Time-independent relationship between digits closure velocity and hand transport acceleration during reach-to-grasp movements.

Prehension movements in primates have been extensively studied for decades, and hand transport and hand grip adjustment are usually considered as the main components of any object reach-to-grasp action. Evident temporal patterns were found for the velocity of the hand during the transport phase and for the digits kinematics during pre-shaping and enclosing phases. However, such kinematics were always analysed separately in regard to time, and never studied in terms of dependence one from another. Nevertheless, if a reliable one-to-one relationship is proven, it would allow reconstructing the digit velocity (and position) simply by knowing the hand acceleration during reaching motions towards the target object, ceasing the usual dependence seen in literature from time of movement and distance from the target. In this study, the aim was precisely to analyse reach-to-grasp motions to explore if such relationship exists and how it can be formulated. Offline and real-time results not only seem to suggest the existence of a time-independent, one-to-one relationship between hand transport and hand grip adjustment, but also that such relationship is quite resilient to the different intrinsic and extrinsic properties of the target objects such as size, shape and position.

An Instrumented Glove for Restoring Sensorimotor Function of the Hand Through Augmented Sensory Feedback.

The loss of sensitivity of the upper limb due to neurological injuries severely limits the ability to manipulate objects, hindering personal independence. Non-invasive augmented sensory feedback techniques are used to promote neural plasticity hence to restore the grasping function. This work presents a wearable device for restoring sensorimotor hand functions based on Discrete Event-driven Sensory Control policy. It consists of an instrumented glove that, relying on piezoelectric sensors, delivers short-lasting vibrotactile stimuli synchronously with the relevant mechanical events (i.e., contact and release) of the manipulation. We first performed a feasibility study on healthy participants (20) that showed overall good performances of the device, with touch-event detection accuracy of 96.2% and a response delay of 22 ms. Later, we pilot tested it on two participants with limited sensorimotor functions. When using the device, they improved their hand motor coordination while performing tests for hand motor coordination assessment (i.e., pick and place test, pick and lift test). In particular, they exhibited more coordinated temporal correlations between grip force and load force profiles and enhanced performances when transferring objects, quantitatively proving the effectiveness of the device.

Cutting Edge Bionics in Highly Impaired Individuals: A Case of Challenges and Opportunities.

Highly impaired individuals stand to benefit greatly from cutting-edge bionic technology, however concurrent functional deficits may complicate the adaptation of such technology. Here, we present a case in which a visually impaired individual with bilateral burn injury amputation was provided with a novel transradial neuromusculoskeletal prosthesis comprising skeletal attachment via osseointegration and implanted electrodes in nerves and muscles for control and sensory feedback. Difficulties maintaining implant hygiene and donning and doffing the prosthesis arose due to his contralateral amputation, ipsilateral eye loss, and contralateral impaired vision necessitating continuous adaptations to the electromechanical interface. Despite these setbacks, the participant still demonstrated improvements in functional outcomes and the ability to control the prosthesis in various limb positions using the implanted electrodes. Our results demonstrate the importance of a multidisciplinary, iterative, and patient-centered approach to making cutting-edge technology accessible to patients with high levels of impairment.

Transcutaneous Magnet Localizer for a Self-Contained Myokinetic Prosthetic Hand.

OBJECTIVE: The search for a physiologically appropriate interface for the control of dexterous hand prostheses is an ongoing challenge in bioengineering. In this context, we proposed an interface, named myokinetic control interface, based on the localization of magnets implanted in the residual limb muscles, to monitor their contractions and send appropriate commands to the artificial hand. As part of such concept, this interface requires a transcutaneous magnet localizer that can be integrated in a self-contained limb prosthesis, a feature yet to be realized within the current state of the art. METHODS: In an attempt to cover this gap, here we present a modular embedded system consisting of a computation unit able to acquire synchronized samples captured by up to eight acquisition units, so to localize multiple magnets. RESULTS: The system exhibits short computation times (<60ms) and power consumption (0.6-1.2W) which are suitable for use in a clinically viable prosthetic arm. The system proved able to localize magnets while moving at speeds in the range of physiological movements (<0.24m/s), with high accuracy (<1mm) and precision (<0.5mm). CONCLUSION: We demonstrated a system suitable for the implementation of a self-contained myokinetic prosthetic hand. SIGNIFICANCE: These results pave the way towards the clinical implementation of the myokinetic interface, with amputees controlling an artificial arm by means of implanted magnets.

