Finger Force Control Research Articles

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.

Dexterity in the Acute Phase of Stroke: Impairments and Neural Substrates

Background Stroke can impair manual dexterity, leading to loss of independence following incomplete recovery. Enhancing our understanding of dexterity impairment may improve neurorehabilitation. Objectives The study aimed to measure dexterity components in acute stroke patients with and without hand motor deficits, compare them to those of healthy controls (HC), and to explore the neural substrates involved in specific components of dexterity. Methods We used the Dextrain Manipulandum to quantify fine finger force control, finger selection accuracy, coactivation, and reaction time (RT). Dexterity was evaluated twice (2 days apart) in 74 patients and 14 HC. Voxel-Lesion-Symptom-Mapping (VLSM) was used to analyze the relationship between tissue damage and dexterity. Results. Due to severe paresis or fatigue, 24 patients could not perform these tasks. In 50 patients (included 4.6 ± 3.3 days post-stroke), finger force control improved (P < .001), as it did in HC (P = .03) who performed better than patients on both evaluations. Accuracy of finger selection did not improve significantly in any group, but the HC performed better on both evaluations. Unexpectedly, coactivation was better in patients than in HC at D3 (P = .03). There were no between-group differences in RT. VLSM showed that damage to the superior temporal gyrus (STG) impaired finger force control while damage to the posterior limb of the internal capsule (PLIC) impaired finger selectivity. Conclusions Acute stroke affecting the STG or PLIC impaired selective components of dexterity. Patients with mild to moderate impairment showed better finger force control and accuracy selection within 48 hours, suggesting the feasibility of detecting early dexterity improvements.

Effects of noninvasive neuromodulation targeting the spinal cord on early learning of force control by the digits.

Control of finger forces underlies our capacity for skilled hand movements acquired during development and reacquired after neurological injury. Learning force control by the digits, therefore, predicates our functional independence. Noninvasive neuromodulation targeting synapses that link corticospinal neurons onto the final common pathway via spike-timing-dependent mechanisms can alter distal limb motor output on a transient basis, yet these effects appear subject to individual differences. Here, we investigated how this form of noninvasive neuromodulation interacts with task repetition to influence early learning of force control during precision grip. The unique effects of neuromodulation, task repetition, and neuromodulation coinciding with task repetition were tested in three separate conditions using a within-subject, cross-over design (n = 23). We found that synchronizing depolarization events within milliseconds of stabilizing precision grip accelerated learning but only after accounting for individual differences through inclusion of subjects who showed upregulated corticospinal excitability at 2 of 3 time points following conditioning stimulation (n = 19). Our findings provide insights into how the state of the corticospinal system can be leveraged to drive early motor skill learning, further emphasizing individual differences in the response to noninvasive neuromodulation. We interpret these findings in the context of biological mechanisms underlying the observed effects and implications for emerging therapeutic applications.

Older individuals do not show task specific variations in EEG band power and finger force coordination.

Controlling and coordinating finger force is crucial for performing everyday tasks and maintaining functional independence. Aging naturally weakens neural, muscular, and musculoskeletal systems, leading to compromised hand motor function. This decline reduces cortical activity, finger force control and coordination in older adults. To investigate independently the EEG band power and finger force coordination in older individuals and compare the results with young healthy adults. Twenty healthy young adults aged 20-30 (26.96±2.68) and fourteen older adults aged 58-72 (62.57±3.58) participated in this study. Participants held the instrumented handle gently for five seconds then lifted and held it for an additional five seconds in the two conditions: fixed (thumb platform secured) and free condition (thumb platform may slide on slider). In the older individuals there was no difference observed in the finger force synergy indices, and EEG beta band power between the two task conditions. However, in the young group synergy indices and EEG beta band power were less in free condition compared to fixed condition. Additionally, in the fixed condition, older adults showed a reduced synergy indices and reduced EEG beta band power than the young adults. Older participants exhibited consistent synergy indices and beta band power across conditions, while young adults adjusted strategies based on tasks. Task-dependent finger force synergy indices were observed in young adults, contrasting with older individuals Additionally, it is suggested that EEG band power and synergy indices may be related, indicating potential associations between EEG activity and finger force coordination.

Effects of Vibration Direction and Pressing Force on Finger Vibrotactile Perception and Force Control.

