Finger Force Research Articles

Effects of wrist and finger posture on finger independence

Intended actions of one finger produce involuntary movement or force in other fingers. Mechanical and neural factors limit finger independence. The interplay between anatomical factors, wrist and finger postures, and finger independence remain unclear. The purpose of this study was to determine the effects of wrist and metacarpophalangeal (MCP) posture on involuntary finger forces and extensor digitorum (ED) activity. Twenty participants performed submaximal isometric finger extensions in three wrist positions (30° extension, neutral, and 30° flexion) and two MCP postures (straight and 90° flexion). Involuntary index finger force increased with MCP flexion, suggesting the importance of intertendinous connections in finger independence. Consistent with previous research, ED activity was generally higher in wrist extension than neutral and flexed postures. Understanding the role of passive properties within the hand may help us improve finger rehabilitation strategies.

The Amount and Pattern of Reciprocal Compensations Predict Performance Stability in a Visually Guided Finger Force Production Task.

Previous work suggests that synergistic activity among motor elements implicated in force production tasks underlies enhanced performance stability associated with visual feedback. A hallmark of synergistic activity is reciprocal compensation, that is, covariation in the states of motor elements that stabilizes critical performance variables. The present study examined if characteristics of reciprocal compensation are indicators of individuals' capacity to respond adaptively to variations in the resolution of visual feedback about criterion performance. Twenty healthy adults (19.25 ± 1.25years; 15 females and five males) pressed two sensors with their index fingers to produce a total target force equivalent to 20% of their maximal voluntary contraction under nine conditions that differed in the spatial resolution of real-time feedback about their performance. By combining within-trial uncontrolled manifold and sample entropy analyses, we quantified the amount and degree of irregularity (i.e.,non-repetitiveness) of reciprocal compensations over time. We found a U-shaped relationship between performance stability and gain. Importantly, this relationship was moderated by the degree of irregularity of reciprocal compensation. Lower irregularity in reciprocal compensation patterns was related to individuals' capacity to maintain (or minimize losses in) performance under changes in feedback resolution. Results invite future investigation into how interindividual variations in reciprocal compensation patterns relate to differences in control strategies supporting adaptive responses in complex, visually guided motor tasks.

Impairment of Multi-Finger Force Control in Adult Patients with Dyskinetic Cerebral Palsy

PURPOSE: This study aimed to investigate the impact of cerebral palsy (CP) on motor control strategies, with a focus on understanding the altered capability of multi-finger force control in adult patients with dyskinetic CP.METHODS: Eight adults with dyskinetic CP and ten age- and sex-matched healthy controls participated in this study. The participants performed three force production tasks using four fingers of the dominant hand under isometric conditions: maximum voluntary contraction (MVC) to assess maximum force production, a ramp task to evaluate finger interdependency (enslaving), and a pulse task to examine the ability to rapidly and precisely adjust force. The co-contraction index (CCI) of finger flexor and extensor was also determined during steady state and pulse force production.RESULTS: Patients with dyskinetic CP demonstrated significant impairments in finger force control compared to healthy controls. Specifically, the accuracy and consistency of force production were reduced in the CP group, and they exhibited a longer time to peak pulse force. The CP group also showed increased finger interdependency and elevated CCI during pulse force production, suggesting a greater reliance on co-contraction and less efficient motor control strategies.CONCLUSIONS: This study highlights the distinct motor control deficits in individuals with dyskinetic CP, particularly in tasks requiring rapid and precise finger force adjustment. These results have important implications for the functional rehabilitation of patients with CP. Therapies aimed at reducing excessive co-contraction and decreasing finger interdependency may be beneficial for improving muscle coordination and overall motor function.

