The authors have no acknowledgements.

The protocol described here followed the guidelines of the ethics committee of the University of Freiburg and was in accordance with the declaration of Helsinki (1964).
1. Ethical Approval - Subject Instruction
<ol>
	<li>Before the actual experiment, instruct all subjects about the purpose of the study and potential risk factors. When applying transcranial magnetic stimulation (TMS), there are some medical risks including any history of epileptic seizures, metal implants in eyes and/or head, diseases of the cardiovascular system and pregnancy. Exclude any subject affirming to one of these risk factors from the study.</li>
	<li>Include only healthy individuals in the study. Exclude individuals with any neurological, mental and/or orthopaedic diseases.</li>
</ol>
2. Subject Preparation
<ol>
	<li>Subject placement
	<ol>
		<li>Throughout the entire experiment, seat subjects in a comfortable chair. Fix the head of the participant using a cast embracing the neck, ensuring a stable head position and avoiding any movements of the head relative the TMS coil.</li>
		<li>Place the right arm of the subjects in a custom-built arm rest to minimize movements of the wrist. Fix the subject&#39;s right index finger to a splint mounted to the arm of a robot. Align the axis of rotation of the robot arm with the metacarpophangeal joint of the right hand so that the joint centre matches the rotational centre of the robot.</li>
	</ol>
	</li>
	<li>Force recordings
	<ol>
		<li>Measure the force applied by the subjects by a torquemeter mounted in the robot arm and measure the position of the robot arm (corresponding to the position of the index finger) by a potentiometer connected to the rotational axis of the robot 22.</li>
	</ol>
	</li>
	<li>Electromyography (EMG)
	<ol>
		<li>Use a bipolar configuration of surface electrodes to measure electrophysiological responses elicited by TMS as well as muscular activation produced by the subjects.
		<ol>
			<li>Before attaching the electrodes to the skin over the first dorsal interosseus muscle (FDI) and the abductor pollicis brevis (APB) of the right hand, shave the skin of the subjects, then slightly abrade it using sandpaper or abrading gel and disinfect it with propanol.</li>
			<li>Following this, attach self-adhesive EMG electrodes to the skin over the muscle bellies of the FDI and APB. Place an additional reference electrode on the olecranon of the same arm.</li>
			<li>Cable-connect all electrodes to an EMG amplifier and to an analogue-digital converter. Amplify the EMG signals (x 1,000), bandpass-filter (10 - 1,000 Hz) and sample at 4 kHz. Store the EMG signals for offline analysis.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>TMS
	<ol>
		<li>Use a figure of eight coil attached to a TMS stimulator to stimulate the contralateral motor cortical hand area.</li>
		<li>Find the optimal position of the coil relative to the scalp for eliciting motor evoked potentials (MEPs) in the FDI muscle by a mapping procedure:
		<ol>
			<li>Place the coil approximately 0.5 cm anterior to the vertex and over the midline with the handle pointing at 45&#176; anticlockwise relative to the sagittal plane, inducing a posterior-anterior flow of the current in the centre of the coil.</li>
			<li>At the beginning, choose a small stimulation (e.g., below 30% maximum stimulator output, MSO) intensity to get the subjects accustomed to the magnetic pulses.</li>
			<li>Subsequently, increase the stimulation intensity in small steps, for example 2 - 3% maximum stimulator output (MSO) and move the coil in the frontal-rostral and medio-lateral direction in order to find the optimal site (hotspot) for stimulating the FDI muscle. The hotspot is defined as the place where the greatest MEP can be observed at a given stimulation intensity.</li>
		</ol>
		</li>
		<li>After finding the FDI hotspot, determine resting motor threshold (MT) as the minimum intensity required to evoke MEP peak-to-peak amplitudes in the EMG larger than 50 &#181;V in three out of five consecutive trials 18. Inspect the size of the MEPs displayed online on the computer screen.</li>
		<li>After eliciting MEPs with 1.0*MT, constantly decrease the stimulation intensity of the TMS machine in steps of 2% MSO until the MEP can no longer be observed and an EMG suppression of the ongoing muscle activity becomes apparent. 
		Note: In order to depict the TMS induced EMG suppression it is necessary to apply a high number of stimulations (see section 5. &#34;Data Processing&#34;)</li>
	</ol>
	</li>
</ol>
3. Feedback Presentation
<ol>
	<li>Divide the participants into three groups (pF, fF, CON).</li>
	<li>Instruct subjects from the position feedback group (pF) in half of the trials to receive feedback about the position of the index finger (position feedback) when moving the index finger by pressing against the robotic device.</li>
	<li>In the other half of the trials, instruct subjects to receive feedback about the applied force while moving the robotic device (force feedback). 
	Note: In reality, however, they always receive the same feedback (position feedback).</li>
	<li>Instruct subjects from the force feedback group (fF) to receive force feedback in half of the trials and receive position feedback in the other half. 