A highly integrated bionic hand with neural control and feedback for use in daily life.

Restoration of sensorimotor function after amputation has remained challenging because of the lack of human-machine interfaces that provide reliable control, feedback, and attachment. Here, we present the clinical implementation of a transradial neuromusculoskeletal prosthesis-a bionic hand connected directly to the user's nervous and skeletal systems. In one person with unilateral below-elbow amputation, titanium implants were placed intramedullary in the radius and ulna bones, and electromuscular constructs were created surgically by transferring the severed nerves to free muscle grafts. The native muscles, free muscle grafts, and ulnar nerve were implanted with electrodes. Percutaneous extensions from the titanium implants provided direct skeletal attachment and bidirectional communication between the implanted electrodes and a prosthetic hand. Operation of the bionic hand in daily life resulted in improved prosthetic function, reduced postamputation, and increased quality of life. Sensations elicited via direct neural stimulation were consistently perceived on the phantom hand throughout the study. To date, the patient continues using the prosthesis in daily life. The functionality of conventional artificial limbs is hindered by discomfort and limited and unreliable control. Neuromusculoskeletal interfaces can overcome these hurdles and provide the means for the everyday use of a prosthesis with reliable neural control fixated into the skeleton.

HANDdata - first-person dataset including proximity and kinematics measurements from reach-to-grasp actions.

HANDdata is a dataset designed to provide hand kinematics and proximity vision data during reach to grasp actions of non-virtual objects, specifically tailored for autonomous grasping of a robotic hand, and with particular attention to the reaching phase. Thus, we sought to capture target object characteristics from radar and time-of-flight proximity sensors, as well as details of the reach-to-grasp action by looking at wrist and fingers kinematics, and at hand-object interaction main events. We structured the data collection as a sequence of static and grasping tasks, organized by increasing levels of complexity. HANDdata is a first-person, reach-to-grasp dataset that includes almost 6000 human-object interactions from 29 healthy adults, with 10 standardized objects of 5 different shapes and 2 kinds of materials. We believe that such data collection can be of value for researchers interested in autonomous grasping robots for healthcare and industrial applications, as well as for those interested in radar-based computer vision and in basic aspects of sensorimotor control and manipulation.

To What Extent Implanting Single vs Pairs of Magnets Per Muscle Affect the Localization Accuracy of the Myokinetic Control Interface? Evidence From a Simulated Environment.

OBJECTIVE: We recently proposed a new concept of human-machine interface to control hand prostheses which we dubbed the myokinetic control interface. Such interface detects muscle displacement during contraction by localizing permanent magnets implanted in the residual muscles. So far, we evaluated the feasibility of implanting one magnet per muscle and monitoring its displacement relative to its initial position. However, multiple magnets could actually be implanted in each muscle, as using their relative distance as a measure of muscle contraction could improve the system robustness against environmental disturbances. METHODS: Here, we simulated the implant of pairs of magnets in each muscle and we compared the localization accuracy of such system with the one magnet per muscle approach, considering first a planar and then an anatomically appropriate configuration. Such comparison was also performed when simulating different grades of mechanical disturbances applied to the system (i.e., shift of the sensor grid). RESULTS: We found that implanting one magnet per muscle always led to lower localization errors under ideal conditions (i.e., no external disturbances). Differently, when mechanical disturbances were applied, magnet pairs outperformed the single magnet approach, confirming that differential measurements are able to reject common mode disturbances. CONCLUSION: We identified important factors affecting the choice of the number of magnets to implant in a muscle. SIGNIFICANCE: Our results provide important guidelines for the design of disturbance rejection strategies and for the development of the myokinetic control interface, as well as for a whole range of biomedical applications involving magnetic tracking.

Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier.

The design of prosthetic controllers by means of neurophysiological signals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the transient EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of ~96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of ~89%. Importantly, for each amputee, it produced at least one acceptable combination of wrist-hand movements (i.e., with accuracy >85%). Regarding the cross-subject classifier, while our algorithm obtained promising results with non-amputees (accuracy up to ~80%), they were not as good with amputees (accuracy up to ~35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments.

Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography.

Although seemingly effortless, the control of the human hand is backed by an elaborate neuro-muscular mechanism. The end result is typically a smooth action with the precise positioning of the joints of the hand and an exerted force that can be modulated to enable precise interaction with the surroundings. Unfortunately, even the most sophisticated technology cannot replace such a comprehensive role but can offer only basic hand functionalities. This issue arises from the drawbacks of the prosthetic hand control strategies that commonly rely on surface EMG signals that contain a high level of noise, thus limiting accurate and robust multi-joint movement estimation. The use of intramuscular EMG results in higher quality signals which, in turn, lead to an improvement in prosthetic control performance. Here, we present the evaluation of fourteen common/well-known algorithms (mean absolute value, variance, slope sign change, zero crossing, Willison amplitude, waveform length, signal envelope, total signal energy, Teager energy in the time domain, Teager energy in the frequency domain, modified Teager energy, mean of signal frequencies, median of signal frequencies, and firing rate) for the direct and proportional control of a prosthetic hand. The method involves the estimation of the forces generated in the hand by using different algorithms applied to iEMG signals from our recently published database, and comparing them to the measured forces (ground truth). The results presented in this paper are intended to be used as a baseline performance metric for more advanced algorithms that will be made and tested using the same database.

Data-Driven Real-Time Magnetic Tracking Applied to Myokinetic Interfaces.

A new concept of human-machine interface to control hand prostheses based on displacements of multiple magnets implanted in the limb residual muscles, the myokinetic control interface, has been recently proposed. In previous works, magnets localization has been achieved following an optimization procedure to find an approximate solution to an analytical model. To simplify and speed up the localization problem, here we employ machine learning models, namely linear and radial basis functions artificial neural networks, which can translate measured magnetic information to desired commands for active prosthetic devices. They were developed offline and then implemented on field-programmable gate arrays using customized floating-point operators. We optimized computational precision, execution time, hardware, and energy consumption, as they are essential features in the context of wearable devices. When used to track a single magnet in a mockup of the human forearm, the proposed data-driven strategy achieved a tracking accuracy of 720 µm 95% of the time and latency of 12.07 µs. The proposed system architecture is expected to be more power-efficient compared to previous solutions. The outcomes of this work encourage further research on improving the devised methods to deal with multiple magnets simultaneously.

Testing silicone digit extensions as a way to suppress natural sensation to evaluate supplementary tactile feedback.

Dexterous use of the hands depends critically on sensory feedback, so it is generally agreed that functional supplementary feedback would greatly improve the use of hand prostheses. Much research still focuses on improving non-invasive feedback that could potentially become available to all prosthesis users. However, few studies on supplementary tactile feedback for hand prostheses demonstrated a functional benefit. We suggest that confounding factors impede accurate assessment of feedback, e.g., testing non-amputee participants that inevitably focus intently on learning EMG control, the EMG's susceptibility to noise and delays, and the limited dexterity of hand prostheses. In an attempt to assess the effect of feedback free from these constraints, we used silicone digit extensions to suppress natural tactile feedback from the fingertips and thus used the tactile feedback-deprived human hand as an approximation of an ideal feed-forward tool. Our non-amputee participants wore the extensions and performed a simple pick-and-lift task with known weight, followed by a more difficult pick-and-lift task with changing weight. They then repeated these tasks with one of three kinds of audio feedback. The tests were repeated over three days. We also conducted a similar experiment on a person with severe sensory neuropathy to test the feedback without the extensions. Furthermore, we used a questionnaire based on the NASA Task Load Index to gauge the subjective experience. Unexpectedly, we did not find any meaningful differences between the feedback groups, neither in the objective nor the subjective measurements. It is possible that the digit extensions did not fully suppress sensation, but since the participant with impaired sensation also did not improve with the supplementary feedback, we conclude that the feedback failed to provide relevant grasping information in our experiments. The study highlights the complex interaction between task, feedback variable, feedback delivery, and control, which seemingly rendered even rich, high-bandwidth acoustic feedback redundant, despite substantial sensory impairment.

