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Adaptive Skill

The Experts below are selected from a list of 144 Experts worldwide ranked by ideXlab platform

Chunyi Su – 1st expert on this subject based on the ideXlab platform

  • Interface Design of a Physical Human–Robot Interaction System for Human Impedance Adaptive Skill Transfer
    IEEE Transactions on Automation Science and Engineering, 2020
    Co-Authors: Chenguang Yang, Chao Zeng, Peidong Liang, Zhijun Li, Ruifeng Li, Chunyi Su

    Abstract:

    It has been established that the transfer of human Adaptive impedance is of great significance for physical human–robot interaction (pHRI). By processing the electromyography (EMG) signals collected from human muscles, the limb impedance could be extracted and transferred to robots. The existing impedance transfer interfaces rely only on visual feedback and, thus, may be insufficient for Skill transfer in a sophisticated environment. In this paper, physical haptic feedback mechanism is introduced to result in muscle activity that would generate EMG signals in a natural manner, in order to achieve intuitive human impedance transfer through a designed coupling interface. Relevant processing methods are integrated into the system, including the spectral collaborative representation-based classifications method used for hand motion recognition; fast smooth envelop and dimensionality reduction algorithm for arm endpoint stiffness estimation. The tutor’s arm endpoint motion trajectory is directly transferred to the robot by the designed coupling module without the restriction of hands. Haptic feedback is provided to the human tutor according to Skill learning performance to enhance the teaching experience. The interface has been experimentally tested by a plugging-in task and a cutting task. Compared with the existing interfaces, the developed one has shown a better performance. Note to Practitioners —This paper is motivated by the limited performance of Skill transfer in the existing human–robot interfaces. Conventional robots perform tasks independently without interaction with humans. However, the new generation of robots with the characteristics, such as flexibility and compliance, become more involved in interacting with humans. Thus, advanced human robot interfaces are required to enable robots to learn human manipulation Skills. In this paper, we propose a novel interface for human impedance Adaptive Skill transfer in a natural and intuitive manner. The developed interface has the following functionalities: 1) it transfers human arm impedance Adaptive motion to the robot intuitively; 2) it senses human motion signals that are decoded into human hand gesture and arm endpoint stiffness that ia employed for natural human robot interaction; and 3) it provides human tutor haptic feedback for enhanced teaching experience. The interface can be potentially used in pHRI, teleoperation, human motor training systems, etc.

  • interface design of a physical human robot interaction system for human impedance Adaptive Skill transfer
    IEEE Transactions on Automation Science and Engineering, 2018
    Co-Authors: Chenguang Yang, Chao Zeng, Peidong Liang, Zhijun Li, Ruifeng Li, Chunyi Su

    Abstract:

    It has been established that the transfer of human Adaptive impedance is of great significance for physical human–robot interaction (pHRI). By processing the electromyography (EMG) signals collected from human muscles, the limb impedance could be extracted and transferred to robots. The existing impedance transfer interfaces rely only on visual feedback and, thus, may be insufficient for Skill transfer in a sophisticated environment. In this paper, physical haptic feedback mechanism is introduced to result in muscle activity that would generate EMG signals in a natural manner, in order to achieve intuitive human impedance transfer through a designed coupling interface. Relevant processing methods are integrated into the system, including the spectral collaborative representation-based classifications method used for hand motion recognition; fast smooth envelop and dimensionality reduction algorithm for arm endpoint stiffness estimation. The tutor’s arm endpoint motion trajectory is directly transferred to the robot by the designed coupling module without the restriction of hands. Haptic feedback is provided to the human tutor according to Skill learning performance to enhance the teaching experience. The interface has been experimentally tested by a plugging-in task and a cutting task. Compared with the existing interfaces, the developed one has shown a better performance. Note to Practitioners —This paper is motivated by the limited performance of Skill transfer in the existing human–robot interfaces. Conventional robots perform tasks independently without interaction with humans. However, the new generation of robots with the characteristics, such as flexibility and compliance, become more involved in interacting with humans. Thus, advanced human robot interfaces are required to enable robots to learn human manipulation Skills. In this paper, we propose a novel interface for human impedance Adaptive Skill transfer in a natural and intuitive manner. The developed interface has the following functionalities: 1) it transfers human arm impedance Adaptive motion to the robot intuitively; 2) it senses human motion signals that are decoded into human hand gesture and arm endpoint stiffness that ia employed for natural human robot interaction; and 3) it provides human tutor haptic feedback for enhanced teaching experience. The interface can be potentially used in pHRI, teleoperation, human motor training systems, etc.