	Note: In fact, this group is solely provided with force feedback.</li>
	<li>Do not instruct the control group (CON) about the source of the feedback. Note: The control group receives force feedback in one half of their trials and position feedback in the other half.</li>
	<li>Randomly alter the order of the sessions, that is, whether trials start with force or position feedback, in all groups.</li>
	<li>Visually display the force and the position feedback on a computer screen placed 1 m in front of the subjects.</li>
	<li>In each condition, present a target line corresponding to 30% of the subject&#39;s individual maximal voluntary force, or the finger angle of the index finger at 30% maximally voluntary contraction (MVC), on the computer screen and instruct the subject to match the target line as closely as possible.</li>
</ol>
4. Maximal Isometric Force
<ol>
	<li>After the subject is prepared (EMG), perform three isometric maximum voluntary contractions (MVC), consisting of a gradual increase in isometric force from zero to maximum over a 3 sec time span and the maximal force held for 2 sec 20,21.</li>
	<li>Verbally encourage the subject to achieve maximal force. After each trial, allow the subjects to rest for 90 sec to avoid fatigue.</li>
</ol>
5. Experimental Procedure
<ol>
	<li>Fatiguing Motor Task- Sustained contractions. 
	Note: The fatiguing task consists of two sustained contractions executed on separate days.
	<ol>
		<li>Instruct the subjects to match the target line of 30% MVC for as long as possible with a line corresponding to the applied force or the position of their finger corresponding to a force level of 30% MVC. 
		Note: The target line during the position feedback condition (pF-group) therefore corresponds to the finger angle when subjects match the force level of 30% MVC.</li>
		<li>Ask the subjects to hold the contractions until task failure, which is defined as the point where the subjects are no longer able to hold the target force inside a 5% window of the target force over a period of 5 sec (fF-group). For the pF-group, define task failure as when the participants are unable to maintain the finger angle within 5 % of the required target angle for 5 sec 12,23.</li>
		<li>Ensure that the two sustained contractions are separated by at least 48 hr.</li>
	</ol>
	</li>
	<li>TMS-protocol 
	Note: The subthreshold TMS experiment is carried out on separate day to the fatiguing contractions. This is important as fatigue has an influence on the EMG suppression evoked by subTMS 24,25 so differences between force and position cannot be clearly identified. Separating the fatiguing contractions from the TMS measurements has the advantage that differences in the EMG suppression can now be clearly be attributed to the different interpretation of the feedback but has the limitation that the results can not directly be linked to the differences in the time to fatigue of the sustained contractions.
	<ol>
		<li>Conduct the part of the experiment using TMS (see also section 3. &#34;Feedback presentation&#34;) on a separate occasion than the fatiguing experiments. Initially, follow the exact same procedure as for the fatiguing contraction (e.g., MVC contractions) but this time, ask the subjects to hold the contractions only as long as the TMS stimulation lasts. Thus, the contractions are not fatigable and only held for approximately 100 sec during each TMS trial.</li>
		<li>Provide a break of 3 min between trials to minimize any bias of fatigue.</li>
	</ol>
	</li>
</ol>
6. Data Processing
<ol>
	<li>TMS
	<ol>
		<li>Apply a total of 100 sweeps, 50 sweeps with and 50 sweeps without stimulation, with an inter-stimulus interval ranging from 0.8 to 1.1 s 20,21,25,26. This short interstimulus interval makes sure that the subjects do not need to hold the contractions for too long so fatiguing effects can be minimized.</li>
		<li>To analyse if the TMS stimulation caused a facilitation (MEP) or an EMG suppression, subtract the rectified and then average 50 sweeps with stimulation (stimulated EMG) from the 50 sweeps without stimulation (control EMG) 20,21,25-27. 
		Note: The onset of the EMG suppression is defined as the time point where the averaged EMG for the sweeps with the stimulation is less than the control EMG for at least 4 msec in a time frame of 20 to 50 msec after the TMS pulse. The end of the suppression is defined as the instant when the stimulated EMG is greater than the control EMG for at least 1 msec and the extent of the suppression is calculated as percentage change (control-stimulated/meancontrol*100).</li>
		<li>Use the sweeps without TMS stimulation for the calculation of the background EMG activation and average them over the same time window as the trials with stimulation 20,21,25,26.</li>
	</ol>
	</li>
	<li>EMG
	<ol>
		<li>Determine the maximal EMG activity by calculating the root-mean-square value recorded in a 0.5s time window around the peak force measured during the MVC tests 20,21.</li>
		<li>For the sustained contractions, analyse the EMG by building 8 sec long bins where the root-mean-square of the rectified EMG is calculated and normalized to the EMG activity obtained during the MVC trials 20,21.</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

