interaction in a natural interface. Our aim is to build assistive robotic systems, in order to increase the independence and safety of these procedures. Towards this end, the expertise of professional carers for walking or bathing sequences and appropriate motions have to be adopted, in order to achieve natural, physical human - robot interaction. Our goal is to report current research work related to the development of two real-life use cases of intelligent robotic systems for elderly aiming to provide user-adaptive and context-aware assistance.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Mobility impairments are prevalent in the elderly population and constitute one of the main causes related to difficulties in performing Activities of Daily Living (ADLs) and consequent reduction of quality of life. When designing a user-friendly assistive device for mobility constrained people, it is important to take into account the diverse spectrum of disabilities, which results into completely different needs to be covered by the device for each specific user. An intelligent adaptive behavior is necessary for the deployment of such systems. Also, elderly people have particular needs in specific case of performing bathing activities, since these tasks require body flexibility. We explore new aspects of assistive living via intelligent assistive robotic systems involving human robot interaction in a natural interface. Our aim is to build assistive robotic systems, in order to increase the independence and safety of these procedures. Towards this end, the expertise of professional carers for walking or bathing sequences and appropriate motions have to be adopted, in order to achieve natural, physical human - robot interaction. Our goal is to report current research work related to the development of two real-life use cases of intelligent robotic systems for elderly aiming to provide user-adaptive and context-aware assistance. |
2016 |
G Chalvatzaki, X S Papageorgiou, C Werner, K Hauer, C S Tzafestas, P Maragos Experimental comparison of human gait tracking algorithms: Towards a context-aware mobility assistance robotic walker Conference Mediterranean Conference on Control and Automation (MED), 2016. Abstract | BibTeX | Links: [PDF] @conference{CPWHTM16, title = {Experimental comparison of human gait tracking algorithms: Towards a context-aware mobility assistance robotic walker}, author = {G Chalvatzaki and X S Papageorgiou and C Werner and K Hauer and C S Tzafestas and P Maragos}, url = {http://robotics.ntua.gr/wp-content/publications/CPWHTM16.pdf}, doi = {10.1109/MED.2016.7535962}, year = {2016}, date = {2016-06-01}, booktitle = {Mediterranean Conference on Control and Automation (MED)}, pages = {719-724}, abstract = {Towards a mobility assistance robot for the elderly, it is essential to develop a robust and accurate gait tracking system. Various pathologies cause mobility inabilities to the aged population, leading to different gait patterns and walking speed. In this work, we present the experimental comparison of two user leg tracking systems of a robotic assistance walker, using data collected by a laser range sensor. The first one is a Kalman Filter tracking system, while the second one proposes the use of Particle Filters. The tracking systems provide the positions and velocities of the user's legs, which are used as observations into an HMM-based gait phases recognition system. The spatiotemporal results of the HMM framework are employed for computing parameters that characterize the human motion, which subsequently can be used to assess and distinguish between possible motion disabilities. For the experimental comparison, we are using real data collected from an ensemble of different elderly persons with a number of pathologies, and ground truth data from a GaitRite System. The results presented in this work, demonstrate the applicability of the tracking systems in real test cases.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Towards a mobility assistance robot for the elderly, it is essential to develop a robust and accurate gait tracking system. Various pathologies cause mobility inabilities to the aged population, leading to different gait patterns and walking speed. In this work, we present the experimental comparison of two user leg tracking systems of a robotic assistance walker, using data collected by a laser range sensor. The first one is a Kalman Filter tracking system, while the second one proposes the use of Particle Filters. The tracking systems provide the positions and velocities of the user's legs, which are used as observations into an HMM-based gait phases recognition system. The spatiotemporal results of the HMM framework are employed for computing parameters that characterize the human motion, which subsequently can be used to assess and distinguish between possible motion disabilities. For the experimental comparison, we are using real data collected from an ensemble of different elderly persons with a number of pathologies, and ground truth data from a GaitRite System. The results presented in this work, demonstrate the applicability of the tracking systems in real test cases. |