Optimal Spatial Sensor Design for Magnetic Tracking in a Myokinetic Control Interface.

BACKGROUND AND OBJECTIVES: Magnetic tracking involves the use of magnetic sensors to localize one or more magnetic objectives, in those applications in which a free line-of-sight between them and the operator is hampered. We applied this concept to prosthetic hands, which could be controlled by tracking permanent magnets implanted in the forearm muscles of amputees (the myokinetic control interface). Concerning the system design, the definition of a sensor distribution which maximizes the information, while minimizing the computational cost of localization, is still an open problem. We present a simple yet effective strategy to define an optimal sensor set for tracking multiple magnets, which we called the Peaks method. METHODS: We simulated a proximal amputation using a 3D CAD model of a human forearm, and the implantation of 11 magnets in the residual muscles. The Peaks method was applied to select a subset of sensors from an initial grid of 480 elements. The approach involves setting an appropriate threshold to select those sensors associated with the peaks in the magnetic flux density and its gradient distributions. Selected sensors were used to track the magnets during muscle contraction. For validating our strategy, an alternative method based on state-of-the-art solutions was implemented. We finally proposed a calibration phase to customize the sensor distribution on the specific patient's anatomy. RESULTS: 80 sensors were selected with the Peaks method, and 101 with the alternative one. A localization accuracy below 0.22 mm and 1.86° for position and orientation, respectively, was always achieved. Unlike alternative methods from the literature, neither iterative or analytical solution, nor a-priori knowledge on the magnet positions or trajectories were required, and yet the outcomes achieved with the two strategies proved statistically comparable. The calibration phase proved useful to adapt the sensors to the patient's stump and to increase the signal-to-noise ratio against intrinsic noise. CONCLUSIONS: We demonstrated an efficient and general solution for solving the design optimization problem (i.e. identifying an optimal sensor set) and reducing the computational cost of localization. The optimal sensor distribution mirrors the field shape traced by the magnets on the sensing surface, being an intuitive and fast way of achieving the same results of more complex and application-specific methods. Several applications in the (bio)medical field involving magnetic tracking will benefit from the outcomes of this work.

Neurophysiology of slip sensation and grip reaction: insights for hand prosthesis control of slippage.

Sensory feedback is pivotal for a proficient dexterity of the hand. By modulating the grip force in function of the quick and not completely predictable change of the load force, grabbed objects are prevented to slip from the hand. Slippage control is an enabling achievement to all manipulation abilities. However, in hand prosthetics, the performance of even the most innovative research solutions proposed so far to control slippage remain distant from the human physiology. Indeed, slippage control involves parallel and compensatory activation of multiple mechanoceptors, spinal and supraspinal reflexes, and higher-order voluntary behavioral adjustments. In this work, we reviewed the literature on physiological correlates of slippage to propose a three-phases model for the slip sensation and reaction. Furthermore, we discuss the main strategies employed so far in the research studies that tried to restore slippage control in amputees. In the light of the proposed three-phase slippage model and from the weaknesses of already implemented solutions, we proposed several physiology-inspired solutions for slippage control to be implemented in the future hand prostheses. Understanding the physiological basis of slip detection and perception and implementing them in novel hand feedback system would make prosthesis manipulation more efficient and would boost its perceived naturalness, fostering the sense of agency for the hand movements.

Feasibility of generating 90 Hz vibrations in remote implanted magnets.

Limb amputation not only reduces the motor abilities of an individual, but also destroys afferent channels that convey essential sensory information to the brain. Significant efforts have been made in the area of upper limb prosthetics to restore sensory feedback, through the stimulation of residual sensory elements. Most of the past research focused on the replacement of tactile functions. On the other hand, the difficulties in eliciting proprioceptive sensations using either haptic or (neural) electrical stimulation, has limited researchers to rely on sensory substitution. Here we propose the myokinetic stimulation interface, that aims at restoring natural proprioceptive sensations by exploiting the so-called tendon illusion, elicited through the vibration of magnets implanted inside residual muscles. We present a prototype which exploits 12 electromagnetic coils to vibrate up to four magnets implanted in a forearm mockup. The results demonstrated that it is possible to generate highly directional and frequency-selective vibrations. The system proved capable of activating a single magnet, out of many. Hence, this interface constitutes a promising approach to restore naturally perceived proprioception after an amputation. Indeed, by implanting several magnets in independent muscles, it would be possible to restore proprioceptive sensations perceived as coming from single digits.