This paper reports about the effects of vibration direction and finger-pressing force on vibrotactile perception, with the goal of improving the effectiveness of haptic feedback on interactive surfaces. An experiment was conducted to assess the sensitivity to normal or tangential vibration at 250 Hz of a finger exerting constant pressing forces of 0.5 or 4.9 N. Results show that perception thresholds for normal vibration depend on the applied pressing force, significantly decreasing for the stronger force level. Conversely, perception thresholds for tangential vibrations are independent of the applied force, and approximately equal the lowest thresholds measured for normal vibration.

Effects of vibration direction and pressing force on finger vibrotactile perception and force control

Effects of vibration direction and pressing force on finger vibrotactile perception and force control

Correlating Grip Force Signals from Multiple Sensors Highlights Prehensile Control Strategies in a Complex Task-User System.

Wearable sensor systems with transmitting capabilities are currently employed for the biometric screening of exercise activities and other performance data. Such technology is generally wireless and enables the non-invasive monitoring of signals to track and trace user behaviors in real time. Examples include signals relative to hand and finger movements or force control reflected by individual grip force data. As will be shown here, these signals directly translate into task, skill, and hand-specific (dominant versus non-dominant hand) grip force profiles for different measurement loci in the fingers and palm of the hand. The present study draws from thousands of such sensor data recorded from multiple spatial locations. The individual grip force profiles of a highly proficient left-hander (expert), a right-handed dominant-hand-trained user, and a right-handed novice performing an image-guided, robot-assisted precision task with the dominant or the non-dominant hand are analyzed. The step-by-step statistical approach follows Tukey’s “detective work” principle, guided by explicit functional assumptions relating to somatosensory receptive field organization in the human brain. Correlation analyses (Person’s product moment) reveal skill-specific differences in co-variation patterns in the individual grip force profiles. These can be functionally mapped to from-global-to-local coding principles in the brain networks that govern grip force control and its optimization with a specific task expertise. Implications for the real-time monitoring of grip forces and performance training in complex task-user systems are brought forward.

Using visual feedback to enhance intonation control with a variable pitch electrolarynx.

This study evaluated the effectiveness of using visual feedback to facilitate pitch control by a speaker using a pressure sensitive onset controlled electrolarynx (EL). This proof-of-concept study was conducted with one healthy adult. The participant-speaker was provided with computer generated visual feedback over five sessions within a consecutive period of three weeks. Changes in force control accuracy were gathered and analyzed. An improvement in finger (thumb) force control accuracy from the first to the last training session was documented. The results of this study provide data toward the development of a clinical training protocol for the use of a pressure sensitive onset controlled EL by laryngectomized speakers. Further, these results highlight the importance of developing a relevant multimodality training protocol for the improvement of postlaryngectomy EL speech production.

Recovery and Prediction of Dynamic Precision Grip Force Control After Stroke

Background and Purpose- Dexterous object manipulation, requiring generation and control of finger forces, is often impaired after stroke. This study aimed to describe recovery of precision grip force control after stroke and to determine clinical and imaging predictors of 6-month performance. Methods- Eighty first-ever stroke patients with varying degrees of upper limb weakness were evaluated at 3 weeks, 3 months, and 6 months after stroke. Twenty-three healthy individuals of comparable age were also studied. The Strength-Dexterity test was used to quantify index finger and thumb forces during compression of springs of varying length in a precision grip. The coordination between finger forces (CorrForce), along with Dexterity-score and Repeatability-score, was calculated. Anatomical magnetic resonance imaging was used to calculate weighted corticospinal tract lesion load (wCST-LL). Results- CorrForce, Dexterity-score, and Repeatability-score in the affected hand were dramatically lower at each time point compared with the less-affected hand and the control group, even in patients with mild motor impairment according to Fugl-Meyer assessment. Improved performance over time occurred in CorrForce and Dexterity-score but not in Repeatability-score. The Fugl-Meyer assessment hand subscale, sensory function, and wCST-LL best predicted CorrForce and Dexterity-score status at 6 months (R2=0.56 and 0.87, respectively). wCST-LL explained substantial variance in CorrForce (R2=0.34) and Dexterity-score (R2=0.50) at 6 months; two-point discrimination and Fugl-Meyer score accounted for considerable additional variance. Absence of recovery in CorrForce was predicted by wCST-LL >4 cc and in Dexterity-score by wCST-LL >6 cc. Conclusions- Findings highlight persisting deficits in the ability to grasp and control finger forces after stroke. wCST-LL was the strongest predictor of performance at 6 months, but early two-point discrimination and Fugl-Meyer score had substantial additional predictive value. Clinical Trial Registration- URL: https://www.clinicaltrials.gov. Unique identifier: NCT02878304.

