Measuring and monitoring skill learning in closed-loop myoelectric hand prostheses using speed-accuracy tradeoffs.

Objective.Closed-loop myoelectric prostheses, which combine supplementary sensory feedback and electromyography (EMG) based control, hold the potential to narrow the divide between natural and bionic hands. The use of these devices, however, requires dedicated training. Therefore, it is crucial to develop methods that quantify how users acquire skilled control over their prostheses to effectively monitor skill progression and inform the development of interfaces that optimize this process.Approach.Building on theories of skill learning in human motor control, we measured speed-accuracy tradeoff functions (SAFs) to comprehensively characterize learning-induced changes in skill-as opposed to merely tracking changes in task success across training-facilitated by a closed-loop interface that combined proportional control and EMG feedback. Sixteen healthy participants and one individual with a transradial limb loss participated in a three-day experiment where they were instructed to perform the box-and-blocks task using a timed force-matching paradigm at four specified speeds to reach two target force levels, such that the SAF could be determined.Main results.We found that the participants' accuracy increased in a similar way across all speeds we tested. Consequently, the shape of the SAF remained similar across days, at both force levels. Further, we observed that EMG feedback enabled participants to improve their motor execution in terms of reduced trial-by-trial variability, a hallmark of skilled behavior. We then fit a power law model of the SAF, and demonstrated how the model parameters could be used to identify and monitor changes in skill.Significance.We comprehensively characterized how an EMG feedback interface enabled skill acquisition, both at the level of task performance and movement execution. More generally, we believe that the proposed methods are effective for measuring and monitoring user skill progression in closed-loop prosthesis control.

EMG feedback improves grasping of compliant objects using a myoelectric prosthesis.

BACKGROUND: Closing the control loop in myoelectric prostheses by providing artificial somatosensory feedback is recognized as an important goal. However, designing a feedback interface that is effective in realistic conditions is still a challenge. Namely, in some situations, feedback can be redundant, as the information it provides can be readily obtained through hearing or vision (e.g., grasping force estimated from the deformation of a compliant object). EMG feedback is a non-invasive method wherein the tactile stimulation conveys to the user the level of their own myoelectric signal, hence a measurement intrinsic to the interface, which cannot be accessed incidentally. METHODS: The present study investigated the efficacy of EMG feedback in prosthesis force control when 10 able-bodied participants and a person with transradial amputation used a myoelectric prosthesis to grasp compliant objects of different stiffness values. The performance with feedback was compared to that achieved when the participants relied solely on incidental cues. RESULTS: The main outcome measures were the task success rate and completion time. EMG feedback resulted in significantly higher success rates regardless of pin stiffness, indicating that the feedback enhanced the accuracy of force application despite the abundance of incidental cues. Contrary to expectations, there was no difference in the completion time between the two feedback conditions. Additionally, the data revealed that the participants could produce smoother control signals when they received EMG feedback as well as more consistent commands across trials, signifying better control of the system by the participants. CONCLUSIONS: The results presented in this study further support the efficacy of EMG feedback when closing the prosthesis control loop by demonstrating its benefits in particularly challenging conditions which maximized the utility of intrinsic feedback sources.

Encoding force modulation in two electrotactile feedback parameters strengthens sensory integration according to maximum likelihood estimation.

Bidirectional human-machine interfaces involve commands from the central nervous system to an external device and feedback characterizing device state. Such feedback may be elicited by electrical stimulation of somatosensory nerves, where a task-relevant variable is encoded in stimulation amplitude or frequency. Recently, concurrent modulation in amplitude and frequency (multimodal encoding) was proposed. We hypothesized that feedback with multimodal encoding may effectively be processed by the central nervous system as two independent inputs encoded in amplitude and frequency, respectively, thereby increasing state estimate quality in accordance with maximum-likelihood estimation. Using an adaptation paradigm, we tested this hypothesis during a grasp force matching task where subjects received electrotactile feedback encoding instantaneous force in amplitude, frequency, or both, in addition to their natural force feedback. The results showed that adaptations in grasp force with multimodal encoding could be accurately predicted as the integration of three independent inputs according to maximum-likelihood estimation: amplitude modulated electrotactile feedback, frequency modulated electrotactile feedback, and natural force feedback (r2 = 0.73). These findings show that multimodal electrotactile feedback carries an intrinsic advantage for state estimation accuracy with respect to single-variable modulation and suggest that this scheme should be the preferred strategy for bidirectional human-machine interfaces with electrotactile feedback.