Proprioceptive Augmentation With Illusory Kinaesthetic Sensation in Stroke Patients Improves Movement Quality in an Active Upper Limb Reach-and-Point Task.

Stroke patients often have difficulty completing motor tasks even after substantive rehabilitation. Poor recovery of motor function can often be linked to stroke-induced damage to motor pathways. However, stroke damage in pathways that impact effective integration of sensory feedback with motor control may represent an unappreciated obstacle to smooth motor coordination. In this study we investigated the effects of augmenting movement proprioception during a reaching task in six stroke patients as a proof of concept. We used a wearable neurorobotic proprioceptive feedback system to induce illusory kinaesthetic sensation by vibrating participants' upper arm muscles over active limb movements. Participants were instructed to extend their elbow to reach-and-point to targets of differing sizes at various distances, while illusion-inducing vibration (90 Hz), sham vibration (25 Hz), or no vibration was applied to the distal tendons of either their biceps brachii or their triceps brachii. To assess the impact of augmented kinaesthetic feedback on motor function we compared the results of vibrating the biceps or triceps during arm extension in the affected arm of stroke patients and able-bodied participants. We quantified performance across conditions and participants by tracking limb/hand kinematics with motion capture, and through Fitts' law analysis of reaching target acquisition. Kinematic analyses revealed that injecting 90 Hz illusory kinaesthetic sensation into the actively contracting (agonist) triceps muscle during reaching increased movement smoothness, movement directness, and elbow extension. Conversely, injecting 90 Hz illusory kinaesthetic sensation into the antagonistic biceps during reaching negatively impacted those same parameters. The Fitts' law analyses reflected similar effects with a trend toward increased throughput with triceps vibration during reaching. Across all analyses, able-bodied participants were largely unresponsive to illusory vibrational augmentation. These findings provide evidence that vibration-induced movement illusions delivered to the primary agonist muscle involved in active movement may be integrated into rehabilitative approaches to help promote functional motor recovery in stroke patients.

Localization accuracy of multiple magnets in a myokinetic control interface.

Magnetic localizers have been widely investigated in the biomedical field, especially for intra-body applications, because they don't require a free line-of-sight between the implanted magnets and the magnetic field sensors. However, while researchers have focused on narrow and specific aspects of the localization problem, no one has comprehensively searched for general design rules for accurately localizing multiple magnetic objectives. In this study, we sought to systematically analyse the effects of remanent magnetization, number of sensors, and geometrical configuration (i.e. distance among magnets-Linter-MM-and between magnets and sensors-LMM-sensor) on the accuracy of the localizer in order to unveil the basic principles of the localization problem. Specifically, through simulations validated with a physical system, we observed that the accuracy of the localization was mainly affected by a specific angle ([Formula: see text] = tan-1(Linter-MM / LMM-sensor)), descriptive of the system geometry. In particular, while tracking nine magnets, errors below ~ 1 mm (10% of the length of the simulated trajectory) and around 9° were obtained if θ ≥ ~ 31°. The latter proved a general rule across all tested conditions, also when the number of magnets was doubled. Our results are interesting for a whole range of biomedical engineering applications exploiting multiple-magnets tracking, such as human-machine interfaces, capsule endoscopy, ventriculostomy interventions, and endovascular catheter navigation.

A database of high-density surface electromyogram signals comprising 65 isometric hand gestures.

Control of contemporary, multi-joint prosthetic hands is commonly realized by using electromyographic signals from the muscles remaining after amputation at the forearm level. Although this principle is trying to imitate the natural control structure where muscles control the joints of the hand, in practice, myoelectric control provides only basic hand functions to an amputee using a dexterous prosthesis. This study aims to provide an annotated database of high-density surface electromyographic signals to aid the efforts of designing robust and versatile electromyographic control interfaces for prosthetic hands. The electromyographic signals were recorded using 128 channels within two electrode grids positioned on the forearms of 20 able-bodied volunteers. The participants performed 65 different hand gestures in an isometric manner. The hand movements were strictly timed using an automated recording protocol which also synchronously recorded the electromyographic signals and hand joint forces. To assess the quality of the recorded signals several quantitative assessments were performed, such as frequency content analysis, channel crosstalk, and the detection of poor skin-electrode contacts.