Towards higher load capacity: innovative design of a robotic hand with soft jointed structure

In this paper, the innovative design of a robotic hand with soft jointed structure is carried out and a tendon-driven mechanism, a master-slave motor coordinated drive mechanism, a thumb coupling transmission mechanism and a thumb steering mechanism are proposed. These innovative designs allow for more effective actuation in each finger, enhancing the load capacity of the robotic hand while maintaining key performance indicators such as dexterity and adaptability. A mechanical model of the robotic finger was made to determine the application limitations and load capacity. The robotic hand was then prototyped for a set of experiments. The experimental results showed that the proposed theoretical model were reliable. Also, the fingertip force of the robotic finger could reach up to 10.3 N, and the load force could reach up to 72.8 N. When grasping target objects of different sizes and shapes, the robotic hand was able to perform the various power grasping and precision grasping in the Cutkosky taxonomy. Moreover, the robotic hand had good flexibility and adaptability by means of adjusting the envelope state autonomously.

Mechanoreception of pneumatic soft robotic finger without tactile sensor based on dual-position feature

Purpose Mechanoreception is crucial for robotic planning and control applications, and for robotic fingers, mechanoreception is generally obtained through tactile sensors. As a new type of robotic finger, the soft finger also requires mechanoreception, like contact force and object stiffness. Unlike rigid fingers, soft fingers have elastic structures, meaning there is a connection between force and deformation of the soft fingers. It allows soft fingers to achieve mechanoreception without using tactile sensors. This study aims to provide a mechanoreception sensing scheme of the soft finger without any tactile sensors. Design/methodology/approach This research uses bending sensors to measure the actual bending state under force and calculates the virtual bending state under assumed no-load conditions using pressure sensors and statics model. The difference between the virtual and actual finger states is the finger deformation under load, and its product with the finger stiffness can be used to calculate the contact force. There are distinctions between the virtual and actual finger state change rates in the pressing process. The difference caused by the stiffness of different objects is different, which can be used to identify the object stiffness. Findings Contact force perception can achieve a detection accuracy of 0.117 N root mean square error within the range of 0–6 N contact force. The contact object stiffness perception has a detection average deviation of about 15%, and the detection standard deviation is 10% for low-stiffness objects and 20% for high-stiffness objects. It performs better at detecting the stiffness of low-stiffness objects, which is consistent with the sensory ability of human fingers. Originality/value This paper proposes a universal mechanoreception method for soft fingers that only uses indispensable bending and pressure sensors without tactile sensors. It helps to reduce the hardware complexity of soft robots. Meanwhile, the soft finger no longer needs to deploy the tactile sensor at the fingertip, which can benefit the optimization design of the fingertip structure without considering the complex sensor installation. On the other hand, this approach is no longer confined to adding components needed. It can fully use the soft robot body’s physical elasticity to convert sensor signals. Essentially, It treats the soft actuators as soft sensors.

Two aspects of feed-forward control of action stability: effects of action speed and unexpected events.

We explored two types of anticipatory synergy adjustments (ASA) during accurate four-finger total force production task. The first type is a change in the index of force-stabilizing synergy during a steady state when a person is expecting a signal to produce a quick force change, which is seen even when the signal does not come (steady-state ASA). The other type is the drop in in the synergy index prior to a planned force change starting at a known time (transient ASA). The subjects performed a task of steady force production at 10% of maximal voluntary contraction (MVC) followed by a ramp to 20% MVC over 1s, 3s, and as a step function (0s). In another task, in 50% of the trials during the steady-state phase, an unexpected signal could come requiring a quick force pulse to 20% MVC (0-surprise). Inter-trial variance in the finger force space was used to quantify the index of force-stabilizing synergy within the uncontrolled manifold hypothesis. We observed significantly lower synergy index values during the steady state in the 0-ramp trials compared to the 1-ramp and 3-ramp trials. There was also larger transient ASA during the 0-ramp trials. In the 0-surprise condition, the synergy index was significantly higher compared to the 0-ramp condition whereas the transient ASA was significantly larger. The finding of transient ASA scaling is of importance for clinical studies, which commonly involve populations with slower actions, which can by itself be associated with smaller ASAs. The participants varied the sharing pattern of total force across the fingers more in the task with "surprises". This was coupled to more attention to precision of performance, i.e., inter-trial deviations from the target as reflected in smaller variance affecting total force, possibly reflecting higher concentration on the task, which the participants perceived as more challenging compared to a similar task without surprise targets.