Closed-Loop Control of a Multifunctional Myoelectric Prosthesis With Full-State Anatomically Congruent Electrotactile Feedback.

State-of-the-art myoelectric hand prostheses provide multi-functional control but lack somatosensory feedback. To accommodate the full functionality of a dexterous prosthesis, the artificial sensory feedback needs to convey several degrees of freedom (DoF) simultaneously. However, this is a challenge with current methods as they are characterized by a low information bandwidth. In this study, we leverage the flexibility of a recently developed system for simultaneous electrotactile stimulation and electromyography (EMG) recording to present the first solution for closed-loop myoelectric control of a multifunctional prosthesis with full-state anatomically congruent electrotactile feedback. The novel feedback scheme (coupled encoding) conveyed proprioceptive (hand aperture, wrist rotation) and exteroceptive information (grasping force). The coupled encoding was compared to the conventional approach (sectorized encoding) and incidental feedback in 10 non-disabled and one amputee participant who used the system to perform a functional task. The results showed that both feedback approaches increased the accuracy of position control compared to incidental feedback. However, the feedback increased completion time, and it did not significantly improve grasping force control. Importantly, the performance of the coupled feedback was not significantly different compared to the conventional scheme, despite the latter being easier to learn during training. Overall, the results indicate that the developed feedback can improve prosthesis control across multiple DoFs but they also highlight the subjects' ability to exploit minimal incidental information. Importantly, the current setup is the first to convey three feedback variables simultaneously using electrotactile stimulation while providing multi-DoF myoelectric control with all hardware components mounted on the same forearm.

Adaptations in motor unit properties underlying changes in recruitment, rate coding, and maximum force.

Changes in the discharge characteristics of motor units as well as in the maximum force-producing capacity of the muscle are observed following training, aging, and fatiguability. The ability to measure the adaptations in the neuromuscular properties underlying these changes experimentally, however, is limited. In this study we used a computational model to systematically investigate the effects of various neural and muscular adaptations on motor unit recruitment thresholds, average motor unit discharge rates in submaximal contractions, and maximum force. The primary focus was to identify candidate adaptations that can explain experimentally observed changes in motor unit discharge characteristics after 4 wk of strength training (Del Vecchio A, Casolo A, Negro F, Scorcelletti M, Bazzucchi I, Enoka R, Felici F, Farina D. J Physiol 597: 1873-1887, 2019). The simulation results indicated that multiple combinations of adaptations, likely involving an increase in maximum discharge rate across motor units, may occur after such training. On a more general level, we found that the magnitude of the adaptations scales linearly with the change in recruitment thresholds, discharge rates, and maximum force. In addition, the combination of multiple adaptations can be predicted as the linear sum of their individual effects. Together, this implies that the outcomes of the simulations can be generalized to predict the effect of any combination of neural and muscular adaptations. In this way, the study provides a tool for estimating potential underlying adaptations in neural and muscular properties to explain any change in commonly used measures of rate coding, recruitment, and maximum force.NEW & NOTEWORTHY Our ability to measure adaptations in neuromuscular properties in vivo is limited. Using a computational model, we quantify the effect of multiple neuromuscular adaptations on common measures of motor unit recruitment, rate coding, and force-producing capacity. Scaling and combining adaptations had a near-linear effect on these measures, indicating that the results can explain and predict neuromuscular adaptations in a wide range of conditions, including, but not limited to, strength training.

