Identifying time-varying neuromuscular system with a recursive least-squares algorithm: a Monte-Carlo simulation study

Olivari, Mario; Nieuwenhuizen, Frank M.; Bülthoff, HH; Pollini, Lorenzo

doi:10.1109/smc.2014.6974484

Cited by 5 publications

(4 citation statements)

References 14 publications

(31 reference statements)

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“…Examples of time-varying identification methods are those that rely on windowed LTI HC modeling [148], wavelets [148], [149], recursive least-squares [147], [150] or Kalman filtering [147], [151], [152]. Such methods are indispensable for studying what actually varies in HCs and which "function approximators" can best describe the adapting HC parameters.…”

Section: Time-varying Adaptationsmentioning

confidence: 99%

See 1 more Smart Citation

Manual Control Cybernetics: State-of-the-Art and Current Trends

Mulder

Pool

Abbink

et al. 2018

IEEE Trans. Human-Mach. Syst.

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Abstract-Manual control cybernetics aims to understand and describe how humans control vehicles and devices using mathematical models of human control dynamics. This 'cybernetic approach' enables objective and quantitative comparisons of human behavior, and allows a systematic optimization of human control interfaces and training associated with manual control. Current cybernetics theory is primarily based on technology and analysis methods formalized in the 1960s and has shown to be limited in its capability to capture the full breadth of human cognition and control. This paper reviews the current state-of-the-art in our knowledge of human manual control, points out the main fundamental limitations in cybernetics, and proposes a possible roadmap to advance the theory and its applications. Central in this roadmap will be a shift from the current linear time-invariant modeling approach that is only truly valid for human behavior under tightly controlled and stationary conditions, to methods that facilitate the analysis of adaptive, and possibly time-varying, human behavior in realistic control tasks. Examples of key current developments in the field of cybernetics -human use of preview, predictable discrete maneuvering, skill acquisition and training, time-varying human modeling, and neuromuscular system modeling -that contribute to this shift are presented in this paper. The new foundations for cybernetics that will emerge from these efforts will impact all domains that involve humans in manual and semi-automatic control.

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Section: Time-varying Adaptationsmentioning

confidence: 99%

“…Recent approaches used small-window FRFs [42], wavelets [156], recursive least-squares algorithms [150], and LPV methods [135], [157]. The second goal requires perturbation techniques to estimate endpoint admittance, which do not significantly influence manual control behavior.…”

Section: A Haptic Shared Control/neuromuscular Adaptationsmentioning

confidence: 99%

Manual Control Cybernetics: State-of-the-Art and Current Trends

Mulder

Pool

Abbink

et al. 2018

IEEE Trans. Human-Mach. Syst.

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“…As shown in this paper, using task-dependent parameters -i.e., a reduction in the gain of the BDFT model (G BDFT ) in the step task, resulting in an average BDFT reduction of 12% instead of a factor 2 amplification -can be sufficient to render a mismatched mitigation effective again. Potential approaches that can facilitate real-time adaptation of BDFT model parameters are, for example, explicit online estimators for the BDFT model's parameters (Olivari et al, 2014;Plaetinck et al, 2019) or predictive methods based on motion tracking (Ahmad et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Mitigation of Biodynamic Feedthrough for Touchscreens on the Flight Deck

Khoshnewiszadeh

Pool

2021

International Journal of Human–Computer Interaction

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Biodynamic feedthrough (BDFT) is a key issue for touchscreen operations on the future flight deck, as cockpit accelerations due to turbulence leave pilots vulnerable to erroneous touches that disrupt task performance. This research focuses on the implementation of a software-based cancellation approach to mitigate the adverse effects of BDFT in touchscreen dragging tasks. A flight-simulator experiment with 18 participants was performed to estimate models of BDFT dynamics for horizontal and vertical touchinputs on a primary flight display. The averaged BDFT models were used to cancel BDFT in the same continuous dragging task used for model identification and a discrete point-to-point dragging task. While for the continuous task the cancellation enabled 63% mitigation in BDFT, the same cancellation was ineffective for the discrete task, due to reduced BDFT susceptibility. Overall, the results show that while model-based BDFT cancellation can be highly effective, a key technical challenge will be ensuring it is sufficiently task-adaptive.

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“…For example, the biomechanical properties of the driver for a vehicle steering task is considered a novel technique of estimating the admittance and total response [3]. By investigating the influence of haptic aids on the pilot's NMS response, an online estimator of the time-varying NMS dynamic method based on Recursive Least Squares is proposed [4,5]. On the basis of the electric power-assisted steering system, Marouf A et al [6] proposed a time-varying method based on a sliding mode observer and the exponentially 2 of 20 weighted recursive least squares (EWRLS) algorithm for the estimation of the viscoelastic properties in the driver's arm.…”

Section: Introductionmentioning

confidence: 99%

A Steering-Following Dynamic Model with Driver’s NMS Characteristic for Human-Vehicle Shared Control

Wei

Xing

et al. 2020

Applied Sciences

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For investigating driver characteristic as well as control authority allocation during the process of human–vehicle shared control (HVSC) for an autonomous vehicle (AV), a HVSC dynamic mode with a driver’s neuromuscular (NMS) state parameters was proposed in this paper. It takes into account the driver’s NMS characteristics such as stretch reflection and reflex stiffness. By designing a model predictive control (MPC) controller, the vehicle’s state feedback and driver’s state are incorporated to construct the HVSC dynamic model. For the validation of the model, a field experiment was conducted. The vehicle state signals are collected by V-BOX, and the driver’s state signals are obtained with the electromyography instrument. Subsequently, the hierarchical least square (HLS) parameter identification algorithm was implemented to identify the parameters of the model based on the experimental results. Moreover, the Unscented Kalman Filter (UKF) was utilized to estimate the important NMS parameters which cannot be measured directly. The experimental results showed that the model we proposed has excellent accuracy in characterizing the vehicle’s dynamic state and estimating the driver’s NMS parameter. This paper will serve as a theoretical basis for the new control strategy allocation between human and vehicle for L3 class AVs.

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Identifying time-varying neuromuscular system with a recursive least-squares algorithm: a Monte-Carlo simulation study

Cited by 5 publications

References 14 publications

Manual Control Cybernetics: State-of-the-Art and Current Trends

Manual Control Cybernetics: State-of-the-Art and Current Trends

Mitigation of Biodynamic Feedthrough for Touchscreens on the Flight Deck

A Steering-Following Dynamic Model with Driver’s NMS Characteristic for Human-Vehicle Shared Control

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