Direct measurement for hand-transmitted vibration of human fingers-hand-arm system in the zero-gravity environment

This paper proposes a direct measurement method to obtain the mechanical impedance and vibration transmissibility of the fingers-hand-arm system when operating power tools based on zero-gravity environment simulations. A hand-transmitted vibration measurement system with a suspension device for zero-gravity simulation is first built. The two-step dynamic measurement method is used to measure the dynamic force and acceleration of fingers and palm directly. The forearm and upper arm’s acceleration is also measured using the two-step dynamic measurement method. Based on eliminating the effect of the handle’s effective mass on the mechanical impedance, the mechanical impedance and vibration transmissibility of the human fingers-hand-arm system during the actual operation of the power tool are calculated by using the measured data, and the biodynamic response characteristics of the fingers-hand-arm system at different frequencies are analyzed. The results show that, under the condition of the ISO standardized operating posture (ISO 10819, 2013), the mechanical impedance and vibration transmissibility curves obtained by direct measurement of 21 subjects have remarkable consistency respectively. Compared with the experimental data in the gravity environment, the mechanical impedance and the vibration transmissibility obtained based on the zero-gravity simulation are more consistent with the working conditions of the on-orbit maintenance power tools.

Decomposing Task-Relevant Information From Surface Electromyogram for User-Generic Dexterous Finger Force Decoding.

Existing electromyographic (EMG) based motor intent detection algorithms are typically user-specific, and a generic model that can quickly adapt to new users is highly desirable. However, establishing such a model remains a challenge due to high inter-person variability and external interference with EMG signals. In this study, we present a feature disentanglement approach, implemented by an autoencoder-like architecture, designed to decompose user-invariant, motor-task-sensitive high-level representations from user-sensitive, task-irrelevant representations in EMG amplitude features. Our method is user-generic and can be applied to unseen users for continuous multi-finger force predictions. We evaluated our approach on eight subjects, predicting the force of three fingers (index, middle, and ring-pinky) concurrently. We assessed the decoder's performance through a rigorous leave-one-subject-out validation. Our developed approach consistently outperformed both the conventional EMG amplitude method and a commonly used feature projection approach, principal component analysis (PCA), with a lower force prediction error (RMSE: 6.91 ± 0.45 % MVC; R2: 0.835 ± 0.026) and a higher finger classification accuracy (83.0 ± 4.5%). The comparison with the state-of-the-art neural networks further demonstrated the superior performance of our method in user-generic force predictions. Overall, our methods provide novel insights into the development of user-generic and accurate neural decoding for myoelectric control of assistive robotic hands.

Highly sensitive and easy-to-attach wearable sensor for measuring finger force based on curvature changes in an ellipse-shaped finger ring

Technologies for digitizing worker actions to enhance human labor tasks, mitigate accidents, and prevent disabling injuries have garnered significant attention. This study focuses on monitoring the force exerted by the fingers and developing a wearable fingertip force sensor based on a simple elliptical ring structure in conjunction with a commercially available resistive bend sensor. Resembling a ring accessory, the sensor is easy to attach and detach, and exhibits high sensitivity, with a resistance change of approximately 9% for a fingertip load of 1 N. Furthermore, to mitigate crosstalk during finger flexion, we propose a combined configuration employing this ring-shaped sensor alongside another sensor designed for measuring and rectifying finger flexion angles. Additionally, we introduce an empirically derived fitting function and a straightforward calibration procedure to extract the function’s parameters. The proposed system achieves an average RMS error of 0.53 N for force estimations of approximately 5 N, even during finger flexion and postural changes.

