Human Response Delay Estimation and Monitoring Using Gamma Distribution Analysis

Martínez-García, Miguel; Gordon, Timothy

doi:10.1109/smc.2018.00145

Cited by 10 publications

(11 citation statements)

References 16 publications

(14 reference statements)

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“…The human response delay, here denoted by the parameter τ , was included in the model to reflect the effective time delay with the additional transport delays during the control task. This value was determined in Zhang et al (2018) for every subject and for the same data used in this study. Accordingly, for this study, τ was pre-fixed with respect to the human subject’s output for each controlled plant G α ( s ) defined in Equation 2 and averagely was 199 ms for joystick control and 272 ms for steering wheel control.…”

Section: Methodsmentioning

confidence: 99%

Memory Pattern Identification for Feedback Tracking Control in Human–Machine Systems

2019

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Objective: The aim of this paper was to identify the characteristics of memory patterns with respect to a visual input, perceived by the human operator during a manual control task, which consisted in following a moving target on a display with a cursor.Background: Manual control tasks involve nondeclarative memory. The memory encodings of different motor skills have been referred to as procedural memories.The procedural memories have a pattern, which this paper sought to identify for the particular case of a onedimensional tracking task. Specifically, data recorded from human subjects controlling dynamical systems with different fractional order were investigated.Method: A Finite Impulse Response (FIR) controller was fitted to the data, and pattern analysis was performed to the fitted parameters. Then, the FIR model was further reduced to a lower order controller; from the simplified model, the stability analysis of the human-machine system in closedloop was conducted.Results: It is shown that the FIR model can be employed to identify and represent patterns in human procedural memories during manual control tasks. The obtained procedural memory pattern presents a time scale of about 650 ms before decay. Furthermore, the fitted controller is stable for systems with fractional order less or equal to 1. Conclusion: For systems of different fractional order, the proposed control scheme -based on a FIR modelcan effectively characterize the linear properties of manual control in humans. Application: This research supports a biofidelic approach to human manual control modeling over feedback visual perceptions. Relevant applications of this research are: the development of shared-control systems, where a virtual human model assists the human during a control task, and human operator state monitoring.

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Section: Methodsmentioning

confidence: 99%

Memory Pattern Identification for Feedback Tracking Control in Human–Machine Systems

2019

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“…The communication effectiveness between a supervisory computer and PLCs in practice takes into account both the protocol's transmission efficiency [27] and the human visual response delay [13,28,29]. The communication timeline designs for PLC visual monitoring focus on two aspects, including transmission load and real-time response.…”

Section: Dynamic Optimization Of Data Packet-based Communication For mentioning

confidence: 99%

“…The human cognitive response involves a complex psychological process, and it is commonly believed that different people have different response times towards the same situation and even the same person may have different response times in different situations. In addition, recent research also demonstrates that human responses can be highly correlated to the fractional-order representation of visual cues [14], and the human response delay can only be approximated by a Gamma distribution [29]. As a result, it is difficult to measure the human response delay in a precise manner.…”

Section: Descriptions Of Data Packet-based Communicationmentioning

confidence: 99%

“…It has been proven in Section 2 that the optimized packet size of packing P * successive sub-networks can be identified from a candidate set by taking into account the real-time packet loss or retransmission rate δ and the human response time limit T R . In the experimental studies, the maximum human response time T R is set to be 200 ms as a benchmark, rather than a precise measurement [13,29]. Furthermore, a larger P * can be achieved by tightening the upper bound of the length of sub-networks in the present monitoring view.…”

Section: Initialization and Optimization Of Communication Packet Sizementioning

confidence: 99%

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Dynamic Optimization of Data Packet-based Communication for PLC Visual Monitoring

Yang

Chen

et al. 2019

Applied Sciences

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This paper presents a dynamic optimization solution for the data packet-based communication of programmable logic controller (PLC) visual monitoring, and the objective is to improve the communication efficiency between a supervisory computer and field PLCs in consideration of the network communication performance, such as network bandwidth, packet loss or retransmission rate, and human response delay. A nonlinear optimization model with constraints is formulated and solved analytically to obtain the minimized transmission time. Experimental results demonstrate that the proposed dynamic optimization can solve the practical industry problem in an efficient and effective manner, and it can provide useful guidance for the data packet-based communication setting of networked control systems.

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“…Overall, we believe that our stochastic model can help us better understand the impact of noise on neural correlates of consciousness. Understanding the impact of noise on perception can help us deduce processes related to conscious perception, apply this knowledge to modulate behavioral responses [ 17 , 18 ] and design computer vision systems [ 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

Implications of Noise on Neural Correlates of Consciousness: A Computational Analysis of Stochastic Systems of Mutually Connected Processes

Kraikivski

2021

Entropy

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Random fluctuations in neuronal processes may contribute to variability in perception and increase the information capacity of neuronal networks. Various sources of random processes have been characterized in the nervous system on different levels. However, in the context of neural correlates of consciousness, the robustness of mechanisms of conscious perception against inherent noise in neural dynamical systems is poorly understood. In this paper, a stochastic model is developed to study the implications of noise on dynamical systems that mimic neural correlates of consciousness. We computed power spectral densities and spectral entropy values for dynamical systems that contain a number of mutually connected processes. Interestingly, we found that spectral entropy decreases linearly as the number of processes within the system doubles. Further, power spectral density frequencies shift to higher values as system size increases, revealing an increasing impact of negative feedback loops and regulations on the dynamics of larger systems. Overall, our stochastic modeling and analysis results reveal that large dynamical systems of mutually connected and negatively regulated processes are more robust against inherent noise than small systems.

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Human Response Delay Estimation and Monitoring Using Gamma Distribution Analysis

Abstract: Human response delay estimation and monitoring using gamma distribution Human response delay estimation and monitoring using gamma distribution analysis analysis PLEASE CITE THE PUBLISHED VERSION

Cited by 10 publications

References 16 publications

Memory Pattern Identification for Feedback Tracking Control in Human–Machine Systems

Memory Pattern Identification for Feedback Tracking Control in Human–Machine Systems

Dynamic Optimization of Data Packet-based Communication for PLC Visual Monitoring

Implications of Noise on Neural Correlates of Consciousness: A Computational Analysis of Stochastic Systems of Mutually Connected Processes

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