The present study investigated if the interpretation of augmented feedback influences the time to fatigue of a sustained submaximal contraction and the neural processing of the primary motor cortex. The results show that as soon as the participants interpreted the feedback as position feedback (compared to force feedback), the time to fatigue was significantly shorter and the inhibitory activity of the motor cortex (measured as the amount of EMG suppression caused by subTMS) is greater. As the task did not change between...

Sensory feedback is crucial to perform movements. Daily activities are hardly possible in the absence of proprioception 1. Furthermore, motor learning is influenced by proprioceptive integration 2 or cutaneous perception 3. Healthy humans with intact sensation are able to weight the sensory inputs arising from various sensory sources in order to meet situation-specific needs 4. This sensory weighing enables humans to perform difficult tasks with high precision even when some aspects of the sensory information are unreliable or even absent (e.g., walking in the dark or with eyes closed).
Additionally, various evidence suggests that providing augmented (or additional) feedback further improves motor control and/or motor learning. Augmented feedback provides additional information by an external source which can be added to the task intrinsic (sensory) feedback arising from the sensory system 5,6. Especially the effect of the content of augmented feedback on motor control and learning has been of great interest in recent years. One of the questions addressed was how humans control force and position 7,8. Initial investigations identified differences in time to fatigue of a sustained submaximal contraction using either position or force feedback and differences in load compliance (e.g., 9-12). When subjects were provided with force feedback, the time to fatigue of the sustained contraction was significantly longer compared to when position feedback was provided. The same phenomenon was observed for a variety of different muscles and limb positions and a number of neuromuscular mechanisms, including a greater rate of motor unit recruitment and a greater decrease in H-reflex area during the position controlled contraction (for review 13). However, in these studies, not only the visual feedback but also the physical characteristics of the muscular contraction (i.e., the compliance of the measurement device) was altered. Therefore, we recently conducted a study not altering compliance but only augmented feedback and provided evidence that provision of force and position feedback alone during a sustained submaximal contraction can cause differences in inhibitory activity within the primary motor cortex (M1). This was shown using a stimulation technique that is known to act solely at the cortical level 14, namely subthreshold transcranial magnetic stimulation (subTMS). Unlike suprathreshold TMS, the response evoked by subTMS, is not modulated by the excitability of spinal &#945;-motoneurons and the excitability excitatory neurons and/or cortical cells 15-17 but solely by the excitability of inhibitory intracortical neurons. The postulated mechanism behind this stimulation technique is that it is applied at intensities below the threshold to evoke a motor evoked potential (MEP). It was shown in patients having electrodes implanted at the cervical level that this type of stimulation does not produce any descending activity but that it primarily activates inhibitory interneurons within the primary motor cortex 14,18,19. This activation of inhibitory interneurons causes a decrease in the ongoing EMG activity and can be quantified by the amount of EMG suppression compared to the EMG activity obtained in trials without stimulation. In this respect, we showed that subjects displayed a significantly greater inhibitory activity in trials in which they received position feedback compared with trials in which force feedback was provided 20. Furthermore, we also showed that not only the presentation of different feedback modalities (force vs. position control) but also the interpretation of feedback can have very similar effects on behavioural and neurophysiological data. More specifically, when we told the participants to receive position feedback (even though it was force feedback) they also not only displayed a shorter time to fatigue but also an increased level of inhibitory M1 activity 21. Using an approach where the same feedback but with different information about its content is always provided has the advantage that the task constraints, i.e., the presentation of the feedback, the gain of the feedback, or the compliance of the load are identical between conditions so that differences in performance and neural activity are clearly related to differences in the interpretation of the feedback and are not biased by different testing conditions. Thus, the current study investigated whether a different interpretation of one and the same feedback influences the duration of a sustained submaximal contraction and furthermore has an impact on the activation of inhibitory activity of the primary motor cortex.