Three Levels of Neural Control Contributing to Performance-stabilizing Synergies in Multi-finger Tasks

We tested a hypothesis on force-stabilizing synergies during four-finger accurate force production at three levels: (1) The level of the reciprocal and coactivation commands, estimated as the referent coordinate and apparent stiffness of all four fingers combined; (2) The level of individual finger forces; and (3) The level of firing of individual motor units (MU). Young, healthy participants performed accurate four-finger force production at a comfortable, non-fatiguing level under visual feedback on the total force magnitude. Mechanical reflections of the reciprocal and coactivation commands were estimated using small, smooth finger perturbations applied by the “inverse piano” device. Firing frequencies of motor units in the flexor digitorum superficialis (FDS) and extensor digitorum communis (EDC) were estimated using surface recording. Principal component analysis was used to identify robust MU groups (MU-modes) with parallel changes in the firing frequency. The framework of the uncontrolled manifold hypothesis was used to compute synergy indices in the spaces of referent coordinate and apparent stiffness, finger forces, and MU-mode magnitudes. Force-stabilizing synergies were seen at all three levels. They were present in the MU-mode spaces defined for MUs in FDS, in EDC, and pooled over both muscles. No effects of hand dominance were seen. The synergy indices defined at different levels of analysis showed no correlations across the participants. The findings are interpreted within the theory of control with spatial referent coordinates for the effectors. We conclude that force stabilization gets contributions from three levels of neural control, likely associated with cortical, subcortical, and spinal circuitry.

Clinically relevant estimation of minimal number of trials for the uncontrolled manifold analysis

Movement biomarkers are crucial for assessing sensorimotor impairments and tracking the effects of interventions over time. The Uncontrolled Manifold (UCM) analysis has been proposed as a novel biomarker for evaluating movement stability and coordination in various motor tasks across neurological and musculoskeletal disorders. Through inter-trial analysis, the UCM partitions the variance of elemental variables (e.g., finger forces) into components that affect (VORT) and do not affect (VUCM) a performance variable (e.g., total force). A third index, ΔV, is computed as the normalized difference between VORT and VUCM. However, the minimum number of trials required to achieve stable UCM estimates, considering its clinimetric properties, is unknown. This study aimed to determine the minimal number (N) of trials for UCM estimates by computing bootstrap estimates of standard errors (SE) at different N trials using thresholds based on the minimal detectable change (MDC, i.e., the minimum change in an outcome measure beyond measurement error). Thirteen adults (24.6 ± 1.1 years old) performed a finger-pressing coordination task. We computed the 95 % confidence intervals (CI) of bootstrap SE distributions for each UCM estimate and detected the lowest number of trials with the 95 % CI of SE below each MDC threshold. We found the minimal N of trials required was VUCM = 14, VORT = 4 and ΔV = 18. Our findings highlight that a relatively low number of trials (i.e., N = 18) are sufficient to compute all UCM estimates beyond the MDC, supporting the use of the UCM framework in clinical settings where many repetitions of a motor task are not practical.

Mechanical Interaction Between the Tendons of the Extrinsic Finger Flexors

ABSTRACTHumans possess an incredible capacity for dexterity, but independent finger control is limited. One factor constraining finger independence is the connections between the tendons of the extrinsic finger muscles. The aim of this study was to assess to what extent the linkages between the distal tendons of flexor digitorum profundus (FDP) and those of the flexor digitorum superficialis (FDS) constrain finger independence. Experiments on human fresh frozen cadaveric upper extremities (n = 6) were performed. First, one finger (target) was flexed whereas the other (nontarget) fingers were held in a nearly extended position. The change (Δ) in total flexion angle (∑Θ; i.e., the sum of angles of the different finger joints) of the target finger from the ∑Θ corresponding to the extended position at the start of the movement until the ∑Θ corresponding to the onset of force exertion at the nontarget fingers was assessed. Second, the distribution of force across the four fingers upon loading the tendon of the target finger was assessed for two finger positions (extended, 90° flexion of metacarpal phalangeal joint). For both muscles and for all fingers, the range of independent movement was small (<7°). Δ∑Θ at force onset was lowest for fingers immediately adjacent to the target finger and highest for more distant fingers. For both muscles and for all fingers, some of the target finger force (<14% for FDP, <2% for FDS) was distributed to the nontarget fingers, which increased (up to 58%) only for FDP in response to target finger flexion. We conclude that mechanical connections between the FDP and FDS tendons constrain finger independence. Such constraints become apparent when moving one finger relative to the other fingers.