Stability and in vivo safety of gold, titanium nitride and parylene C coatings on NdFeB magnets implanted in muscles towards a new generation of myokinetic prosthetic limbs.

Rare earth magnets are the elective choice when high magnetic field density is required and they are particularly intriguing for inclusion in implantable devices. A safe implantation of NdFeB magnets in muscles would enable the control of limb prostheses using a myokinetic interface i.e., direct control of artificial limb movements by means of magnetic tracking of residual muscle contractions. However, myokinetic prosthesis control is prevented by NdFeB magnets poor biocompatibility, at present. Here we investigated three biocompatible materials as NdFeB magnet coating candidates, namely gold, titanium nitride and parylene C, which have not been analyzed in a systematic way for this purpose, so far. In vitro testing in a tissue-mimicking environment and upon contact with C2C12 myoblasts enabled assessment of the superiority of parylene C coated magnets in terms of corrosion prevention and lack of cytotoxicity. In addition, parylene C coated magnets implanted in rabbit muscles for 28 days confirmed, both locally and systemically, their biocompatibility, with a lack of irritation and toxicity associated with the implant. These findings pave the way towards the development of implantable devices based on permanent magnets and of a new generation of limb prostheses.

The Myokinetic Control Interface: How Many Magnets Can be Implanted in an Amputated Forearm? Evidence From a Simulated Environment.

We recently introduced the concept of a new human-machine interface (the myokinetic control interface) to control hand prostheses. The interface tracks muscle contractions via permanent magnets implanted in the muscles and magnetic field sensors hosted in the prosthetic socket. Previously we showed the feasibility of localizing several magnets in non-realistic workspaces. Here, aided by a 3D CAD model of the forearm, we computed the localization accuracy simulated for three different below-elbow amputation levels, following general guidelines identified in early work. To this aim we first identified the number of magnets that could fit and be tracked in a proximal (T1), middle (T2) and distal (T3) representative amputation, starting from 18, 20 and 23 eligible muscles, respectively. Then we ran a localization algorithm to estimate the poses of the magnets based on the sensor readings. A sensor selection strategy (from an initial grid of 840 sensors) was also implemented to optimize the computational cost of the localization process. Results showed that the localizer was able to accurately track up to 11 (T1), 13 (T2) and 19 (T3) magnetic markers (MMs) with an array of 154, 205 and 260 sensors, respectively. Localization errors lower than 7% the trajectory travelled by the magnets during muscle contraction were always achieved. This work not only answers the question: "how many magnets could be implanted in a forearm and successfully tracked with a the myokinetic control approach?", but also provides interesting insights for a wide range of bioengineering applications exploiting magnetic tracking.

Online Grasp Force Estimation From the Transient EMG.

Myoelectric upper limb prostheses are controlled using information from the electrical activity of residual muscles (i.e. the electromyogram, EMG). EMG patterns at the onset of a contraction (transient phase) have shown predictive information about upcoming grasps. However, decoding this information for the estimation of the grasp force was so far overlooked. In a previous offline study, we proved that the transient phase of the EMG indeed contains information about the grasp force and determined the best algorithm to extract this information. Here we translated those findings into an online platform to be tested with both non-amputees and amputees. The platform was tested during a pick and lift task (tri-digital grasp) with light objects (200 g - 1 kg), for which fine control of the grasp force is more important. Results show that, during this task, it is possible to estimate the target grasp force with an absolute error of 2.06 (1.32) % and 2.04 (0.49) % the maximum voluntary force for non-amputee and amputees, respectively, using information from the transient phase of the EMG. This approach would allow for a biomimetic regulation of the grasp force of a prosthetic hand. Indeed, the users could contract their muscles only once before the grasp begins with no need to modulate the grasp force for the whole duration of the grasp, as required with continuous classifiers. These results pave the way to fast, intuitive and robust myoelectric controllers of limb prostheses.