The Experts below are selected from a list of 36 Experts worldwide ranked by ideXlab platform
William J. Kaiser - One of the best experts on this subject based on the ideXlab platform.
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Demonstration of active guidance with SmartCane
2015Co-Authors: Maxim Batalin, Thanos Stathopoulos, William J. KaiserAbstract:The usage of conventional Assistive Cane devices is criti-cal in reducing the risk of falls, which are particularly detri-mental for the elderly and disabled. Many of the individu-als that experience the greatest risk of falling rely on Cane devices for support of ambulation. However, the results of many studies have shown that incorrect Cane usage is preva-lent among Cane users. The original SmartCane Assistive system [4] has been developed to provide a method for ac-quiring detailed motion data from Cane usage. The Cane it-self, however, lacks any type of programmability as well as real-time data processing algorithms to provide feedback to the Cane user. In this demonstration, we have incorporated an embedded computing platform into SmartCane [2] and developed a real-time sensor information processing algo-rithm that provides direct detection of Cane usage charac-teristics. The new system provides local data processing capability by classifying whether an individual is execut-ing a stride with proper Cane motion and applied forces. It also provides direct feedback information to the individual, thereby guiding the subject towards proper Cane usage and reducing the risk of falls. 1
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Active guidance towards proper Cane usage
2008 5th International Summer School and Symposium on Medical Devices and Biosensors, 2008Co-Authors: Maxim Batalin, William J. KaiserAbstract:The usage of conventional Assistive Cane devices is critical in reducing the risk of falls, which are particularly detrimental to the elderly and disabled. Individuals that experience the greatest risks rely on Cane devices for support of ambulation. Results of many studies, however, have shown that incorrect Cane usage is prevalent among Cane users. The original SmartCane Assistive system has been developed to provide a method for acquiring detailed motion data from Cane usage. The Cane itself, however, lacks any type of programmability as well as real-time data processing algorithms to provide feedback to the Cane user. This paper describes the development of a real-time sensor information processing algorithm that provides direct detection of Cane usage characteristics. Specifically, it supports direct feedback to the Cane user, permitting guidance for proper Cane usage and reducing the risk of falls. This paper also aims to improve upon the existing system by incorporating MicroLEAP, an energy-aware embedded computing platform. The new system provides local data processing capability by classifying whether an individual is executing a stride with proper Cane motion and applied forces.
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IPSN - Demonstration of Active Guidance with SmartCane
2008 International Conference on Information Processing in Sensor Networks (ipsn 2008), 2008Co-Authors: Maxim Batalin, Thanos Stathopoulos, William J. KaiserAbstract:The usage of conventional Assistive Cane devices is critical in reducing the risk of falls, which are particularly detrimental for the elderly and disabled. Many of the individuals that experience the greatest risk of falling rely on Cane devices for support of ambulation. However, the results of many studies have shown that incorrect Cane usage is prevalent among Cane users. The original SmartCane Assistive system [4] has been developed to provide a method for acquiring detailed motion data from Cane usage. The Cane itself, however, lacks any type of programmability as well as real-time data processing algorithms to provide feedback to the Cane user. In this demonstration, we have incorporated an embedded computing platform into SmartCane [2] and developed a real-time sensor information processing algorithm that provides direct detection of Cane usage characteristics. The new system provides local data processing capability by classifying whether an individual is executing a stride with proper Cane motion and applied forces. It also provides direct feedback information to the individual, thereby guiding the subject towards proper Cane usage and reducing the risk of falls.
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BODYNETS - The SmartCane system: an Assistive device for geriatrics
Proceedings of the 3rd International ICST Conference on Body Area Networks, 2008Co-Authors: Brett Jordan, Maxim Batalin, William J. Kaiser, Thanos Stathopoulos, Alireza Vahdatpour, Majid Sarrafzadeh, Meika Fang, Joshua ChodoshAbstract:Falls are currently a leading cause of death from injury in the elderly. The usage of the conventional Assistive Cane devices is critical in reducing the risk of falls and is relied upon by over 4 million patients in the U.S.. While Canes provide physical support as well as supplementary sensing feedback to patients, at the same time, these conventional aids also exhibit serious adverse effects that contribute to falls. The falls due to the improper usage of the Canes are particularly acute in the elderly and disabled where reduced cognitive capacity accompanied by the burden of managing Cane motion leads to increased risk. This paper describes the development of the SmartCane Assistive system that encompasses broad engineering challenges that will impact general development of individualized, robust Assistive and prosthetic devices. The SmartCane system combines advances in signal processing, embedded computing, and wireless networking technology to provide capabilities for remote monitoring, local signal processing, and real-time feedback on the Cane usage. This system aims to reduce risks of injuries and falls by enabling training and guidance of patients in proper usage of Assistive devices.