EMG feedback outperforms force feedback in the presence of prosthesis control disturbance.

Closing the prosthesis control loop by providing artificial somatosensory feedback can improve utility and user experience. Additionally, closed-loop control should be more robust with respect to disturbance, but this might depend on the type of feedback provided. Thus, the present study investigates and compares the performance of EMG and force feedback in the presence of control disturbances. Twenty able-bodied subjects and one transradial amputee performed delicate and power grasps with a prosthesis in a functional task, while the control signal gain was temporarily increased (high-gain disturbance) or decreased (low-gain disturbance) without their knowledge. Three outcome measures were considered: the percentage of trials successful in the first attempt (reaction to disturbance), the average number of attempts in trials where the wrong force was initially applied (adaptation to disturbance), and the average completion time of the last attempt in every trial. EMG feedback was shown to offer significantly better performance compared to force feedback during power grasping in terms of reaction to disturbance and completion time. During power grasping with high-gain disturbance, the median first-attempt success rate was significantly higher with EMG feedback (73.3%) compared to that achieved with force feedback (60%). Moreover, the median completion time for power grasps with low-gain disturbance was significantly longer with force feedback than with EMG feedback (3.64 against 2.48 s, an increase of 32%). Contrary to our expectations, there was no significant difference between feedback types with regards to adaptation to disturbances and the two feedback types performed similarly in delicate grasps. The results indicated that EMG feedback displayed better performance than force feedback in the presence of control disturbances, further demonstrating the potential of this approach to provide a reliable prosthesis-user interaction.

Continuous Transition Impairs Discrimination of Electrotactile Frequencies.

Just-noticeable difference (JND), indicating the ability to accurately identify small differences in stimulation parameters, can be used to choose more sensitive stimulation methods as well as to calibrate tactile feedback in closed-loop human-machine interfacing. The JND is typically estimated using a forced-choice-discrimination task, in which two stimuli with different intensities are delivered separated by a brief pause. In the applications of tactile feedback, however, the stimulation parameters are typically modulated continuously. It is unclear if the discriminability of stimuli separated in time characterizes the ability to distinguish continuous changes in stimulation intensity. The present study compared the JND when pairs of frequency-modulated electrotactile stimuli were separated in time and presented continuously at two different baseline frequencies (20 and 60 Hz). The results showed that the JND was significantly smaller with time-separation between stimuli, but that the JND obtained with different types of transitions were in most cases linearly associated. In conclusion, the discriminability of time-separated stimuli is systematically better compared to that of the stimuli presented continuously. This can have an impact when calibrating the tactile feedback where the conventional method of the JND assessment might lead to an overly optimistic estimate of detectable changes.

Estimating speed-accuracy trade-offs to evaluate and understand closed-loop prosthesis interfaces.

Objective. Closed-loop prosthesis interfaces, which combine electromyography (EMG)-based control with supplementary feedback, represent a promising direction for developing the next generation of bionic limbs. However, we still lack an understanding of how users utilize these interfaces and how to evaluate competing solutions. In this study, we used the framework of speed-accuracy trade-off functions (SAF) to understand, evaluate, and compare the performance of two closed-loop user-prosthesis interfaces.Approach. Ten able-bodied participants and an amputee performed a force-matching task in a functional box-and-block setup at three different speeds. All participants were subjected to both interfaces in a crossover study design with a 1 week washout period. Importantly, both interfaces used direct proportional control but differed in the feedback provided to the participant (EMG feedback vs. Force feedback). We estimated the SAFs afforded by the two interfaces and sought to understand how the participants planned and executed the task under the various conditions.Main results. We found that execution speed significantly influenced performance, and that EMG feedback afforded better overall performance, especially at medium speeds. Notably, we found that there was a difference in the SAF between the two interfaces, with EMG feedback enabling participants to attain higher accuracies faster than Force feedback. Furthermore, both interfaces enabled participants to develop flexible control policies, while EMG feedback also afforded participants the ability to generate smoother, more repeatable EMG commands.Significance. Overall, the results indicate that the performance of closed-loop prosthesis interfaces depends critically on the feedback approach and execution speed. This study showed that the SAF framework could be used to reveal the differences between feedback approaches, which might not have been detected if the assessment was performed at a single speed. Therefore, we argue that it is important to consider the speed-accuracy trade-offs to rigorously evaluate and compare user-prosthesis interfaces.

