Progressive shared control for training in virtual environments

Li, Yanfang; Huegel, Joel C.; Patoğlu, Volkan; O’Malley, Marcia K.

doi:10.1109/whc.2009.4810873

Cited by 44 publications

(35 citation statements)

References 17 publications

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“…Performance of the manual control task used in our prior studies is influenced by the participant's ability to perform system identification in order to excite the virtual dynamic system near its resonant frequency [12]. Our long-term goal is to understand the participants' ability to identify dynamics of external systems in order to improve the performance of the shared controller for training in haptic virtual environments.…”

Section: Introductionmentioning

confidence: 99%

“…However, the error-reducing shared controller did not have a significant effect on task performance after a month-long training protocol. We concluded that assistance designed with knowledge of the task and an intuitive sense of the motions required to achieve good performance does not necessarily result in training efficacy, and shared controllers should be systematically designed to beneficially influence motor skill acquisition.Performance of the manual control task used in our prior studies is influenced by the participant's ability to perform system identification in order to excite the virtual dynamic system near its resonant frequency [12]. Our long-term goal is to understand the participants' ability to identify dynamics of external systems in order to improve the performance of the shared controller for training in haptic virtual environments.…”

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confidence: 99%

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Effects of magnitude and phase cues on human motor adaptation

Israr

Kapson

Patoğlu

et al. 2009

World Haptics 2009 - Third Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoper

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Recent findings have shown that humans can adapt their internal control model to account for the changing dynamics of systems they manipulate. In this paper, we explore the effects of magnitude and phase cues on human motor adaptation. In our experiments, participants excite virtual second-order systems at resonance via a two-degree of freedom haptic interface, with visual and visual plus haptic feedback conditions. Then, we change the virtual system parameters and observe the resulting motor adaptation in catch trials. Through four experimental conditions we demonstrate the effects of magnitude and phase cues on human motor adaptation. First, we show that humans adapt to a nominal virtual system resonant frequency. Second, humans shift to higher and lower natural frequencies during catch trials regardless of feedback modality and force cues. Third, participants can detect changes in natural frequency when gain, magnitude, and phase cues are manipulated independently. Fourth, participants are able to detect changes in natural frequency when the feedback (visual or visual plus haptic) is delayed such that the phase shift between the nominal system and catch trial system is zero. The persistent ability of participants to perform system identification of the dynamic systems which they control, regardless of the cue that is conveyed, demonstrates the human's versatility with regard to manual control situations. We intend to further investigate human motor adaptation and the time for adaptation in order to improve the efficacy of shared control methodologies for training and rehabilitation in haptic virtual environments. KEYWORDS:Rhythmic motion, motor adaptation, internal models, catch trials. INTRODUCTIONHumans frequently perform motor tasks that require interactions with external dynamic systems, such as driving a car or wielding a tool. Such systems may be underactuated or may have higher order control mappings, thereby requiring training in order for the human to learn proper control of the system [1,2]. For rhythmic tasks such as pumping a swing or bouncing a ball, the perception of the dynamic behavior of the external system directly affects the control input planned and executed by the user [3]. However, psychophysical analysis of actively controlled dynamic systems, which may shed light on the mechanisms used by humans to execute motor tasks, has received little attention. A broader understanding of human motor control could directly benefit researchers who develop training protocols or simulations to teach new motor skills.Virtual environments have been explored as a means to teach new motor skills in domains such as surgery, assembly, and pilot training. Haptic guidance schemes have been incorporated in virtual environments to improve performance and to reduce training duration and user workload. Virtual fixtures, record-and-play, shared control, and error-based guidance schemes have shown potential to improve user performance during task completion and to accelerate learning rates, by guiding the...

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Effects of magnitude and phase cues on human motor adaptation

Israr

Kapson

Patoğlu

et al. 2009

World Haptics 2009 - Third Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoper

Self Cite

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“…We plan to use the results of this study to design more intelligent shared control and haptic guidance algorithms to improve training effectiveness and efficiency for rhythmic manual control tasks. For example, the WFs determined here can guide progressive training routines that adjust assistance gains incrementally [31]. The findings indicate that haptic feedback, combined with visual feedback, can improve discrimination thresholds for low-frequency dynamic systems perceived passively, while discrimination of the dynamic behavior of actively excited systems does not show sensitivity to feedback modality.…”

Section: Discussionmentioning

confidence: 76%

“…The findings indicate that haptic feedback, combined with visual feedback, can improve discrimination thresholds for low-frequency dynamic systems perceived passively, while discrimination of the dynamic behavior of actively excited systems does not show sensitivity to feedback modality. This finding will influence shared controller design depending on the approach selected (e.g., record and replay methods that require passive excitation [32], [33], [34] versus virtual fixtures or active shared controllers that require active excitation [4], [5], [7], [8], [31], [35], [36]). The method of excitation alone does not show a significant effect on discrimination ability.…”

Section: Discussionmentioning

confidence: 99%

Passive and Active Discrimination of Natural Frequency of Virtual Dynamic Systems

Israr

Patoğlu

et al. 2009

IEEE Trans. Haptics

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Abstract-It has been shown that humans use combined feedforward and feedback control strategies when manipulating external dynamic systems and when exciting virtual dynamic systems at resonance and that they can tune their control parameters in response to changing natural frequencies. We present a study to determine the discrimination thresholds for the natural frequency of such resonant dynamic systems. Weber fractions (WF, %) are reported for the discrimination of 1, 2, 4, and 8 Hz natural frequencies. Participants were instructed either to passively perceive or actively excite the virtual system via a one degree-of-freedom haptic interface with visual and/or haptic feedback. The average WF for natural frequency ranged from 4% to 8.5% for 1, 2, and 4 Hz reference natural frequencies, while the WF was approximately 20% for systems with a reference natural frequency of 8 Hz. Results indicate that sensory feedback modality has a significant effect on WF during passive perception, but no significant effect in the active perception case. The data also suggest that discrimination sensitivity is not significantly affected by excitation mode. Finally, results for systems with equivalent natural frequencies but different spring stiffness indicate that participants do not discriminate natural frequency based on the maximum force magnitude perceived.

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“…[18], [24]), but also other scenarios in physical human-robot interaction (consider e.g. a mobility assistant [36], human-robot collaborative manipulation [15], [16], rehabilitation [37]) or training with a haptic aid [38]. While in our considered scenario we assume the path to be known to the assistance, in reality this can be easily realized using a path planning algorithm and a map of the environment.…”

Section: Problem Statement and Approachmentioning

confidence: 99%