  • Interface Design of a Physical Human–Robot Interaction System for Human Impedance Adaptive Skill Transfer
    IEEE Transactions on Automation Science and Engineering, 2018
    Co-Authors: Chenguang Yang, Chao Zeng, Peidong Liang, Zhijun Li, Ruifeng Li, Chunyi Su

    Abstract:

    It has been established that the transfer of human Adaptive impedance is of great significance for physical human-robot interaction (pHRI). By processing the electromyography (EMG) signals collected from human muscles, the limb impedance could be extracted and transferred to robots. The existing impedance transfer interfaces rely only on visual feedback and, thus, may be insufficient for Skill transfer in a sophisticated environment. In this paper, physical haptic feedback mechanism is introduced to result in muscle activity that would generate EMG signals in a natural manner, in order to achieve intuitive human impedance transfer through a designed coupling interface. Relevant processing methods are integrated into the system, including the spectral collaborative representation-based classifications method used for hand motion recognition; fast smooth envelop and dimensionality reduction algorithm for arm endpoint stiffness estimation. The tutor’s arm endpoint motion trajectory is directly transferred to the robot by the designed coupling module without the restriction of hands. Haptic feedback is provided to the human tutor according to Skill learning performance to enhance the teaching experience. The interface has been experimentally tested by a plugging-in task and a cutting task. Compared with the existing interfaces, the developed one has shown a better performance.

Allison A King – 2nd expert on this subject based on the ideXlab platform

  • maternal depression is associated with Adaptive Skill deficits of children with sickle cell disease through parenting practices
    Blood, 2009
    Co-Authors: Jennifer J Macdonald, Jennifer Rees, Gene H Brody, Yifu Chen, Desiree A White, Allison A King

    Abstract:

    Abstract 1397

    Poster Board I-419

    Background: Caregivers of children with sickle cell disease (SCD) have high levels of stress and depression. Students with SCD have a higher prevalence of behavioral and cognitive deficits compared to healthy students. Adaptive Skills are particularly important for children with chronic disease because they are the Skills needed to transition into independent adulthood. We hypothesize that (1) Maternal depressive symptoms are associated with decreased Adaptive Skills in children with SCD, and (2) This association will be mediated by the association between maternal depression and the provision of lower levels of competence promoting parenting.

    Methods: We completed a cross-sectional analysis of a single center prospective cohort study. Adaptive Skills of children with SCD were assessed by parent report of the Behavior Assessment System for Children (BASC). The BASC reflects the child’s Adaptive Skills by 5 key Adaptive scales: adaptability, activities of daily living, functional communication, social Skills, and leadership. Maternal risk for depression was assessed using the Center for Epidemiologic Studies Depression Scale (CES-D). We completed structured interviews of the mothers to assess parenting quality. Effective parenting was characterized by high levels of support, involvement, monitoring, and low levels of ongoing conflict. A path analysis using ordinary least squares was used for statistical analysis.

    Results: 48 children with SCD and their mothers were evaluated. 52% of the children were male; mean age was 12 yrs (Range 6-16). 25% of the children repeated a grade level in school. The mothers’ mean age was 38 yrs (Range 27-59) and the average yearly household income per capita was $7,133 (Range 708- 22,800). 60% of the children received healthcare via Medicaid. 20% of the mothers were at risk for depression.12.5% of the children had clinically significant deficits in Adaptive Skills and18.8% of the children were considered “at risk.” There was a moderate correlation between maternal depression and child Adaptive Skills (r = .481, p=.01). A path analysis revealed that in the presence of parenting, the association between maternal depression and child Adaptive Skills is no longer significant. The effect of maternal depression is mediated by depression induced decrements in competence promoting parenting practices ([Fig. 1][1]). Medicaid was associated with higher maternal depressive symptoms.

    Conclusions: Our preliminary data provide direct evidence that maternal depression is associated with proximal parenting practices that are associated with child Adaptive Skills. Parenting practices can be modified through education and family support and serve as a potential intervention target for moderating the effects of maternal depression on child Adaptive Skills in this vulnerable population.

    ![Figure 1][2]

    Figure 1
    Path analysis of maternal depression, parenting and child Adaptive Skills.

    ** P<.01,* P<.05 (two-tail);, CFI=.92, IFI=.93, n=48. Maternal depressive symptoms have a negative association with proximal parenting processes that are linked to Adaptive Skills in children with SCD. Stronger parenting indices are associated with better Adaptive Skills. In the presence of parenting, the direct association between maternal depression and Adaptive Skills is no longer significant.