Combined spatial and frequency encoding for electrotactile feedback of myoelectric signals.

Electrotactile stimulation has been commonly used in human-machine interfaces to provide feedback to the user, thereby closing the control loop and improving performance. The encoding approach, which defines the mapping of the feedback information into stimulation profiles, is a critical component of an electrotactile interface. Ideally, the encoding will provide a high-fidelity representation of the feedback variable while being easy to perceive and interpret by the subject. In the present study, we performed a closed-loop experiment wherein discrete and continuous coding schemes are combined to exploit the benefits of both techniques. Subjects performed a muscle activation-matching task relying solely on electrotactile feedback representing the generated myoelectric signal (EMG). In particular, we investigated the performance of two different coding schemes (spatial and spatial combined with frequency) at two feedback resolutions (low: 3 and high: 5 intervals). In both schemes, the stimulation electrodes were placed circumferentially around the upper arm. The magnitude of the normalized EMG was divided into intervals, and each electrode was associated with one interval. When the generated EMG entered one of the intervals, the associated electrode started stimulating. In the combined encoding, the additional frequency modulation of the active electrode also indicated the momentary magnitude of the signal within the interval. The results showed that combined coding decreased the undershooting rate, variability and absolute deviation when the resolution was low but not when the resolution was high, where it actually worsened the performance. This demonstrates that combined coding can improve the effectiveness of EMG feedback, but that this effect is limited by the intrinsic variability of myoelectric control. Our findings, therefore, provide important insights as well as elucidate limitations of the information encoding methods when using electrotactile stimulation to convey a feedback signal characterized by high variability (EMG biofeedback).

Lack of increased rate of force development after strength training is explained by specific neural, not muscular, motor unit adaptations.

Although maximal force increases following short-term isometric strength training, the rate of force development (RFD) may remain relatively unaffected. The underlying neural and muscular mechanisms during rapid contractions after strength training are largely unknown. Since strength training increases the neural drive to muscles, it may be hypothesized that there are distinct neural or muscular adaptations determining the change in RFD independently of an increase in maximal force. Therefore, we examined motor unit population data acquired from surface electromyography during the rapid generation of force before and after 4 wk of strength training. We observed that strength training did not change the RFD because it did not influence the number of motor units recruited per second or their initial discharge rate during rapid contractions. Although strength training did not change motoneuron behavior in the force increase phase of rapid contractions, it increased the discharge rate of motoneurons (by ∼4 spikes/s) when reaching the plateau phase (∼150 ms) of the rapid contractions, determining an increase in maximal force production. Computer simulations with a motor unit model that included neural and muscular properties, closely matched the experimental observations and demonstrated that the lack of change in RFD following training is primarily mediated by an unchanged maximal recruitment speed of motoneurons. These results demonstrate that maximal force and contraction speed are determined by different adaptations in motoneuron behavior following strength training and indicate that increases in the recruitment speed of motoneurons are required to evoke training-induced increases in RFD.NEW & NOTEWORTHY Although maximal force increases with strength training, the rate of force development may remain unaffected. For the first time, we associated motor unit population behavior during rapid force contractions before and after a 4-wk isometric strength training intervention. We found that strength training combined with slow and rapid contractions does not change rate of force development. The specific mechanisms include similar discharge rate during the initial phase of contraction and similar recruitment speed of motoneurons.