Maxim Batalin - One of the best experts on this subject based on the ideXlab platform.
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Demonstration of active guidance with SmartCane
2015Co-Authors: Maxim Batalin, Thanos Stathopoulos, William J. KaiserAbstract:The usage of conventional Assistive Cane devices is criti-cal in reducing the risk of falls, which are particularly detri-mental for the elderly and disabled. Many of the individu-als that experience the greatest risk of falling rely on Cane devices for support of ambulation. However, the results of many studies have shown that incorrect Cane usage is preva-lent among Cane users. The original SmartCane Assistive system [4] has been developed to provide a method for ac-quiring detailed motion data from Cane usage. The Cane it-self, however, lacks any type of programmability as well as real-time data processing algorithms to provide feedback to the Cane user. In this demonstration, we have incorporated an embedded computing platform into SmartCane [2] and developed a real-time sensor information processing algo-rithm that provides direct detection of Cane usage charac-teristics. The new system provides local data processing capability by classifying whether an individual is execut-ing a stride with proper Cane motion and applied forces. It also provides direct feedback information to the individual, thereby guiding the subject towards proper Cane usage and reducing the risk of falls. 1
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Active guidance towards proper Cane usage
2008 5th International Summer School and Symposium on Medical Devices and Biosensors, 2008Co-Authors: Maxim Batalin, William J. KaiserAbstract:The usage of conventional Assistive Cane devices is critical in reducing the risk of falls, which are particularly detrimental to the elderly and disabled. Individuals that experience the greatest risks rely on Cane devices for support of ambulation. Results of many studies, however, have shown that incorrect Cane usage is prevalent among Cane users. The original SmartCane Assistive system has been developed to provide a method for acquiring detailed motion data from Cane usage. The Cane itself, however, lacks any type of programmability as well as real-time data processing algorithms to provide feedback to the Cane user. This paper describes the development of a real-time sensor information processing algorithm that provides direct detection of Cane usage characteristics. Specifically, it supports direct feedback to the Cane user, permitting guidance for proper Cane usage and reducing the risk of falls. This paper also aims to improve upon the existing system by incorporating MicroLEAP, an energy-aware embedded computing platform. The new system provides local data processing capability by classifying whether an individual is executing a stride with proper Cane motion and applied forces.
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IPSN - Demonstration of Active Guidance with SmartCane
2008 International Conference on Information Processing in Sensor Networks (ipsn 2008), 2008Co-Authors: Maxim Batalin, Thanos Stathopoulos, William J. KaiserAbstract:The usage of conventional Assistive Cane devices is critical in reducing the risk of falls, which are particularly detrimental for the elderly and disabled. Many of the individuals that experience the greatest risk of falling rely on Cane devices for support of ambulation. However, the results of many studies have shown that incorrect Cane usage is prevalent among Cane users. The original SmartCane Assistive system [4] has been developed to provide a method for acquiring detailed motion data from Cane usage. The Cane itself, however, lacks any type of programmability as well as real-time data processing algorithms to provide feedback to the Cane user. In this demonstration, we have incorporated an embedded computing platform into SmartCane [2] and developed a real-time sensor information processing algorithm that provides direct detection of Cane usage characteristics. The new system provides local data processing capability by classifying whether an individual is executing a stride with proper Cane motion and applied forces. It also provides direct feedback information to the individual, thereby guiding the subject towards proper Cane usage and reducing the risk of falls.