Regression-based reconstruction of human grip force trajectories with noninvasive scalp electroencephalography

Objective. Robotic devices show promise in restoring motor abilities to individuals with upper limb paresis or amputations. However, these systems are still limited in obtaining reliable signals from the human body to effectively control them. We propose that these robotic devices can be controlled through scalp electroencephalography (EEG), a neuroimaging technique that can capture motor commands through brain rhythms. In this work, we studied if EEG can be used to predict an individual’s grip forces produced by the hand. Approach. Brain rhythms and grip forces were recorded from able-bodied human subjects while they performed an isometric force production task and a grasp-and-lift task. Grip force trajectories were reconstructed with a linear model that incorporated delta band (0.1–1 Hz) voltage potentials and spectral power in the theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), low gamma (30–50 Hz), mid gamma (70–110 Hz), and high gamma (130–200 Hz) bands. Trajectory reconstruction models were trained and tested through 10-fold cross validation. Main results. Modest accuracies were attained in reconstructing grip forces during isometric force production (median r = 0.42), and the grasp-and-lift task (median r = 0.51). Predicted trajectories were also analyzed further to assess the linear models’ performance based on task requirements. For the isometric force production task, we found that predicted grip trajectories did not yield static grip forces that were distinguishable in magnitude across three task conditions. For the grasp-and-lift task, we estimate there would be an approximate 25% error in distinguishing when a user wants to hold or release an object. Significance. These findings indicate that EEG, a noninvasive neuroimaging modality, has predictive information in neural features associated with finger force control and can potentially contribute to the development of brain machine interfaces (BMI) for performing activities of daily living.

Quantifying Differences among Ten Fingers in Force Control Capabilities by a Modified Meyer Model

Quantifiable differences among fingers in force control capability have both important practical and theoretical values in characterizing force control of accurate finger-tapping tasks. Following the classical Fitts’ law paradigm, we quantified the differences among ten fingers in term of speed–accuracy trade-off (SAT) in performing repetitive discrete force control tasks. Visual cues displaying targeted force magnitudes and tolerances were provided. Users were required to apply the targeted force within the given tolerance quickly and accurately by pressing a force sensor using the specified finger. We found that ten fingers obeyed the Meyer model in the SAT performance and they differed in reaction time, the index of performance (IP), and the goodness of fit for the Meyer model. A modified Meyer model was proposed to quantify the difference between ten fingers in the SAT performance using only one parameter, making the quantification easier than using the original Meyer model. Pairwise comparisons showed that the differences between symmetrical fingers on both hands were insignificant except for the pair of index fingers. These findings provided us with multiple perspectives on the differentiation among ten fingers in the force control capabilities. Our study helps lay the foundation for engineering systems that rely on finger force control ability.

Evaluation of Finger Force Control Ability in terms of Multi-finger Synergy.

In this study, finger force control abilities are quantified by the concept of multi-finger synergy in conjunction with uncontrolled manifold (UCM) analysis. Two indices, named repeatability and flexibility, representing features of multi-finger synergy were proposed to overcome the limitation of previously introduced indices, such as floor effects and distortion problems. The proposed indices were applied to stroke patients and healthy adults through specifically designed experiments. The experimental results showed a clear difference between stroke patients and healthy adults. Also, interestingly, there was a difference in outcome between two stroke patient subgroups: stroke patients in whom the dominant hand was affected and non-dominant hand was affected groups.

Evaluation of Finger Force Control Ability in Terms of Multi-Finger Synergy.

In this study, finger force control abilities are quantified by the concept of multi-finger synergy in conjunction with uncontrolled manifold (UCM) analysis. Two indices, namely, repeatability and flexibility, representing features of multi-finger synergy were proposed to overcome the limitation of previously introduced indices, such as floor effects and distortion problems. The proposed indices were applied to stroke patients and healthy adults through specifically designed experiments. The experimental results showed a clear difference between stroke patients and healthy adults. Also, interestingly, there was a difference in an outcome between two-stroke patient subgroups: stroke patients in whom the dominant hand was affected and non-dominant hand was affected groups.

