Precision grip force dynamics: a system identification approach

Human tactile sensing and sensorimotor mechanism: from afferent tactile signals to efferent motor control

In tactile sensing, decoding the journey from afferent tactile signals to efferent motor commands is a significant challenge primarily due to the difficulty in capturing population-level afferent nerve signals during active touch. This study integrates a finite element hand model with a neural dynamic model by using microneurography data to predict neural responses based on contact biomechanics and membrane transduction dynamics. This research focuses specifically on tactile sensation and its direct translation into motor actions. Evaluations of muscle synergy during in -vivo experiments revealed transduction functions linking tactile signals and muscle activation. These functions suggest similar sensorimotor strategies for grasping influenced by object size and weight. The decoded transduction mechanism was validated by restoring human-like sensorimotor performance on a tendon-driven biomimetic hand. This research advances our understanding of translating tactile sensation into motor actions, offering valuable insights into prosthetic design, robotics, and the development of next-generation prosthetics with neuromorphic tactile feedback.

The Impact of Command-Following Task on Human-in-the-Loop Control Behavior

This article presents results from an experiment in which 44 human subjects interact with a dynamic system 40 times over a one-week period. The subjects are divided into four groups. All groups interact with the same dynamic system, but each group performs a different sequence of command-following tasks. All reference commands have frequency content between 0 and 0.5 Hz. We use a subsystem identification algorithm to estimate the control strategy (feedback and feedforward) that each subject uses on each trial. The experimental and identification results are used to examine the impact of the command-following tasks on the subjects' performance and the control strategies that the subjects learn. Results demonstrate that certain reference commands (e.g., a sum of sinusoids) are more difficult for subjects to learn to follow than others (e.g., a chirp), and the difference in difficulty is related to the subjects' ability to match the phase of the reference command. In addition, the identification results show that differences in command-following performance for different tasks can be attributed to three aspects of the subjects' identified controllers: 1) compensating for time delay in feedforward; 2) using a comparatively accurate approximation of the inverse dynamics in feedforward; and 3) using a feedback controller with comparatively high gain. Results also demonstrate that subjects generalize their control strategy when the command changes. Specifically, when the command changes, subjects maintain relatively high gain in feedback and retain their feedforward internal model of the inverse dynamics. Finally, we provide evidence that subjects use prediction of the command (if possible) to improve performance but that subjects can learn to improve performance without prediction. Specifically, subjects learn to use feedback controllers with comparatively high gain to improve performance even though the command is unpredictable.

The Impact of Nonminimum-Phase Zeros on Human-in-the-Loop Control Systems

We present results from an experiment in which 33 human subjects interact with a dynamic system 40 times over a one-week period. The subjects are divided into three groups. For each interaction, a subject performs a command-following task, where the reference command is the same for all trials and all subjects. However, each group interacts with a different dynamic system, which is represented by a transfer function. The transfer functions have the same poles but different zeros. One has a minimum-phase zero , another has a nonminimum-phase zero , and the last has a slower (i.e., closer to the imaginary axis) nonminimum-phase zero z_sn ∈ (0,z_n) . The experimental results show that nonminimum-phase zeros tend to make dynamic systems more difficult for humans to learn to control. We use a subsystem identification algorithm to identify the control strategy that each subject uses on each trial. The identification results show that the identified feedforward controllers approximate the inverse dynamics of the system with which the subjects interact better on the last trial than on the first trial. However, the subjects interacting with the minimum-phase system are able to approximate the inverse dynamics in feedforward more accurately than the subjects interacting with the nonminimum-phase system. This observation suggests that nonminimum-phase zeros are an impediment to approximating inverse dynamics in feedforward. Finally, we provide evidence that humans rely on feedforward-step-like-control strategies with systems (e.g., nonminimum-phase systems) for which it is difficult to approximate the inverse dynamics in feedforward.

The Roles of Feedback and Feedforward as Humans Learn to Control Unknown Dynamic Systems

We present results from an experiment in which human subjects interact with an unknown dynamic system 40 times during a two-week period. During each interaction, subjects are asked to perform a command-following (i.e., pursuit tracking) task. Each subject's performance at that task improves from the first trial to the last trial. For each trial, we use subsystem identification to estimate each subject's feedforward (or anticipatory) control, feedback (or reactive) control, and feedback time delay. Over the 40 trials, the magnitudes of the identified feedback controllers and the identified feedback time delays do not change significantly. In contrast, the identified feedforward controllers do change significantly. By the last trial, the average identified feedforward controller approximates the inverse of the dynamic system. This observation provides evidence that a fundamental component of human learning is updating the anticipatory control until it models the inverse dynamics.