    Disclosures: Brody : NIH: Research Funding. White : NIH: Research Funding. King : NIH-NHLBI: Research Funding; Doris Duke Charitable Foundation: Research Funding.

    [1]: #F1
    [2]: pending:yes

  • Maternal Depression Is Associated with Adaptive Skill Deficits of Children with Sickle Cell Disease through Parenting Practices.
    Blood, 2009
    Co-Authors: Jennifer J Macdonald, Jennifer Rees, Gene H Brody, Yifu Chen, Desiree A White, Allison A King

    Abstract:

    Abstract 1397

    Poster Board I-419

    Background: Caregivers of children with sickle cell disease (SCD) have high levels of stress and depression. Students with SCD have a higher prevalence of behavioral and cognitive deficits compared to healthy students. Adaptive Skills are particularly important for children with chronic disease because they are the Skills needed to transition into independent adulthood. We hypothesize that (1) Maternal depressive symptoms are associated with decreased Adaptive Skills in children with SCD, and (2) This association will be mediated by the association between maternal depression and the provision of lower levels of competence promoting parenting.

    Methods: We completed a cross-sectional analysis of a single center prospective cohort study. Adaptive Skills of children with SCD were assessed by parent report of the Behavior Assessment System for Children (BASC). The BASC reflects the child’s Adaptive Skills by 5 key Adaptive scales: adaptability, activities of daily living, functional communication, social Skills, and leadership. Maternal risk for depression was assessed using the Center for Epidemiologic Studies Depression Scale (CES-D). We completed structured interviews of the mothers to assess parenting quality. Effective parenting was characterized by high levels of support, involvement, monitoring, and low levels of ongoing conflict. A path analysis using ordinary least squares was used for statistical analysis.

    Results: 48 children with SCD and their mothers were evaluated. 52% of the children were male; mean age was 12 yrs (Range 6-16). 25% of the children repeated a grade level in school. The mothers’ mean age was 38 yrs (Range 27-59) and the average yearly household income per capita was $7,133 (Range 708- 22,800). 60% of the children received healthcare via Medicaid. 20% of the mothers were at risk for depression.12.5% of the children had clinically significant deficits in Adaptive Skills and18.8% of the children were considered “at risk.” There was a moderate correlation between maternal depression and child Adaptive Skills (r = .481, p=.01). A path analysis revealed that in the presence of parenting, the association between maternal depression and child Adaptive Skills is no longer significant. The effect of maternal depression is mediated by depression induced decrements in competence promoting parenting practices ([Fig. 1][1]). Medicaid was associated with higher maternal depressive symptoms.

    Conclusions: Our preliminary data provide direct evidence that maternal depression is associated with proximal parenting practices that are associated with child Adaptive Skills. Parenting practices can be modified through education and family support and serve as a potential intervention target for moderating the effects of maternal depression on child Adaptive Skills in this vulnerable population.

    ![Figure 1][2]

    Figure 1
    Path analysis of maternal depression, parenting and child Adaptive Skills.

    ** P

Chenguang Yang – 3rd expert on this subject based on the ideXlab platform

  • Interface Design of a Physical Human–Robot Interaction System for Human Impedance Adaptive Skill Transfer
    IEEE Transactions on Automation Science and Engineering, 2020
    Co-Authors: Chenguang Yang, Chao Zeng, Peidong Liang, Zhijun Li, Ruifeng Li, Chunyi Su

    Abstract:

    It has been established that the transfer of human Adaptive impedance is of great significance for physical human–robot interaction (pHRI). By processing the electromyography (EMG) signals collected from human muscles, the limb impedance could be extracted and transferred to robots. The existing impedance transfer interfaces rely only on visual feedback and, thus, may be insufficient for Skill transfer in a sophisticated environment. In this paper, physical haptic feedback mechanism is introduced to result in muscle activity that would generate EMG signals in a natural manner, in order to achieve intuitive human impedance transfer through a designed coupling interface. Relevant processing methods are integrated into the system, including the spectral collaborative representation-based classifications method used for hand motion recognition; fast smooth envelop and dimensionality reduction algorithm for arm endpoint stiffness estimation. The tutor’s arm endpoint motion trajectory is directly transferred to the robot by the designed coupling module without the restriction of hands. Haptic feedback is provided to the human tutor according to Skill learning performance to enhance the teaching experience. The interface has been experimentally tested by a plugging-in task and a cutting task. Compared with the existing interfaces, the developed one has shown a better performance. Note to Practitioners —This paper is motivated by the limited performance of Skill transfer in the existing human–robot interfaces. Conventional robots perform tasks independently without interaction with humans. However, the new generation of robots with the characteristics, such as flexibility and compliance, become more involved in interacting with humans. Thus, advanced human robot interfaces are required to enable robots to learn human manipulation Skills. In this paper, we propose a novel interface for human impedance Adaptive Skill transfer in a natural and intuitive manner. The developed interface has the following functionalities: 1) it transfers human arm impedance Adaptive motion to the robot intuitively; 2) it senses human motion signals that are decoded into human hand gesture and arm endpoint stiffness that ia employed for natural human robot interaction; and 3) it provides human tutor haptic feedback for enhanced teaching experience. The interface can be potentially used in pHRI, teleoperation, human motor training systems, etc.