Individual recovery profiles of manual dexterity, and relation to corticospinal lesion load and excitability after stroke –a longitudinal pilot study

In this longitudinal pilot study, we investigated how manual dexterity recovery was related to corticospinal tract (CST) injury and excitability, in six patients undergoing conventional rehabilitation. Key components of manual dexterity, namely finger force control, finger tapping rate and independence of finger movements, were quantified. Structural MRI was obtained to calculate CST lesion load. CST excitability was assessed by measuring rest motor threshold (RMT) and the amplitude of motor evoked potentials (MEPs) using transcranial magnetic stimulation (TMS). Measurements were obtained at two weeks, three and six months post-stroke. At six months post-stroke, complete recovery of hand gross motor impairment (i.e., maximal Fugl-Meyer score for hand) had occurred in three patients and four patients had recovered ability to accurately control finger force. However, tapping rate and independence of finger movements remained impaired in all six patients at six months. Recovery in hand gross motor impairment and finger force control occurred in patients with smaller CST lesion load and almost complete recovery of CST excitability, although RMT or MEP size remained slightly altered in the stroke-affected hemisphere compared to the unaffected hemisphere. The two patients with poorest recovery showed persistent absence of MEPs and greatest structural injury to CST. The findings support good motor recovery being overall correlated with smaller CST lesion, and with almost complete recovery of CST excitability. However, impairment of manual dexterity persisted despite recovery in gross hand movements and grasping abilities, suggesting involvement of additional brain structures for fine manual tasks.

The role of corticospinal excitability and corticospinal lesion load in recovery of manual dexterity after stroke: A longitudinal pilot study

Impaired manual dexterity is frequently reported after stroke and thought to result from corticospinal tract (CST) damage. However, how CST function contributes to functional recovery remains unclear. In this prospective longitudinal study we investigated recovery of dexterity and CST injury and excitability in six patients undergoing conventional rehabilitation. The Finger Force Manipulandum was used to measure dexterity components like force control, finger tapping and independence of finger movements. Transcranial magnetic stimulation was used to measure CST excitability, and structural MRI to calculate weighted-CST lesion load. Clinical tests showed complete recovery of gross motor hand movements in three patients by six months (maximal Fugl–Meyer Upper Extremity assessment score for hand). At six months, four patients had fully recovered in their ability to accurately control finger force. However, tapping speed and independence of finger movements remained affected in all patients at six months when compared to healthy subjects. Recovery in gross motor hand movements and finger force control occurred in those patients with smallest CST lesion load and recovery of CST excitability on TMS, although motor evoked potentials (MEPs) remained of smaller amplitude compared to those evoked from the contralesional side. The two patients with poorest recovery in manual dexterity showed persistent absence of MEPs and greatest structural injury to CST. Although in a small patient sample, the findings show persistent deficits in manual dexterity after stroke despite good recovery of gross motor hand function and partial recovery of CST excitability, suggesting that CST integrity may be necessary but not sufficient for post-stroke recovery of dexterity.

나이와 체력수준이 정밀한 손가락 힘 조절에 미치는 영향

The purpose of this study was to investigate the effects of age and physical fitness level on precision finger force control of women. The subjects were 20s(n=33), 60s(n=27), and 70s(n=30), and their physical fitness was divided into 3 levels and a precision tracking was performed. The results of this study were as follows: First, precision finger force control was influenced different age : The fraction time on target and distance off target of the elderly in their 60s and 70s were lower than those in their 20s. Second, There was a very high correlation between age and precision tracking accuracy. There was also a correlation between %body fat, cardiorespiratory endurance, muscle strength and precision tracking accuracy. In conclusion, the precision finger force control is decreased by aging, and the lower the body fat percentage, the better cardiorespiratory endurance and muscle strength, the higher the precision finger force control. Therefore, it is suggested that the maintenance of proper physical fitness level of the elderly is an important factor in maintaining precision finger force control related to neural control of brain function.

Effects of practice on visual finger-force control in children at risk of developmental coordination disorder

BackgroundThe production of finger force control is essential for a large number of daily activities. There is evidence that deficits in the mechanisms of accuracy and control of finger force tasks are associated with children's motor difficulties. ObjectiveTo compare the effect of practice of an isometric finger force/torque task between children with significant movement difficulty and those with no difficulty movement. MethodsTwenty-four children aged between 9 and 10 years (12 at risk of developmental coordination disorder and 12 with no movement difficulty – typically developing children) were asked to produce finger force/torque control in a continuous and constant 25% of maximum voluntary torque with visual feedback during 15s. Practice was given during five consecutive days with 15 trials per day. After the practice with visual feedback, children were asked to perform five trials without visual feedback. In these trials, feedback was removed 5s after the start of the trial. ResultsTypically developing children were consistently more accurate in maintaining finger force/torque control than those children at risk of developmental coordination disorder. Children from both groups improved the performance in the task according to practice sessions. Also, children at risk of developmental coordination disorder poorly performed the task without visual feedback as they did when visual feedback was available. ConclusionThe present study give support to the idea that movement difficulty is associated with finger force/torque control and children at risk of developmental coordination disorder can improve finger force/torque control with practice when visual feedback is available.