X S Papageorgiou, G Chalvatzaki, K N Lianos, C Werner, K Hauer, C S Tzafestas, P Maragos Experimental validation of human pathological gait analysis for an assisted living intelligent robotic walker Conference C_BIOROB, 2016. Abstract | BibTeX | Links: [PDF] @conference{BIOROB2016, title = {Experimental validation of human pathological gait analysis for an assisted living intelligent robotic walker}, author = {X S Papageorgiou and G Chalvatzaki and K N Lianos and C Werner and K Hauer and C S Tzafestas and P Maragos}, url = {http://robotics.ntua.gr/wp-content/publications/BIOROB2016.pdf}, doi = {10.1109/BIOROB.2016.7523776}, year = {2016}, date = {2016-06-01}, booktitle = {C_BIOROB}, pages = {1086-1091}, abstract = {A robust and effective gait analysis functionality is an essential characteristic for an assistance mobility robot dealing with elderly persons. The aforementioned functionality is crucial for dealing with mobility disabilities which are widespread in these parts of the population. In this work we present experimental validation of our in house developed system. We are using real data, collected from an ensemble of different elderly persons with a number of pathologies, and we present a validation study by using a GaitRite System. Our system, following the standard literature conventions, characterizes the human motion with a set of parameters which subsequently can be used to assess and distinguish between possible motion disabilities, using a laser range finder as its main sensor. The initial results, presented in this work, demonstrate the applicability of our framework in real test cases. Regarding such frameworks, a crucial technical question is the necessary complexity of the overall tracking system. To answer this question, we compare two approaches with different complexity levels. The first is a static rule based system acting on filtered laser data, while the second system utilizes a Hidden Markov Model for gait cycle estimation, and extraction of the gait parameters. The results demonstrate that the added complexity of the HMM system is necessary for improving the accuracy and efficacy of the system.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } A robust and effective gait analysis functionality is an essential characteristic for an assistance mobility robot dealing with elderly persons. The aforementioned functionality is crucial for dealing with mobility disabilities which are widespread in these parts of the population. In this work we present experimental validation of our in house developed system. We are using real data, collected from an ensemble of different elderly persons with a number of pathologies, and we present a validation study by using a GaitRite System. Our system, following the standard literature conventions, characterizes the human motion with a set of parameters which subsequently can be used to assess and distinguish between possible motion disabilities, using a laser range finder as its main sensor. The initial results, presented in this work, demonstrate the applicability of our framework in real test cases. Regarding such frameworks, a crucial technical question is the necessary complexity of the overall tracking system. To answer this question, we compare two approaches with different complexity levels. The first is a static rule based system acting on filtered laser data, while the second system utilizes a Hidden Markov Model for gait cycle estimation, and extraction of the gait parameters. The results demonstrate that the added complexity of the HMM system is necessary for improving the accuracy and efficacy of the system. |
2015 |
G Papageorgiou X.S. Moustris, G Pitsikalis V. Chalvatzaki, A Dometios, N Kardaris, C S Tzafestas, P Maragos User-Oriented Cognitive Interaction and Control for an Intelligent Robotic Walker Conference 17th International Conference on Social Robotics (ICSR 2015), 2015. Abstract | BibTeX | Links: [PDF] @conference{ICSR2015_2, title = {User-Oriented Cognitive Interaction and Control for an Intelligent Robotic Walker}, author = {G Papageorgiou X.S. Moustris and G Pitsikalis V. Chalvatzaki and A Dometios and N Kardaris and C S Tzafestas and P Maragos}, url = {http://robotics.ntua.gr/wp-content/publications/ICSR2015_2.pdf}, year = {2015}, date = {2015-10-01}, booktitle = {17th International Conference on Social Robotics (ICSR 2015)}, abstract = {Mobility impairments are prevalent in the elderly population and constitute one of the main causes related to difficulties in performing Activities of Daily Living (ADLs) and consequent reduction of quality of life. This paper reports current research work related to the control of an intelligent robotic rollator aiming to provide user-adaptive and context-aware walking assistance. To achieve such targets, a large spectrum of multimodal sensory processing and interactive control modules need to be developed and seamlessly integrated, that can, on one side track and analyse human motions and actions, in order to detect pathological situations and estimate user needs, while predicting at the same time the user (short-term or long-range) intentions in order to adapt robot control actions and supportive behaviours accordingly. User-oriented human-robot interaction and control refers to the functionalities that couple the motions, the actions and, in more general terms, the behaviours of the assistive robotic device to the user in a non-physical interaction context.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Mobility impairments are prevalent in the elderly population and constitute one of the main causes related to difficulties in performing Activities of Daily Living (ADLs) and consequent reduction of quality of life. This paper reports current research work related to the control of an intelligent robotic rollator aiming to provide user-adaptive and context-aware walking assistance. To achieve such targets, a large spectrum of multimodal sensory processing and interactive control modules need to be developed and seamlessly integrated, that can, on one side track and analyse human motions and actions, in order to detect pathological situations and estimate user needs, while predicting at the same time the user (short-term or long-range) intentions in order to adapt robot control actions and supportive behaviours accordingly. User-oriented human-robot interaction and control refers to the functionalities that couple the motions, the actions and, in more general terms, the behaviours of the assistive robotic device to the user in a non-physical interaction context. |