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BODYNETS - The SmartCane system: an Assistive device for geriatrics
Proceedings of the 3rd International ICST Conference on Body Area Networks, 2008Co-Authors: Brett Jordan, Maxim Batalin, William J. Kaiser, Thanos Stathopoulos, Alireza Vahdatpour, Majid Sarrafzadeh, Meika Fang, Joshua ChodoshAbstract:Falls are currently a leading cause of death from injury in the elderly. The usage of the conventional Assistive Cane devices is critical in reducing the risk of falls and is relied upon by over 4 million patients in the U.S.. While Canes provide physical support as well as supplementary sensing feedback to patients, at the same time, these conventional aids also exhibit serious adverse effects that contribute to falls. The falls due to the improper usage of the Canes are particularly acute in the elderly and disabled where reduced cognitive capacity accompanied by the burden of managing Cane motion leads to increased risk. This paper describes the development of the SmartCane Assistive system that encompasses broad engineering challenges that will impact general development of individualized, robust Assistive and prosthetic devices. The SmartCane system combines advances in signal processing, embedded computing, and wireless networking technology to provide capabilities for remote monitoring, local signal processing, and real-time feedback on the Cane usage. This system aims to reduce risks of injuries and falls by enabling training and guidance of patients in proper usage of Assistive devices.
Javier Moreno - One of the best experts on this subject based on the ideXlab platform.
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Extending the Application of an Assistant Personal Robot as a Walk-Helper Tool
Robotics, 2019Co-Authors: Jordi Palacín, Eduard Clotet, Dani Martínez, David Martínez, Javier MorenoAbstract:This paper presents the application of a mobile robot designed as an Assistant Personal Robot (APR) as a walk-helper tool. The hypothesis is that the height and weight of this mobile robot can be used also to provide a dynamic physical support and guidance to people while they walk. This functionality is presented as a soft walking aid at home but not as a substitute of an Assistive Cane or a walker device, which may withstand higher weights and provide better stability during a walking. The APR operates as a walk-helper tool by providing user interaction using the original arms of the mobile robot and by using the onboard sensors of the mobile robot in order to avoid obstacles and guide the walking through free areas. The results of the experiments conducted with the walk-helper have showed the automatic generation of smooth walking trajectories and a reduction in the number of manual trajectory corrections required to complete a walking displacement.
Moreno Blanc Javier - One of the best experts on this subject based on the ideXlab platform.
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Extending the Application of an Assistant Personal Robot as a Walk-Helper Tool
'MDPI AG', 2019Co-Authors: Palacín Roca Jordi, Clotet Bellmunt Eduard, Martínez Lacasa Daniel, Martínez David, Moreno Blanc JavierAbstract:This paper presents the application of a mobile robot designed as an Assistant Personal Robot (APR) as a walk-helper tool. The hypothesis is that the height and weight of this mobile robot can be used also to provide a dynamic physical support and guidance to people while they walk. This functionality is presented as a soft walking aid at home but not as a substitute of an Assistive Cane or a walker device, which may withstand higher weights and provide better stability during a walking. The APR operates as a walk-helper tool by providing user interaction using the original arms of the mobile robot and by using the onboard sensors of the mobile robot in order to avoid obstacles and guide the walking through free areas. The results of the experiments conducted with the walk-helper have showed the automatic generation of smooth walking trajectories and a reduction in the number of manual trajectory corrections required to complete a walking displacement.This work was partially funded by Indra and Adecco Fundation, accessibility grant 2017, the University of Lleida, UdL-Impuls Grant, the RecerCaixa 2013 grant, the Government of Catalonia (Comissionat per a Universitats i Recerca, Departament d’Innovació, Universitats i Empresa), and by the European Social Fund (ECO/1794/2015)
Thanos Stathopoulos - One of the best experts on this subject based on the ideXlab platform.