Influence of central and peripheral motor unit properties on isometric muscle force entropy: A computer simulation study.

Approximate entropy of isometric force is a popular measure to characterize behavioral changes across muscle contraction conditions. The degree to which force entropy characterizes the randomness of the motor control strategy, however, is not known. In this study, we used a computational model to investigate the correlation between approximate entropy of the synaptic input to a motor neuron pool, the neural drive to muscle (cumulative spike train; CST), and the force. This comparison was made across several simulation conditions, that included different synaptic command signal bandwidths, motor neuron pool sizes, and muscle contractile properties. The results indicated that although force entropy to some degree reflects the entropy of the synaptic command to motor neurons, it is biased by changes in motor unit properties. As a consequence, there was a low correlation between approximate entropy of force and the motor neuron input signal across all simulation conditions (r2 = 0.13). Therefore, force entropy should only be used to compare motor control strategies across conditions where motor neuron properties can be assumed to be maintained. Instead, we recommend that the entropy of the descending motor commands should be estimated from CSTs comprising spike trains of multiple motor units.

Electrotactile and Vibrotactile Feedback Enable Similar Performance in Psychometric Tests and Closed-Loop Control.

Electro- and vibro-tactile stimulation are commonly employed for feedback in closed-loop human-machine interfacing. Although these feedback systems have been extensively investigated individually, they are rarely objectively compared. In this study, two state-of-the-art stimulation units (concentric electrode and C2-tactor) similar in shape and size were compared in psychometric and online control tests. The just noticeable difference and number of discriminable levels for intensity and frequency modulation were determined across values of carrier frequency and intensity, respectively. Next, subjects performed a compensatory tracking task, in which the feedback encoded the momentary tracking error. In the psychometric tests, intensity modulation outperformed frequency modulation and electrotactile stimulation enabled significantly higher resolution than vibrotactile stimulation, for the same carrier frequency. However, for the best-case settings (eletro-tactile: 100 Hz; vibro-tactile: 200 Hz), the two stimulation modalities were equivalent in the psychometric tests and in the online control tests, where the two stimulation methods resulted in similar correlation and deviation between the target and the generated trajectory. Time delay was slightly but significantly lower for the vibrotactile modality. Overall, the present assessment shows that despite psychometric differences between the two stimulation methods, they enable similar online control performance when parameters are optimally selected for each modality.

The impact of objective functions on control policies in closed-loop control of grasping force with a myoelectric prosthesis.

Objective.Supplemental sensory feedback for myoelectric prostheses can provide both psychosocial and functional benefits during prosthesis control. However, the impact of feedback depends on multiple factors and there is insufficient understanding about the fundamental role of such feedback in prosthesis use. The framework of human motor control enables us to systematically investigate the user-prosthesis control loop. In this study, we explore how different task objectives such as speed and accuracy shape the control policy developed by participants in a prosthesis force-matching task.Approach.Participants were randomly assigned to two groups that both used identical electromyography control interface and prosthesis force feedback, through vibrotactile stimulation, to perform a prosthesis force-matching task. However, the groups received different task objectives specifying speed and accuracy demands. We then investigated the control policies developed by the participants. To this end, we not only evaluated how successful or fast participants were but also analyzed the behavioral strategies adopted by the participants to obtain such performance gains.Main results.First, we observed that participants successfully integrated supplemental prosthesis force feedback to develop both feedforward and feedback control policies, as demanded by the task objectives. We then observed that participants who first developed a (slow) feedback policy were quickly able to adapt their policy to more stringent speed demands, by switching to a combined feedforward-feedback control strategy. However, the participants who first developed a (fast) feedforward policy were not able to change their control policy and adjust to greater accuracy demands.Significance.Overall, the results signify how the framework of human motor control can be applied to study the role of feedback in user-prosthesis interaction. The results also reveal the utility of training prosthesis users to integrate supplemental feedback into their state estimation by designing training protocols that encourage the development of combined feedforward and feedback policy.