G Chalvatzaki, X S Papageorgiou, C S Tzafestas Gait Modelling for a Context-Aware User-Adaptive Robotic Assistant Platform Conference 2015, ISSN: 978-88-97999-63-8. Abstract | BibTeX | Links: [PDF] @conference{CPT15, title = {Gait Modelling for a Context-Aware User-Adaptive Robotic Assistant Platform}, author = {G Chalvatzaki and X S Papageorgiou and C S Tzafestas}, url = {http://robotics.ntua.gr/wp-content/publications/CPT15.pdf}, issn = {978-88-97999-63-8}, year = {2015}, date = {2015-09-01}, pages = {132-141}, abstract = {For a context-aware robotic assistant platform that follows patients with moderate mobility impairment and adapts its motion to the patient?s needs, the de- velopment of an efficient leg tracker and the recogni- tion of pathological gait are very important. In this work, we present the basic concept for the robot con- trol architecture and analyse three essential parts of the Adaptive Context-Aware Robot Control scheme; the detection and tracking of the subject?s legs, the gait modelling and classification and the computation of gait parameters for the impairment level assess- ment. We initially process raw laser data and estimate the legs? position and velocity with a Kalman Filter and then use this information as input for a Hidden Markov Model-based framework that detects specific gait patterns and classifies human gait into normal or pathological. We then compute gait parameters com- monly used for medical diagnosis. The recognised gait patterns along with the gait parameters will be used for the impairment level assessment, which will activate certain control assistive actions regarding the pathological state of the patient.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } For a context-aware robotic assistant platform that follows patients with moderate mobility impairment and adapts its motion to the patient?s needs, the de- velopment of an efficient leg tracker and the recogni- tion of pathological gait are very important. In this work, we present the basic concept for the robot con- trol architecture and analyse three essential parts of the Adaptive Context-Aware Robot Control scheme; the detection and tracking of the subject?s legs, the gait modelling and classification and the computation of gait parameters for the impairment level assess- ment. We initially process raw laser data and estimate the legs? position and velocity with a Kalman Filter and then use this information as input for a Hidden Markov Model-based framework that detects specific gait patterns and classifies human gait into normal or pathological. We then compute gait parameters com- monly used for medical diagnosis. The recognised gait patterns along with the gait parameters will be used for the impairment level assessment, which will activate certain control assistive actions regarding the pathological state of the patient. |
X S Papageorgiou, G Chalvatzaki, C S Tzafestas, P Maragos Hidden markov modeling of human pathological gait using laser range finder for an assisted living intelligent robotic walker Conference IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015. Abstract | BibTeX | Links: [PDF] @conference{IROS2015, title = {Hidden markov modeling of human pathological gait using laser range finder for an assisted living intelligent robotic walker}, author = {X S Papageorgiou and G Chalvatzaki and C S Tzafestas and P Maragos}, url = {http://robotics.ntua.gr/wp-content/publications/IROS2015.pdf}, doi = {10.1109/IROS.2015.7354283}, year = {2015}, date = {2015-09-01}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, pages = {6342-6347}, abstract = {The precise analysis of a patient's or an elderly person's walking pattern is very important for an effective intelligent active mobility assistance robot. This walking pattern can be described by a cyclic motion, which can be modeled using the consecutive gait phases. In this paper, we present a completely non-invasive framework for analyzing and recognizing a pathological human walking gait pattern. Our framework utilizes a laser range finder sensor to detect and track the human legs, and an appropriately synthesized Hidden Markov Model (HMM) for state estimation, and recognition of the gait patterns. We demonstrate the applicability of this setup using real data, collected from an ensemble of different elderly persons with a number of pathologies. The results presented in this paper demonstrate that the proposed human data analysis scheme has the potential to provide the necessary methodological (modeling, inference, and learning) framework for a cognitive behavior-based robot control system. More specifically, the proposed framework has the potential to be used for the classification of specific walking pathologies, which is needed for the development of a context-aware robot mobility assistant.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } The precise analysis of a patient's or an elderly person's walking pattern is very important for an effective intelligent active mobility assistance robot. This walking pattern can be described by a cyclic motion, which can be modeled using the consecutive gait phases. In this paper, we present a completely non-invasive framework for analyzing and recognizing a pathological human walking gait pattern. Our framework utilizes a laser range finder sensor to detect and track the human legs, and an appropriately synthesized Hidden Markov Model (HMM) for state estimation, and recognition of the gait patterns. We demonstrate the applicability of this setup using real data, collected from an ensemble of different elderly persons with a number of pathologies. The results presented in this paper demonstrate that the proposed human data analysis scheme has the potential to provide the necessary methodological (modeling, inference, and learning) framework for a cognitive behavior-based robot control system. More specifically, the proposed framework has the potential to be used for the classification of specific walking pathologies, which is needed for the development of a context-aware robot mobility assistant. |
2014 |