  • interface design of a physical human robot interaction system for human impedance Adaptive Skill transfer
    IEEE Transactions on Automation Science and Engineering, 2018
    Co-Authors: Chenguang Yang, Chao Zeng, Peidong Liang, Zhijun Li, Ruifeng Li, Chunyi Su

    Abstract:

    It has been established that the transfer of human Adaptive impedance is of great significance for physical human–robot interaction (pHRI). By processing the electromyography (EMG) signals collected from human muscles, the limb impedance could be extracted and transferred to robots. The existing impedance transfer interfaces rely only on visual feedback and, thus, may be insufficient for Skill transfer in a sophisticated environment. In this paper, physical haptic feedback mechanism is introduced to result in muscle activity that would generate EMG signals in a natural manner, in order to achieve intuitive human impedance transfer through a designed coupling interface. Relevant processing methods are integrated into the system, including the spectral collaborative representation-based classifications method used for hand motion recognition; fast smooth envelop and dimensionality reduction algorithm for arm endpoint stiffness estimation. The tutor’s arm endpoint motion trajectory is directly transferred to the robot by the designed coupling module without the restriction of hands. Haptic feedback is provided to the human tutor according to Skill learning performance to enhance the teaching experience. The interface has been experimentally tested by a plugging-in task and a cutting task. Compared with the existing interfaces, the developed one has shown a better performance. Note to Practitioners —This paper is motivated by the limited performance of Skill transfer in the existing human–robot interfaces. Conventional robots perform tasks independently without interaction with humans. However, the new generation of robots with the characteristics, such as flexibility and compliance, become more involved in interacting with humans. Thus, advanced human robot interfaces are required to enable robots to learn human manipulation Skills. In this paper, we propose a novel interface for human impedance Adaptive Skill transfer in a natural and intuitive manner. The developed interface has the following functionalities: 1) it transfers human arm impedance Adaptive motion to the robot intuitively; 2) it senses human motion signals that are decoded into human hand gesture and arm endpoint stiffness that ia employed for natural human robot interaction; and 3) it provides human tutor haptic feedback for enhanced teaching experience. The interface can be potentially used in pHRI, teleoperation, human motor training systems, etc.

  • Interface Design of a Physical Human–Robot Interaction System for Human Impedance Adaptive Skill Transfer
    IEEE Transactions on Automation Science and Engineering, 2018
    Co-Authors: Chenguang Yang, Chao Zeng, Peidong Liang, Zhijun Li, Ruifeng Li, Chunyi Su

    Abstract:

    It has been established that the transfer of human Adaptive impedance is of great significance for physical human-robot interaction (pHRI). By processing the electromyography (EMG) signals collected from human muscles, the limb impedance could be extracted and transferred to robots. The existing impedance transfer interfaces rely only on visual feedback and, thus, may be insufficient for Skill transfer in a sophisticated environment. In this paper, physical haptic feedback mechanism is introduced to result in muscle activity that would generate EMG signals in a natural manner, in order to achieve intuitive human impedance transfer through a designed coupling interface. Relevant processing methods are integrated into the system, including the spectral collaborative representation-based classifications method used for hand motion recognition; fast smooth envelop and dimensionality reduction algorithm for arm endpoint stiffness estimation. The tutor’s arm endpoint motion trajectory is directly transferred to the robot by the designed coupling module without the restriction of hands. Haptic feedback is provided to the human tutor according to Skill learning performance to enhance the teaching experience. The interface has been experimentally tested by a plugging-in task and a cutting task. Compared with the existing interfaces, the developed one has shown a better performance.