The effect of calibration parameters on the control of a myoelectric hand prosthesis using EMG feedback.

Objective.The implementation of somatosensory feedback in upper limb myoelectric prostheses is an important step towards the restoration of lost sensory-motor functions. EMG feedback is a recently proposed method for closing the control loop wherein the myoelectric signal that drives the prosthesis is also used to generate the feedback provided to the user. Therefore, the characteristics of the myoelectric signal (variability and sensitivity) are likely to significantly affect the ability of the subject to utilize this feedback for online control of the prosthesis.Approach.In the present study, we investigated how the cutoff frequency of the low-pass filter (0.5, 1 and 1.5 Hz) and normalization value (20%, 40% and 60% of the maximum voluntary contraction (MVC)), that are used for the generation of the myoelectric signal, affect the quality of closed-loop control with EMG feedback. Lower cutoff and normalization decrease the intrinsic variability of the EMG but also increase the time lag between the contraction and the feedback (cutoff) as well as the sensitivity of the myoelectric signal (normalization). Ten participants were asked to generate three grasp force levels with a myoelectric prosthetic hand, while receiving five-level vibrotactile EMG feedback, over nine experimental runs (all parameter combinations).Main results.The outcome measure was the success rate (SR) in achieving the appropriate level of myoelectric signal (primary outcome) and grasping force (secondary outcome). Overall, the experiments demonstrated that EMG feedback provided robust control across conditions. Nevertheless, the performance was significantly better for the lowest cutoff (0.5 Hz) and higher normalization (40% and 60%). The highest SR for the EMG was 71.9%, achieved in the condition (40% MVC and 0.5 Hz), and this was 24.1% higher than that in the condition (20% MVC and 1.5 Hz), which resulted in the lowest performance. The SR for the force followed a similar trend.Significance.This is the first study that systematically explored the parameter space for the calibration of EMG feedback, which is a critical step for the future clinical application of this approach.

Feedforward modulation of gamma motor neuron activity can improve motor command accuracy.

Objective. Coactivation of gamma and alpha motor neuron activity ensures that muscle spindle responsiveness is maintained during muscle contractions. However, some evidence suggests that the activity of gamma motor neurons is phase-advanced with respect to that of alpha motor neurons during manual control tasks. We hypothesized that this might be a deliberate control strategy to maximize movement accuracy.Approach. Using a computational model of the neural activation of a muscle and its type Ia sensory feedback to the motor neurons, we systematically investigated the impact of the phase difference between oscillatory descending input to alpha and dynamic gamma motor neurons. Specifically, the amplification of the alpha motor neuron drive to the muscle was investigated as a function of the frequency of the synaptic input (1-9 Hz individually or superimposed) and the alpha-gamma phase difference (0-2π).Main results. Simulation results showed that when the phase advance of the dynamic gamma drive resulted in delays between muscle velocity and type Ia afferent feedback similar to those previously observed experimentally, low-frequency components (1 and 2 Hz) of the motor neuron synaptic input were amplified (gain up to 1.7). On the other hand, synaptic input at higher frequencies was unaffected.Significance. This finding suggests that by imposing a phase advance of the input to dynamic gamma motor neurons, components of the neural drive usually associated with voluntary control are amplified. In this way, our study suggests that this neural strategy increases the control-to-neural-noise ratio of the motor output during movement.

A compact system for simultaneous stimulation and recording for closed-loop myoelectric control.