Towards Efficient Neural Decoder for Dexterous Finger Force Predictions.

Dexterous control of robot hands requires a robust neural-machine interface capable of accurately decoding multiple finger movements. Existing studies primarily focus on single-finger movement or rely heavily on multi-finger data for decoder training, which requires large datasets and high computation demand. In this study, we investigated the feasibility of using limited single-finger surface electromyogram (sEMG) data to train a neural decoder capable of predicting the forces of unseen multi-finger combinations. We developed a deep forest-based neural decoder to concurrently predict the extension and flexion forces of three fingers (index, middle, and ring-pinky). We trained the model using varying amounts of high-density EMG data in a limited condition (i.e., single-finger data). We showed that the deep forest decoder could achieve consistently commendable performance with 7.0% of force prediction errors and R2 value of 0.874, significantly surpassing the conventional EMG amplitude method and convolutional neural network approach. However, the deep forest decoder accuracy degraded when a smaller amount of data was used for training and when the testing data became noisy. The deep forest decoder shows accurate performance in multi-finger force prediction tasks. The efficiency aspect of the deep forest lies in the short training time and small volume of training data, which are two critical factors in current neural decoding applications. This study offers insights into efficient and accurate neural decoder training for advanced robotic hand control, which has the potential for real-life applications during human-machine interactions.

Using a Sensor-Embedded Baseball to Identify Finger Characteristics Related to Spin Rate and Pitching Velocity in Pitchers.

Previous investigations have shown a positive relationship between baseball pitching velocity and the kinetic chain involved in pitching motion. However, no study has examined the influence of finger characteristics on pitching velocity and rate of spin via a sensor-embedded baseball. Twenty-one pitchers volunteered and were recruited for this study. An experimental baseball embedded with a force sensor and an inertial measurement unit was designed for pitching performance measurement. Finger length and strength were measured as dependent variables. Spin rate and velocity were independent variables. Pearson product-moment correlations (r) and intraclass correlation coefficients (ICCs) determined the relationship between finger characteristics and pitching performance. Finger length discrepancy, two-point pinch strength, index finger RFD (rate of force development), middle finger impulse, and force discrepancy had significant correlations with spin rate (r = 0.500~0.576, p ≤ 0.05). Finger length discrepancy, two-point pinch, three-point pinch strength, index and middle finger RFD, middle finger impulse, and force combination had significant correlations with fastball pitching velocity (r = 0.491~0.584, p ≤ 0.05). Finger length discrepancy, finger pinch strength, and pitching finger force including maximal force and RFD may be factors that impact fastball spin rate and fastball pitching velocity.

Force matching: motor effects that are not reported by the actor.

We explored unintentional drifts of finger forces during force production and matching task. Based on earlier studies, we predicted that force matching with the other hand would reduce or stop the force drift in instructed fingers while uninstructed (enslaved) fingers remain unaffected. Twelve young, healthy, right-handed participants performed two types of tasks with both hands (task hand and match hand). The task hand produced constant force at 20% of MVC level with the Index and Ring fingers pressing in parallel on strain gauge force sensors. The Middle finger force wasn't instructed, and its enslaved force was recorded. Visual feedback on the total force by the instructed fingers was either present throughout the trial or only during the first 5s (no-feedback condition). The other hand matched the perceived force level of the task hand starting at either 4, 8, or 15s from the trial initiation. No feedback was ever provided for the match hand force. After the visual feedback was removed, the task hand showed a consistent drift to lower magnitudes of total force. Contrary to our prediction, over all conditions, force matching caused a brief acceleration of force drift in the task hand, which then reached a plateau. There was no effect of matching on drifts in enslaved finger force. We interpret the force drifts within the theory of control with spatial referent coordinates as consequences of drifts in the command (referent coordinate) to the antagonist muscles. This command is not adequately incorporated into force perception.