Synergies in the space of control variables within the equilibrium-point hypothesis

We use an approach rooted in the recent theory of synergies to analyze possible co-variation between two hypothetical control variables involved in finger force production based on the equilibrium-point (EP) hypothesis. These control variables are the referent coordinate (R) and apparent stiffness (C) of the finger. We tested a hypothesis that inter-trial co-variation in the {R; C} space during repeated, accurate force production trials stabilizes the fingertip force. This was expected to correspond to a relatively low amount of inter-trial variability affecting force and a high amount of variability keeping the force unchanged. We used the “inverse piano” apparatus to apply small and smooth positional perturbations to fingers during force production tasks. Across trials, R and C showed strong co-variation with the data points lying close to a hyperbolic curve. Hyperbolic regressions accounted for over 99% of the variance in the {R; C} space. Another analysis was conducted by randomizing the original {R; C} data sets and creating surrogate data sets that were then used to compute predicted force values. The surrogate sets always showed much higher force variance compared to the actual data, thus reinforcing the conclusion that finger force control was organized in the {R; C} space, as predicted by the EP hypothesis, and involved co-variation in that space stabilizing total force.

Real-time embedded frame work for sEMG skeletal muscle force estimation and LQG control algorithms for smart upper extremity prostheses

This paper presents a real-time embedded framework for finger force control of upper extremity prostheses. The proposed system is based on the hypothesis that models describing the finger force and surface Electromyographic (sEMG) signal relationships of healthy subjects can be applied to amputees. An identification/estimation scheme is applied to the collected sEMG and finger force signals in order to infer dynamical models relating the two. A LQG control law is proposed based on this estimation scheme in order to control finger forces of upper extremity prostheses. For the force estimation, filtered sEMG signals from a sensor array and finger force data of a healthy subject are acquired. Real-time estimation and control are implemented using a PIC32MX360F512L microcontroller. In this paper, a novel fusion technique, the Optimized Linear Model Fusion Algorithm (OLMFA) is developed for estimating the skeletal muscle force from the sEMG sensor array in real-time. The sEMG signal is rectified and filtered using a Half-Gaussian filter, and fed to the OLMFA based Multiple Input Single Output (MISO) force model. This MISO system provides the estimated finger force as reference input to the upper extremity prostheses in real-time. A LQG controller is designed to control the finger force of the prostheses utilizing the force estimate from the OLMFA as a reference. Both the OLMFA and the LQG control scheme are prototyped on the embedded framework for testing of the real-time performance. The proposed embedded framework features rate partitioning and UART interface for performance validation and troubleshooting. The OLMFA based force estimation yields a real-time performance of 85.6% mean correlation and 20.4% mean relative error with a standard deviation of ±1.6 and ±1.5 respectively for 18 test subject’s k-fold cross validation data. The LQG control algorithm yields a real-time performance of 91.6% mean correlation and 9.2% mean relative error with a standard deviation of ±1.4 and ±1.3 respectively.

Force Variability as an Objective Measure of Surgical Skill

This study investigated the force variability of subjects with different level of surgical skills for different force levels. Twelve participants were recruited from three different levels of surgical experiences: A group of surgeon (N = 4), medical student (N = 3) and engineering student (N = 5) underwent a simple finger force control task using a custom developed ‘Force Matching’ module. Three different levels of target force were used: 2 N, 4 N, and 6 N. The task was performed simultaneously using right and left hands. The mean error of force was measured to compare the performance between the three group using Kruskal-Wallis test. A statistically significant difference was detected among the three groups at 2 N when using right hand. We also found that the surgeon group made less error compared to the two other groups at force level 4 N and 6 N for both hands. This finding has important implication for developing a parametric assessment model to evaluate basic skill level in surgical procedures. However, for most accurate result, a big sample size of subject is required.