<table><tbody><tr><td>torquemeter</td><td>LCB 130, ME-Mebsysteme, Neuendorf, Germany</td><td></td><td>Part of robotic device built for force and position recordings</td></tr><tr><td>potentiometer</td><td>type 120574, Megatron, Putzbrunn, Germany</td><td></td><td>Part of robotic device built for force and position recordings</td></tr><tr><td>EMG electrodes</td><td>Blue sensor P, Ambu, Bad Nauheim, Germany</td><td></td><td></td></tr><tr><td>TMS coil</td><td>Magstim</td><td></td><td></td></tr><tr><td>TMS machine</td><td>Magstim Company Ltd., Whitland, UK</td><td></td><td></td></tr><tr><td>Recording software</td><td>Labview-Based</td><td></td><td>custom written software</td></tr></tbody></table>

force and position control in humans - the role of augmented feedback

During motor behaviour, humans interact with the environment by for example manipulating objects and this is only possible because sensory feedback is constantly integrated into the central nervous system and these sensory inputs need to be weighted in order meet the task specific goals. Additional feedback presented as augmented feedback was shown to have an impact on motor control and motor learning. A number of studies investigated whether force or position feedback has an influence on motor control and neural activation. However, as in the previous studies the presentation of the force and position feedback was always identical, a recent study assessed whether not only the content but also the interpretation of the feedback has an influence on the time to fatigue of a sustained submaximal contraction and the (inhibitory) activity of the primary motor cortex using subthreshold transcranial magnetic stimulation. This paper describes one possible way to investigate the influence of the interpretation of feedback on motor behaviour by investigating the time to fatigue of submaximal sustained contractions together with the neuromuscular adaptations that can be investigated using surface EMG. Furthermore, the current protocol also describes how motor cortical (inhibitory) activity can be investigated using subthreshold TMS, a method known to act solely on the cortical level. The results show that when participants interpret the feedback as position feedback, they display a significantly shorter time to fatigue of a submaximal sustained contraction. Furthermore, subjects also displayed an increased inhibitory activity of the primary cortex when they believed to receive position feedback compared when they believed to receive force feedback. Accordingly, the results show that interpretation of feedback results in differences on a behavioural level (time to fatigue) that is also reflected in interpretation-specific differences in the amount of inhibitory M1 activity.