Towards humanlike grasp in robotic hands: mechanical implementation of force synergies

In the field of robotic hands, finger force coordination is usually achieved by complex mechanical structures and control systems. This study presents the design of a novel transmission system inspired from the physiological concept of force synergies, aiming to simplify the control of multifingered robotic hands. To this end, we collected human finger force data during six isometric grasping tasks, and force synergies (i.e. the synergy weightings and the corresponding activation coefficients) were extracted from the concatenated force data to explore their potential for force modulation. We then implemented two force synergies with a cable-driven transmission mechanism consisting of two spring-loaded sliders and five V-shaped bars. Specifically, we used fixed synergy weightings to determine the stiffness of the compression springs, and the displacements of sliders were determined by time-varying activation coefficients. The derived transmission system was then used to drive a five-finger robotic hand named SYN hand. We also designed a motion encoder to selectively activate desired fingers, making it possible for two motors to empower a variety of hand postures. Experiments on the prototype demonstrate successful grasp of a wide range of objects in everyday life, and the finger force distribution of SYN hand can approximate that of human hand during six typical tasks. To our best knowledge, this study shows the first attempt to mechanically implement force synergies for finger force modulation in a robotic hand. In comparison to state-of-the-art robotic hands with similar functionality, the proposed hand can distribute humanlike force ratios on the fingers by simple position control, rather than resorting to additional force sensors or complex control strategies. The outcome of this study may provide alternatives for the design of novel anthropomorphic robotic hands, and thus show application prospects in the field of hand prostheses and exoskeletons.

Post-Stroke Impairments of Manual Dexterity and Finger Proprioception: Their Contribution to Upper Limb Activity Capacity

Background Knowing how impaired manual dexterity and finger proprioception affect upper limb activity capacity is important for delineating targeted post-stroke interventions for upper limb recovery. Objectives To investigate whether impaired manual dexterity and finger proprioception explain variance in post-stroke activity capacity, and whether they explain more variance than conventional clinical assessments of upper limb sensorimotor impairments. Methods Activity capacity and hand sensorimotor impairments were assessed using clinical measures in N = 42 late subacute/chronic hemiparetic stroke patients. Dexterity was evaluated using the Dextrain Manipulandum to quantify accuracy of visuomotor finger force-tracking (N = 36), timing of rhythmic tapping (N = 36), and finger individuation (N = 24), as well as proprioception (N = 27). Stepwise multivariate and hierarchical linear regression models were used to identify impairments best explaining activity capacity. Results Dexterity and proprioceptive components significantly increased the variance explained in activity capacity: (i) Box and Block Test was best explained by baseline tonic force during force-tracking and tapping frequency (adjusted R2 = .51); (ii) Motor Activity Log was best explained by success rate in finger individuation (adjusted R2 = .46); (iii) Action Research Arm Test was best explained by release of finger force and proprioceptive measures (improved reaction time related to use of proprioception; adjusted R2 = .52); and (iv) Moberg Pick-Up test was best explained by proprioceptive function (adjusted R2 = .18). Models excluding dexterity and proprioception variables explained up to 19% less variance. Conclusions Manual dexterity and finger proprioception explain unique variance in activity capacity not captured by conventional impairment measures and should be assessed when considering the underlying causes of post-stroke activity capacity limitations. URL: https://www.clinicaltrials.gov. Unique identifier: NCT03934073.