BACKGROUND: Despite important advancements in control and mechatronics of myoelectric prostheses, the communication between the user and his/her bionic limb is still unidirectional, as these systems do not provide somatosensory feedback. Electrotactile stimulation is an attractive technology to close the control loop since it allows flexible modulation of multiple parameters and compact interface design via multi-pad electrodes. However, the stimulation interferes with the recording of myoelectric signals and this can be detrimental to control. METHODS: We present a novel compact solution for simultaneous recording and stimulation through dynamic blanking of stimulation artefacts. To test the system, a feedback coding scheme communicating wrist rotation and hand aperture was developed specifically to stress the myoelectric control while still providing meaningful information to the subjects. Ten subjects participated in an experiment, where the quality of closed-loop myoelectric control was assessed by controlling a cursor in a two degrees of freedom target-reaching task. The benchmark performance with visual feedback was compared to that achieved by combining visual feedback and electrotactile stimulation as well as by using electrotactile feedback only. RESULTS: There was no significant difference in performance between visual and combined feedback condition with regards to successfully reached targets, time to reach a target, path efficiency and the number of overshoots. Therefore, the quality of myoelectric control was preserved in spite of the stimulation. As expected, the tactile condition was significantly poorer in completion rate (100/4% and 78/25% for combined and tactile condition, respectively) and time to reach a target (9/2 s and 13/4 s for combined and tactile condition, respectively). However, the performance in the tactile condition was still good, with no significant difference in path efficiency (38/8%) and the number of overshoots (0.5/0.4 overshoots), indicating that the stimulation was meaningful for the subjects and useful for closed-loop control. CONCLUSIONS: Overall, the results demonstrated that the developed system can provide robust closed-loop control using electrotactile stimulation. The system supports different encoding schemes and allows placing the recording and stimulation electrodes next to each other. This is an important step towards an integrated solution where the developed unit will be embedded into a prosthetic socket.

Inability to increase the neural drive to muscle is associated with task failure during submaximal contractions.

We investigated changes in motor unit (MU) behavior and vasti-muscle contractile properties during sustained submaximal fatiguing contractions with a new time-domain tracking technique to understand the mechanisms responsible for task failure. Sixteen participants performed a nonfatiguing 15-s isometric knee extension at 50% of the maximum voluntary (MVC) torque, followed by a 30% MVC sustained contraction until exhaustion. Two grids of 64 surface electromyography electrodes were placed over vastus medialis and lateralis. Signals were decomposed into MU discharge times and the MUs from the 30% MVC sustained contraction were followed until task failure by overlapping decomposition intervals. These MUs were then tracked between 50% and 30% MVC. During the sustained fatiguing contraction, MUs of the two muscles decreased their discharge rate until ∼40% of the endurance time, referred to as the reversal time, and then increased their discharge rate until task failure. This reversal in firing behavior predicted total endurance time and was matched by opposite changes in twitch force (increase followed by a decrease). Despite the later increase in MU firing rates, peak discharge rates at task failure did not reach the frequency attained during a nonfatiguing 50% MVC contraction. These results show that changes in MU firing properties are influenced by adjustments in contractile properties during the course of the contraction, allowing the identification of two phases. Nevertheless, the contraction cannot be sustained, possibly because of progressive motoneuron inhibition/decreased excitability, as the later increase in firing rate saturates at a much lower frequency compared with a higher-force nonfatiguing contraction.NEW & NOTEWORTHY Motor unit firing and contractile properties during a submaximal contraction until failure were assessed with a new tracking technique. Two distinct phases in firing behavior were observed, which compensated for changes in twitch area and predicted time to failure. However, the late increase in firing rate was below the rates attained in absence of fatigue, which points to an inability of the central nervous system to sufficiently increase the neural drive to muscle with fatigue.

Amplitude versus spatially modulated electrotactile feedback for myoelectric control of two degrees of freedom.