Interpretation of feedback
In procedure described here, subjects were instructed in a way that they believed in half of their trials to have received position feedback and in the other half of the trials to have received force feedback. In fact, they were tricked in half of their trials as they the pF-group always received position feedback and the fF-group always received force feedback.
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Watch this Scientific Journal Video about Force and Position Control in Humans - The Role of Augmented Feedback at JoVE.com

Force and Position Control in Humans - The Role of Augmented Feedback

Controlling an identical movement with position or force feedback results in different neural activation and motor behavior. This protocol describes how to investigate behavioral changes by looking at neuromuscular fatigue and how to evaluate motor cortical (inhibitory) activity using subthreshold TMS with respect to the interpretation of augmented feedback.

During motor behaviour, humans interact with the environment by for example manipulating objects and this is only possible because sensory feedback is ...

force-and-position-control-in-humans-the-role-of-augmented-feedback

Research

JoVE Journal

Behavior

7.7K Views.  University of Freiburg. Controlling an identical movement with position or force feedback results in different neural activation and motor behavior. This protocol describes how to investigate behavioral changes by looking at neuromuscular fatigue and how to evaluate motor cortical (inhibitory) activity using subthreshold TMS with respect to the interpretation of augmented feedback. 

Video: Force and Position Control in Humans - The Role of Augmented Feedback

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Recent research that tests interactive devices for prolonged therapy practice has revealed new prospects for robotics combined with graphical and other forms of biofeedback. Previous human-robot interactive systems have required different software commands to be implemented for each robot leading to unnecessary developmental overhead time each time a new system becomes available. For example, when a haptic/graphic virtual reality environment has been coded for one specific robot to provide haptic feedback, that specific robot would not be able to be traded for another robot without recoding the program. However, recent efforts in the open source community have proposed a wrapper class approach that can elicit nearly identical responses regardless of the robot used. The result can lead researchers across the globe to perform similar experiments using shared code. Therefore modular "switching out"of one robot for another would not affect development time. In this paper, we outline the successful creation and implementation of a wrapper class for one robot into the open-source H3DAPI, which integrates the software commands most commonly used by all robots.

Recently, a vast amount of prospects have come available for human-robot interactive systems. In this paper we outline the integration of a new robotic device with open source software that can rapidly make possible a library of interactive functionality. We then outline a clinical application for a neurorehabilitation application.

Recent research that tests interactive devices for prolonged therapy practice has revealed new prospects for robotics combined with graphical and other ...

Bioengineering

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Advances in robotic systems have resulted in prostheses for the upper limb that can produce multifunctional movements. However, these sophisticated systems require upper limb amputees to learn complex control schemes. Humans have the ability to learn new movements through imitation and other learning strategies. This protocol describes a structured rehabilitation method, which includes imitation, repetition, and reinforcement learning, and aims to assess if this method can improve multifunctional prosthetic control. A left below elbow amputee, with 4 years of experience in prosthetic use, took part in this case study. The prosthesis used was a Michelangelo hand with wrist rotation, and the added features of wrist flexion and extension, which allowed more combinations of hand movements. The participant&#8217;s Southampton Hand Assessment Procedure score improved from 58 to 71 following structured training. This suggests that a structured training protocol of imitation, repetition and reinforcement may have a role in learning to control a new prosthetic hand. A larger clinical study is however required to support these findings.

As prosthetic development moves towards the goal of natural control, harnessing amputees&#8217; inherent ability to learn new motor skills may enable proficiency. This manuscript describes a structured rehabilitation protocol, which includes imitation, repetition, and reinforcement learning strategies, for improved multifunctional prosthetic control.

Advances in robotic systems have resulted in prostheses for the upper limb that can produce multifunctional movements. However, these sophisticated ...