X S Papageorgiou, G Chalvatzaki, C S Tzafestas, P Maragos Hidden Markov modeling of human normal gait using laser range finder for a mobility assistance robot Conference IEEE International Conference on Robotics and Automation (ICRA), 2014, ISSN: 1050-4729. Abstract | BibTeX | Links: [PDF] @conference{ICRA2014, title = {Hidden Markov modeling of human normal gait using laser range finder for a mobility assistance robot}, author = {X S Papageorgiou and G Chalvatzaki and C S Tzafestas and P Maragos}, url = {http://robotics.ntua.gr/wp-content/publications/ICRA2014.pdf}, doi = {10.1109/ICRA.2014.6906899}, issn = {1050-4729}, year = {2014}, date = {2014-05-01}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, pages = {482-487}, abstract = {For an effective intelligent active mobility assistance robot, the walking pattern of a patient or an elderly person has to be analyzed precisely. A well-known fact is that the walking patterns are gaits, that is, cyclic patterns with several consecutive phases. These cyclic motions can be modeled using the consecutive gait phases. In this paper, we present a completely non-invasive framework for analyzing a normal human walking gait pattern. Our framework utilizes a laser range finder sensor to collect the data, a combination of filters to preprocess these data, and an appropriately synthesized Hidden Markov Model (HMM) for state estimation, and recognition of the gait data. We demonstrate the applicability of this setup using real data, collected from an ensemble of different persons. The results presented in this paper demonstrate that the proposed human data analysis scheme has the potential to provide the necessary methodological (modeling, inference, and learning) framework for a cognitive behavior-based robot control system. More specifically, the proposed framework has the potential to be used for the recognition of abnormal gait patterns and the subsequent classification of specific walking pathologies, which is needed for the development of a context-aware robot mobility assistant.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } For an effective intelligent active mobility assistance robot, the walking pattern of a patient or an elderly person has to be analyzed precisely. A well-known fact is that the walking patterns are gaits, that is, cyclic patterns with several consecutive phases. These cyclic motions can be modeled using the consecutive gait phases. In this paper, we present a completely non-invasive framework for analyzing a normal human walking gait pattern. Our framework utilizes a laser range finder sensor to collect the data, a combination of filters to preprocess these data, and an appropriately synthesized Hidden Markov Model (HMM) for state estimation, and recognition of the gait data. We demonstrate the applicability of this setup using real data, collected from an ensemble of different persons. The results presented in this paper demonstrate that the proposed human data analysis scheme has the potential to provide the necessary methodological (modeling, inference, and learning) framework for a cognitive behavior-based robot control system. More specifically, the proposed framework has the potential to be used for the recognition of abnormal gait patterns and the subsequent classification of specific walking pathologies, which is needed for the development of a context-aware robot mobility assistant. |
X S Papageorgiou, C S Tzafestas, P Maragos, G Pavlakos, G Chalvatzaki, G Moustris, I Kokkinos, A Peer, B Stanczyk, E -S Fotinea, E Efthimiou Advances in Intelligent Mobility Assistance Robot Integrating Multimodal Sensory Processing Conference J_HCII, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-07446-7. Abstract | BibTeX | Links: [PDF] @conference{HCII2014, title = {Advances in Intelligent Mobility Assistance Robot Integrating Multimodal Sensory Processing}, author = {X S Papageorgiou and C S Tzafestas and P Maragos and G Pavlakos and G Chalvatzaki and G Moustris and I Kokkinos and A Peer and B Stanczyk and E -S Fotinea and E Efthimiou}, editor = {C Stephanidis and M Antona}, url = {http://robotics.ntua.gr/wp-content/publications/HCII2014.pdf}, doi = {https://doi.org/10.1007/978-3-319-07446-7_66}, isbn = {978-3-319-07446-7}, year = {2014}, date = {2014-01-01}, booktitle = {J_HCII}, pages = {692--703}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {Mobility disabilities are prevalent in our ageing society and impede activities important for the independent living of elderly people and their quality of life. The goal of this work is to support human mobility and thus enforce fitness and vitality by developing intelligent robotic platforms designed to provide user-centred and natural support for ambulating in indoor environments. We envision the design of cognitive mobile robotic systems that can monitor and understand specific forms of human activity, in order to deduce what the human needs are, in terms of mobility. The goal is to provide user and context adaptive active support and ambulation assistance to elderly users, and generally to individuals with specific forms of moderate to mild walking impairment.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Mobility disabilities are prevalent in our ageing society and impede activities important for the independent living of elderly people and their quality of life. The goal of this work is to support human mobility and thus enforce fitness and vitality by developing intelligent robotic platforms designed to provide user-centred and natural support for ambulating in indoor environments. We envision the design of cognitive mobile robotic systems that can monitor and understand specific forms of human activity, in order to deduce what the human needs are, in terms of mobility. The goal is to provide user and context adaptive active support and ambulation assistance to elderly users, and generally to individuals with specific forms of moderate to mild walking impairment. |