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Demonstration of active guidance with SmartCane
2015Co-Authors: Maxim Batalin, Thanos Stathopoulos, William J. KaiserAbstract:The usage of conventional Assistive Cane devices is criti-cal in reducing the risk of falls, which are particularly detri-mental for the elderly and disabled. Many of the individu-als that experience the greatest risk of falling rely on Cane devices for support of ambulation. However, the results of many studies have shown that incorrect Cane usage is preva-lent among Cane users. The original SmartCane Assistive system [4] has been developed to provide a method for ac-quiring detailed motion data from Cane usage. The Cane it-self, however, lacks any type of programmability as well as real-time data processing algorithms to provide feedback to the Cane user. In this demonstration, we have incorporated an embedded computing platform into SmartCane [2] and developed a real-time sensor information processing algo-rithm that provides direct detection of Cane usage charac-teristics. The new system provides local data processing capability by classifying whether an individual is execut-ing a stride with proper Cane motion and applied forces. It also provides direct feedback information to the individual, thereby guiding the subject towards proper Cane usage and reducing the risk of falls. 1
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ABSTRACT
2009Co-Authors: Brett Jordan, Thanos Stathopoulos, Alireza Vahdatpour, Maxim Batalin, William Kaiser, Majid SarrafzadehAbstract:Falls are currently a leading cause of death from injury in the elderly. The usage of conventional Assistive Cane devices is critical in reducing the risk of falls and is relied upon by over 4 million patients in the U.S. While Canes provide physical support as well as supplementary sensing feedback to patients, at the same time, these conventional aids also exhibit serious adverse effects that contribute to falls. The falls due to the improper usage of the Canes are particularly acute in the elderly and disabled where reduced cognitive capacity accompanied by the burden of managing Cane motion leads to increased risk. This paper describes the development of the SmartCane Assistive system that encompasses broad engineering challenges that will impact general development of individualized, robust Assistive and prosthetic devices. The SmartCane system combines advances in signal processing, embedded computing, and wireless networking technology to provide capabilities for remote monitoring, local signal processing, and real-time feedback on the Cane usage. This system aims to reduce risks of injuries and falls by enabling training and guidance of patients in proper usage of Assistive devices
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IPSN - Demonstration of Active Guidance with SmartCane
2008 International Conference on Information Processing in Sensor Networks (ipsn 2008), 2008Co-Authors: Maxim Batalin, Thanos Stathopoulos, William J. KaiserAbstract:The usage of conventional Assistive Cane devices is critical in reducing the risk of falls, which are particularly detrimental for the elderly and disabled. Many of the individuals that experience the greatest risk of falling rely on Cane devices for support of ambulation. However, the results of many studies have shown that incorrect Cane usage is prevalent among Cane users. The original SmartCane Assistive system [4] has been developed to provide a method for acquiring detailed motion data from Cane usage. The Cane itself, however, lacks any type of programmability as well as real-time data processing algorithms to provide feedback to the Cane user. In this demonstration, we have incorporated an embedded computing platform into SmartCane [2] and developed a real-time sensor information processing algorithm that provides direct detection of Cane usage characteristics. The new system provides local data processing capability by classifying whether an individual is executing a stride with proper Cane motion and applied forces. It also provides direct feedback information to the individual, thereby guiding the subject towards proper Cane usage and reducing the risk of falls.
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BODYNETS - The SmartCane system: an Assistive device for geriatrics
Proceedings of the 3rd International ICST Conference on Body Area Networks, 2008Co-Authors: Brett Jordan, Maxim Batalin, William J. Kaiser, Thanos Stathopoulos, Alireza Vahdatpour, Majid Sarrafzadeh, Meika Fang, Joshua ChodoshAbstract:Falls are currently a leading cause of death from injury in the elderly. The usage of the conventional Assistive Cane devices is critical in reducing the risk of falls and is relied upon by over 4 million patients in the U.S.. While Canes provide physical support as well as supplementary sensing feedback to patients, at the same time, these conventional aids also exhibit serious adverse effects that contribute to falls. The falls due to the improper usage of the Canes are particularly acute in the elderly and disabled where reduced cognitive capacity accompanied by the burden of managing Cane motion leads to increased risk. This paper describes the development of the SmartCane Assistive system that encompasses broad engineering challenges that will impact general development of individualized, robust Assistive and prosthetic devices. The SmartCane system combines advances in signal processing, embedded computing, and wireless networking technology to provide capabilities for remote monitoring, local signal processing, and real-time feedback on the Cane usage. This system aims to reduce risks of injuries and falls by enabling training and guidance of patients in proper usage of Assistive devices.