Movement Retraining using Real-time Feedback of Performance

Any modification of movement - especially movement patterns that have been honed over a number of years - requires re-organization of the neuromuscular patterns responsible for governing the movement performance. This motor learning can be enhanced through a number of methods that are utilized in research and clinical settings alike. In general, verbal feedback of performance in real-time or knowledge of results following movement is commonly used clinically as a preliminary means of instilling motor learning. Depending on patient preference and learning style, visual feedback (e.g. through use of a mirror or different types of video) or proprioceptive guidance utilizing therapist touch, are used to supplement verbal instructions from the therapist. Indeed, a combination of these forms of feedback is commonplace in the clinical setting to facilitate motor learning and optimize outcomes.
Laboratory-based, quantitative motion analysis has been a mainstay in research settings to provide accurate and objective analysis of a variety of movements in healthy and injured populations. While the actual mechanisms of capturing the movements may differ, all current motion analysis systems rely on the ability to track the movement of body segments and joints and to use established equations of motion to quantify key movement patterns. Due to limitations in acquisition and processing speed, analysis and description of the movements has traditionally occurred offline after completion of a given testing session.
This paper will highlight a new supplement to standard motion analysis techniques that relies on the near instantaneous assessment and quantification of movement patterns and the display of specific movement characteristics to the patient during a movement analysis session. As a result, this novel technique can provide a new method of feedback delivery that has advantages over currently used feedback methods.

Retraining abnormal movement patterns following injury or disease is a key component of physical rehabilitation. Recent advances in technology have permitted accurate assessment of movement during a variety of tasks, with near instantaneous quantification of results. This provides new opportunities for modification of faulty movement patterns in real time.

Any modification of movement - especially movement patterns that have been honed over a number of years - requires re-organization of the neuromuscular ...

Medicine

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

This work describes a methodological framework that can be used to explicitly and implicitly characterize the sense of agency developed over the neural-machine interface (NMI) control of sensate virtual or robotic prosthetic hands. The formation of agency is fundamental in distinguishing the actions that we perform with our limbs as being our own. By striving to incorporate advanced upper-limb prostheses into these same perceptual mechanisms, we can begin to integrate an artificial limb more closely into the user&#39;s existing cognitive framework for limb control. This has important implications in promoting user acceptance, use, and effective control of advanced upper-limb prostheses. In this protocol, participants control a virtual prosthetic hand and receive kinesthetic sensory feedback through their preexisting NMIs. A series of virtual grasping tasks are performed and perturbations are systematically introduced to the kinesthetic feedback and virtual hand movements. Two separate measures of agency are employed: established psychophysical questionnaires (to capture the explicit experience of agency) and a time interval estimate task to capture the implicit sense of agency (intentional binding). Results of this protocol (questionnaire scores and time interval estimates) can be analyzed to quantify the extent of agency formation.

Here we present a protocol which characterizes the sense of agency developed over the control of sensate virtual or robotic prosthetic hands. Psychophysical questionnaires are employed to capture the explicit experience of agency, and time interval estimates (intentional binding) are employed to implicitly measure the sense of agency.

This work describes a methodological framework that can be used to explicitly and implicitly characterize the sense of agency developed over the ...

Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback

The application of incongruent sensory signals that involves disrupted tactile feedback is rarely explored, specifically with the presence of vibrotactile feedback (VTF). This protocol aims to test the effect of VTF on the response to incongruent visual-tactile stimuli. The tactile feedback is acquired by grasping a block and moving it across a partition. The visual feedback is a real-time virtual presentation of the moving block, acquired using a motion capture system. The congruent feedback is the reliable presentation of the movement of the block, so that the subject feels that the block is grasped and see it move along with the path of the hand. The incongruent feedback appears as the movement of the block diverts from the actual movement path, so that it seems to drop from the hand when it is actually still held by the subject, thereby contradicting the tactile feedback. Twenty subjects (age 30.2 &#177; 16.3) repeated 16 block transfers, while their hand was hidden. These were repeated with VTF and without VTF (total of 32 block transfers). Incongruent stimuli were presented randomly twice within the 16 repetitions in each condition (with and without VTF). Each subject was asked to rate the difficulty level of performing the task with and without the VTF. There were no statistically significant differences in the length of the hand paths and durations between transfers recorded with congruent and incongruent visual-tactile signals &#8211; with and without the VTF. The perceived difficulty level of performing the task with the VTF significantly correlated with the normalized path length of the block with VTF (r = 0.675, p = 0.002). This setup is used to quantify the additive or reductive value of VTF during motor function that involves incongruent visual-tactile stimuli. Possible applications are prosthetics design, smart sport-wear, or any other garments that incorporate VTF.