OBJECTIVE: Artificial proprioceptive feedback from a myoelectric prosthesis is an important aspect in enhancing embodiment and user satisfaction, possibly lowering the demand for visual attention while controlling a prosthesis in everyday tasks. Contemporary myoelectric prostheses are advanced mechatronic systems with multiple degrees of freedom, and therefore, to communicate the prosthesis state, the feedback interface needs to transmit several variables simultaneously. In the present study, two different configurations for conveying proprioceptive information of wrist rotation and hand aperture through multichannel electrotactile stimulation were developed and evaluated during online myoelectric control. APPROACH: Myoelectric recordings were acquired from the dominant forearm and electrotactile stimulation was delivered on the non-dominant forearm using a compact interface. The first feedback configuration, which was based on spatial coding, transmitted the information using a moving tactile stimulus, whereas the second, amplitude-based configuration conveyed the position via sensation intensity. Thirteen able-bodied subjects used pattern classification-based myoelectric control with both feedback configurations to accomplish a target-reaching task. MAIN RESULTS: High task performance (completion rate > 90%) was observed for both configurations, with no significant difference in completion rate, time to reach the target, distance error and path efficiency, respectively. SIGNIFICANCE: Overall, the results demonstrated that both feedback configurations allowed subjects to perceive and interpret two feedback variables delivered simultaneously, despite using a compact stimulation interface. This is an encouraging result for the prospect of communicating the full state of a multifunctional hand prosthesis.

Closed-loop Control using Electrotactile Feedback Encoded in Frequency and Pulse Width.

Sensory substitution by electrotactile stimulation has been widely investigated for improving the functionality of human-machine interfaces. Few studies, however, have objectively compared different ways in which such systems can be implemented. In this study, we compare encoding of a feedback variable in stimulation pulse width or stimulation frequency during a closed-loop control task. Specifically, participants were asked to track a predefined pseudorandom trajectory using a joystick with electrotactile feedback as the only indication of the tracking error. Each participant performed eight 90 s trials per encoding scheme. Tracking performance using frequency modulation enabled lower tracking error (RMSE: Frequency modulation: 0.27 ± 0.03; Pulse width modulation: 0.31 ± 0.05; p < 0.05) and a higher correlation with the target trajectory (Frequency modulation: 83.4 ± 4.1%; Pulse width modulation: 79.8 ± 5.2%; p < 0.05). There was no significant improvement in performance over the eight trials. Furthermore, frequency-domain analysis revealed that frequency modulation was characterized with a higher gain at lower error frequencies. In summary, the results indicate that encoding of feedback variables in the frequency of pulses enables better control than pulse width modulation in closed-loop dynamic tasks.

Neural and muscular determinants of maximal rate of force development.

The ability to produce rapid forces requires quick motor unit recruitment, high motor unit discharge rates, and fast motor unit force twitches. The relative importance of these parameters for maximum rate of force development (RFD), however, is poorly understood. In this study, we systematically investigated these relationships using a computational model of motor unit pool activity and force. Across simulations, neural and muscular properties were systematically varied in experimentally observed ranges. Motor units were recruited over an interval starting from contraction onset (range: 22-233 ms). Upon recruitment, discharge rates declined from an initial rate (range: 89-212 pulses per second), with varying likelihood of doublet (interspike interval of 3 ms; range: 0-50%). Finally, muscular adaptations were modeled by changing average twitch contraction time (range: 42-78 ms). Spectral analysis showed that the effective neural drive to the simulated muscle had smaller bandwidths than the average motor unit twitch, indicating that the bandwidth of the motor output, and thus the capacity for explosive force, was limited mainly by neural properties. The simulated RFD increased by 1,050 ± 281% maximal voluntary contraction force per second from the longest to the shortest recruitment interval. This effect was more than fourfold higher than the effect of increasing the initial discharge rate, more than fivefold higher than the effect of increasing the chance of doublets, and more than sixfold higher than the effect of decreasing twitch contraction times. The simulated results suggest that the physiological variation of the rate by which motor units are recruited during ballistic contractions is the main determinant for the variability in RFD across individuals.NEW & NOTEWORTHY An important limitation of human performance is the ability to generate explosive movements by means of rapid development of muscle force. The physiological determinants of this ability, however, are poorly understood. In this study, we show using extensive simulations that the rate by which motor units are recruited is the main limiting factor for maximum rate of force development.