Adaptive Human Control Gains during Precision Grip

Eight human test subjects attempted to track a desired position trajectory with an instrumented manipulandum (MN). The test subjects used the MN with three different levels of stiffness. A transfer function was developed to represent the human application of a precision grip from the data when the test subjects initially displaced the MN so as to learn the position mapping from the MN onto the display. Another transfer function was formed from the data of the remainder of the experiments, after significant displacement of the MN occurred. Both of these transfer functions accurately modelled the system dynamics for a portion of the experiments, but neither was accurate for the duration of the experiments because the human grip dynamics changed while learning the position mapping. Thus, an adaptive system model was developed to describe the learning process of the human test subjects as they displaced the MN in order to gain knowledge of the position mapping. The adaptive system model was subsequently validated following comparison with the human test subject data. An examination of the average absolute error between the position predicted by the adaptive model and the actual experimental data yielded an overall average error of 0.34mm for all three levels of stiffness.

Visuomotor optimality and its utility in parametrization of response

We present a method of characterizing visuomotor response by inferring subject-specific physiologically meaningful parameters within the framework of optimal control theory. The characterization of visuomotor response is of interest in the assessment of impairment and rehabilitation, the analysis of man-machine systems, and sensorimotor research. We model the visuomotor response as a linear quadratic Gaussian (LQG) controller, a Bayesian optimal state estimator in series with a linear quadratic regulator. Subjects used a modified computer mouse to attempt to keep a displayed cursor at a fixed desired location despite a Gaussian random disturbance and simple cursor dynamics. Nearly all subjects' behavior was consistent with the hypothesized optimality. Experimental data were used to fit an LQG model whose assumptions are simple and consistent with other sensorimotor work. The parametrization is parsimonious and yields quantities of clear physiological meaning: noise intensity, level of exertion, delay, and noise bandwidth. Significant variations in response were observed, consistent with signal-dependent noise and changes in exerted effort. This is a novel example of the role of optimal control theory in explaining variance in human visuomotor response. We also present technical improvements on the use of LQG in human operator modeling.

Exploring the human grip force system: a preliminary study

It has been postulated that cutaneo-receptors from the digits contribute to the control of manipulatory functions of the hand. However, few studies have examined the dynamics of the human grip force system (HGFS) in terms of the dynamic relationship between the vertical loads, taken as an input and the resulting grip force, taken as the output. This paper describes the experimental procedures we have developed to explore the HGFS and presents some initial experimental results. These demonstrate that the step response of the HGFS is biphasic. The first, short latency phase, likely involves only passive and intrinsic components. The second, longer latency phase is likely related to reflex mechanisms since it is preceded by a strong burst of EMG. Moreover, the HGFS response depended nonlinearly on the amplitude and direction of the load applied. These results indicate that further investigation of HGFS dynamics will require the use of nonlinear identification methods.

Adaptive force generation for precision-grip lifting by a spectral timing model of the cerebellum

We modeled adaptive generation of precision grip forces during object lifting. The model presented adjusts reactive and anticipatory grip forces to a level just above that needed to stabilize lifted objects in the hand. The model obeys principles of cerebellar structure and function by using slip sensations as error signals to adapt phasic motor commands to tonic force generators associated with output synergies controlling grip aperture. The learned phasic commands are weight- and texture-dependent. Simulations of the new circuit model reproduce key aspects of experimental observations of force application. Over learning trials, the onset of grip force buildup comes to lead the load force buildup, and the rate-of-rise of grip force, but not load force, scales inversely with the friction of the object.

Control strategies correcting inaccurately programmed fingertip forces: model predictions derived from human behavior

When picking up a familiar object between the index finger and the thumb, the motor commands are predetermined by the CNS to correspond to the frictional demand of the finger-object contact area. If the friction is less than expected, the object will start to slip out of the hand, giving rise to unexpected sensory information. Here we study the correction strategies of the motor system in response to an unexpected frictional demand. The motor commands to the mononeuron pool are estimated by a novel technique combining behavioral recordings and neuromuscular modelling. We first propose a mathematical model incorporating muscles, hand mechanics, and the action of lifting an object. A simple control system sends motor commands to and receives sensory signals from the model. We identify three factors influencing the efficiency of the correction: the time development of the motor command, the delay between the onset of the grip and load forces (GF-LF-delay), and how fast the lift is performed. A sensitivity analysis describes how these factors affect the ability to prevent or stop slipping and suggests an efficient control strategy that prepares and corrects for an altered frictional condition. We then analyzed fingertip grip and load forces (GF and LF) and position data from 200 lifts made by five healthy subjects. The friction was occasionally reduced, forcing an increase of the GF to prevent the object being dropped. The data were then analyzed by feeding it through the inverted model. This provided an estimate of the motor commands to the motoneuron pool. As suggested by the sensitivity analysis the GF-LF-delay was indeed used by the subjects to prevent slip. In agreement with recordings from neurons in the primary motor cortex of the monkey, a sharp burst in the estimated GF motor command (NGF) efficiently arrested any slip. The estimated motor commands indicate a control system that uses a small set of corrective commands, which together with the GF-LF-delay form efficient correction strategies. The selection of a strategy depends on the amount of tactile information reporting unexpected friction and how long it takes to arrive. We believe that this technique of estimating the motor commands behind the fingertip forces during a precision grip lift can provide a powerful tool for the investigation of the central control of the motor system.