We present a protocol to apply incongruent visual-tactile stimuli during an object transfer task. Specifically, during block transfers, performed while the hand is hidden, a virtual presentation of the block shows random occurrences of false block drops. The protocol also describes adding vibrotactile feedback while performing the motor task.

The application of incongruent sensory signals that involves disrupted tactile feedback is rarely explored, specifically with the presence of vibrotactile ...

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

The fields that develop methods for sensory substitution and sensory augmentation have aimed to control external goals using signals from the central nervous systems (CNS). Less frequent however, are protocols that update external signals self-generated by interactive bodies in motion. There is a paucity of methods that combine the body-heart-brain biorhythms of one moving agent to steer those of another moving agent during dyadic exchange. Part of the challenge to accomplish such a feat has been the complexity of the setup using multimodal bio-signals with different physical units, disparate time scales and variable sampling frequencies.
In recent years, the advent of wearable bio-sensors that can non-invasively harness multiple signals in tandem, has opened the possibility to re-parameterize and update the peripheral signals of interacting dyads, in addition to improving brain- and/or body-machine interfaces. Here we present a co-adaptive interface that updates efferent somatic-motor output (including kinematics and heart rate) using biosensors; parameterizes the stochastic bio-signals, sonifies this output, and feeds it back in re-parameterized form as visuo/audio-kinesthetic reafferent input. We illustrate the methods using two types of interactions, one involving two humans and another involving a human and its avatar interacting in near real time. We discuss the new methods in the context of possible new ways to measure the influences of external input on internal somatic-sensory-motor control.

We present protocols and methods of analyses to build co-adaptive interfaces that stream, parameterize, analyze, and modify human body and heart signals in close-loop. This setup interfaces signals derived from the peripheral and central nervous systems of the person with external sensory inputs to help track biophysical change.

The fields that develop methods for sensory substitution and sensory augmentation have aimed to control external goals using signals from the central ...

A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli

Artificial sensory feedback (ASF) systems can be used to compensate for lost proprioception in individuals with lower-limb impairments. Effective design of these ASF systems requires an in-depth understanding of how the parameters of specific feedback mechanism affect user perception and reaction to stimuli. This article presents a method for applying vibrotactile stimuli to human participants and measuring their response. Rotating mass vibratory motors are placed at pre-defined locations on the participant's thigh, and controlled through custom hardware and software. The speed and accuracy of participants' volitional responses to vibrotactile stimuli are measured for researcher-specified combinations of motor placement and vibration frequency. While the protocol described here uses push-buttons to collect a simple binary response to the vibrotactile stimuli, the technique can be extended to other response mechanisms using inertial measurement units or pressure sensors to measure joint angle and weight bearing ratios, respectively. Similarly, the application of vibrotactile stimuli can be explored for body segments other than the thigh.

This article describes a technique for applying vibrotactile stimuli to the thigh of a human participant, and measuring the accuracy and reaction time of the participant&#39;s volitional response for various combinations of stimulation location and frequency.

Artificial sensory feedback (ASF) systems can be used to compensate for lost proprioception in individuals with lower-limb impairments.  Effective design ...