Comparison of the activation level in the sensorimotor cortex between motor point and proximal nerve bundle electrical stimulation

Objective. Neuromuscular electrical stimulation (NMES) is widely used for motor function rehabilitation in stroke survivors. Compared with the conventional motor point (MP) stimulation, the stimulation at the proximal segment of the peripheral nerve (PN) bundles has been demonstrated to have multiple advantages. However, it is not known yet whether the PN stimulation can increase the cortical activation level, which is crucial for motor function rehabilitation. Approach. The current stimuli were delivered transcutaneously at the muscle belly of the finger flexors and the proximal segment of the median and ulnar nerves, respectively for the MP and PN stimulation. The stimulation intensity was determined to elicit the same contraction levels between the two stimulation methods in 18 healthy individuals and a stroke patient. The functional near-infrared spectroscopy and the electromyogram were recorded to compare the activation pattern of the sensorimotor regions and the target muscles. Main Results. For the healthy subjects, the PN stimulation induced significantly increased concentration of the oxygenated hemoglobin in the contralateral sensorimotor areas, and enhanced the functional connectivity between brain regions compared with the MP stimulation. Meanwhile, the compound action potentials had a smaller amplitude and the H-reflex became stronger under the PN stimulation, indicating that more sensory axons were activated in the PN stimulation. For the stroke patient, the PN stimulation can elicit finger forces and induce activation of both the contralateral and ipsilateral motor cortex. Conclusions. Compared with the MP stimulation, the PN stimulation can induce more cortical activation in the contralateral sensorimotor areas possibly via involving more activities in the central pathway. Significance. This study demonstrated the potential of the PN stimulation to facilitate functional recovery via increasing the cortical activation level, which may help to improve the outcome of the NMES-based rehabilitation for motor function recovery after stroke.

Unsupervised neural decoding for concurrent and continuous multi-finger force prediction

Reliable prediction of multi-finger forces is crucial for neural-machine interfaces. Various neural decoding methods have progressed substantially for accurate motor output predictions. However, most neural decoding methods are performed in a supervised manner, i.e., the finger forces are needed for model training, which may not be suitable in certain contexts, especially in scenarios involving individuals with an arm amputation. To address this issue, we developed an unsupervised neural decoding approach to predict multi-finger forces using spinal motoneuron firing information. We acquired high-density surface electromyogram (sEMG) signals of the finger extensor muscle when subjects performed single-finger and multi-finger tasks of isometric extensions. We first extracted motor units (MUs) from sEMG signals of the single-finger tasks. Because of inevitable finger muscle co-activation, MUs controlling the non-targeted fingers can also be recruited. To ensure an accurate finger force prediction, these MUs need to be teased out. To this end, we clustered the decomposed MUs based on inter-MU distances measured by the dynamic time warping technique, and we then labeled the MUs using the mean firing rate or the firing rate phase amplitude. We merged the clustered MUs related to the same target finger and assigned weights based on the consistency of the MUs being retained. As a result, compared with the supervised neural decoding approach and the conventional sEMG amplitude approach, our new approach can achieve a higher R2 (0.77 ± 0.036 vs. 0.71 ± 0.11 vs. 0.61 ± 0.09) and a lower root mean square error (5.16 ± 0.58 %MVC vs. 5.88 ± 1.34 %MVC vs. 7.56 ± 1.60 %MVC). Our findings can pave the way for the development of accurate and robust neural-machine interfaces, which can significantly enhance the experience during human-robotic hand interactions in diverse contexts.

Proactive response preparation contributes to contingency learning: novel evidence from force-sensitive keyboards.

Contingency learning can involve learning that the identity of one stimulus in a sequence predicts the identity of the next stimulus. It remains unclear, however, whether such learning speeds responses to the next stimulus only by reducing the threshold for triggering the expected response after stimulus onset or also by preparing the expected response before stimulus onset. To distinguish between these competing accounts, we manipulated the probabilities with which each of two prime arrows (Left and Right) were followed by each of two probe arrows (Up and Down) in a prime-probe task while using force-sensitive keyboards to monitor sub-threshold finger force. Consistent with the response preparation account, two experiments revealed greater force just before probe onset on the response key corresponding to the direction in which the probe was more (versus less) likely to point (e.g., Up vs. Down). Furthermore, mirroring sequential contingency effects in behavior, this pre-probe force effect vanished after a single low-probability trial. These findings favor the response preparation account over the threshold only account. They also suggest the possibility that contingency learning in our tasks indexes trial-by-trial expectations regarding the utility of the prime for predicting the upcoming probe.