Experimental Methods to Study Human Postural Control

Many components of the nervous and musculoskeletal systems act in concert to achieve the stable, upright human posture. Controlled experiments accompanied by appropriate mathematical methods are needed to understand the role of the different sub-systems involved in human postural control. This article describes a protocol for performing perturbed standing experiments, acquiring experimental data, and carrying out the subsequent mathematical analysis, with the aim of understanding the role of musculoskeletal system and central control in human upright posture. The results generated by these methods are important, because they provide insight into the healthy balance control, form the basis for understanding the etiology of impaired balance in patients and the elderly, and aid in the design of interventions to improve postural control and stability. These methods can be used to study the role of somatosensory system, intrinsic stiffness of ankle joint, and visual system in postural control, and may also be extended to investigate the role of vestibular system. The methods are to be used in the case of an ankle strategy, where the body moves primarily about the ankle joint and is considered a single-link inverted pendulum.

This article presents an experimental/analytic framework to study human postural control. The protocol provides step-by-step procedures for performing standing experiments, measuring body kinematics and kinetics signals, and analyzing the results to provide insight into the mechanisms underlying human postural control.

Many components of the nervous and musculoskeletal systems act in concert to achieve the stable, upright human posture. Controlled experiments accompanied ...

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Feedback control theory has been extensively implemented to theoretically model human sensorimotor control. However, experimental platforms capable of manipulating important components of multiple feedback loops lack development. This paper describes WheelCon, an open-source platform aimed at resolving such insufficiencies. Using only a computer, a standard display, and inexpensive gaming steering wheel equipped with a force feedback motor, WheelCon safely simulates the canonical sensorimotor task of riding a mountain bike down a steep, twisting, bumpy trail. The platform provides flexibility, as will be demonstrated in the demos provided, so that researchers may manipulate the disturbances, delay, and quantization (data rate) in the layered feedback loops, including a high-level advanced plan layer and a low-level delayed reflex layer. In this paper, we illustrate WheelCon&#39;s graphical user interface (GUI), the input and output of existing demos, and how to design new games. In addition, we present the basic feedback model and the experimental results from the demo games, which align well with the model&#39;s prediction. The WheelCon platform can be downloaded at https://github.com/Doyle-Lab/WheelCon. In short, the platform is featured to be cheap, simple to use, and flexible to program for effective sensorimotor neuroscience research and control engineering education.

WheelCon is a novel, free and open-source platform to design video games that noninvasively simulates mountain biking down a steep, twisting, bumpy trail. It contains components presenting in human sensorimotor control (delay, quantization, noise, disturbance, and multiple feedback loops) and allows researchers to study the layered architecture in sensorimotor control.

Feedback control theory has been extensively implemented to theoretically model human sensorimotor control. However, experimental platforms capable of ...

Neuroscience

Effects of feedback

Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents the open-loop gain and H is the feedback factor. This alteration means feedback can amplify or attenuate the gain depending on the frequency range, thus influencing the overall system performance.
Stability is another crucial aspect affected by feedback. Stability refers to the system&#39;s ability to accurately follow an input command and maintain a controllable output. An unstable system cannot reliably track input commands, resulting in erratic and uncontrollable outputs. Feedback can impact stability in two opposing ways: it can either destabilize an initially stable system or stabilize an unstable one. If the product GH equals negative one, the system becomes unstable, as any finite input results in an infinite output. Conversely, incorporating a negative feedback loop can stabilize an otherwise unstable system by adjusting the feedback gain appropriately.
In addition to gain and stability, feedback also affects a system&#39;s response to extraneous signals and noise. In the presence of disturbances, feedback helps minimize the impact of these unwanted signals. If the factor in the feedback loop&#39;s denominator is greater than unity and the system remains stable, the noise component is effectively reduced. This reduction is crucial in maintaining the integrity of the desired signal and ensuring the system&#39;s robustness against external interferences.
Overall, feedback mechanisms are indispensable in refining the performance of control systems. By carefully designing and implementing feedback loops, engineers can enhance stability, control gain, minimize noise, and improve the system&#39;s responsiveness and accuracy. The strategic use of feedback thus plays a pivotal role in advancing the efficacy and reliability of modern control systems across various applications.

Feedback in control systems extends beyond error reduction, impacting aspects like stability, bandwidth, gain, impedance, and sensitivity.
The significant ...