Thumb-tip Force Research Articles

Inverse distance weighting to rapidly generate large simulation datasets

Obtaining large biomechanical datasets for machine learning is an ongoing challenge. Physics-based simulations offer one approach for generating large datasets, but many simulation methods, such as computed muscle control (CMC), are computationally costly. In contrast, interpolation methods, such as inverse distance weighting (IDW), are computationally fast. We examined whether IDW is a low-cost and accurate approach for interpolating muscle activations from CMC.IDW was evaluated using lateral pinch simulations in OpenSim. Simulated pinch data were organized into grids of varying sparsity (high, medium, and low density), where each grid point represented the muscle activations associated with a unique combination of mass and height of a young adult. For each grid, muscle activations were calculated via CMC and IDW for 108 random mass-height pairs that were not coincident with simulation grid vertices. We evaluated the interpolation errors from IDW for each grid, as well as the sensitivity of lateral pinch force to these errors. The root mean square error (RMSE) associated with interpolated muscle activations decreased with increasing grid density and never exceeded 4%. While CMC received a target thumb-tip force of 40 N, errors from the interpolated muscle activations never impacted the simulated force magnitude by more than 0.1 N. Furthermore, the computation time for CMC simulations averaged 4.22 core-minutes, while IDW averaged 0.95 core-seconds per mass-height pair.These results indicate IDW is a practical approach for rapidly estimating muscle activations from sparse CMC datasets. Future works could adapt our IDW approach to evaluate other tasks, biomechanical features, and/or populations.

The Sensitivity of the Endpoint Forces of Thumb Extrinsic and Intrinsic Muscles to Changes in Joint Angles, Muscle Moment Arms and Bone Lengths in the Flexed Thumb.

A previous study showed in situ measurements of thumb-tip forces produced by muscles vary substantially among cadaveric specimens. Potential sources of variability include inter-specimen anatomic differences and postural deviations from the nominal posture in which the specimens were tested. This study aimed to theoretically determine the variation in thumb-tip force caused by inter-specimen differences in thumb anatomy and posture. We developed a two-dimensional mathematical model of force production at the thumb tip based on published estimates of muscle moment arms, bone length, and joint angle measurements from nine cadaveric specimens. The model was placed in a flexed posture. Using the model, we calculated variations in magnitude and direction of each muscle's thumb-tip force induced by a ±1 standard deviation (or equivalent) variation in each bone length, the moment arm of the muscle (i.e., anatomic factors), and each joint angle (i.e., postural factor). For most muscles, inter-specimen differences in the metacarpophalangeal (MP) joint angle produced at least a 75% larger variation in thumb-tip force magnitude than that produced by other factors. For all muscles, differences in the interphalangeal joint angle among specimens produced the largest variation in thumb-tip force direction. For some muscles, inter-specimen differences in bone lengths, moment arms, and MP joint angles also produced large variations in thumb-tip force direction. This study suggests deviation from the nominal flexed thumb posture and large measurement variability in muscle moment arms are primary and secondary sources, respectively, of variability in thumb-tip forces produced by the majority of thumb muscles. Further, this study suggests a more careful approach to standardizing the thumb posture would likely improve current measurements of thumb-tip forces.Clinical Relevance- This work describes the influence of anatomic and postural factors on thumb-tip forces that thumb muscles produce. The results of this work have implications for musculoskeletal modeling and surgical reconstruction of grasp.

Towards an Extensive Thumb Assist: A Comparison between Whole-Finger and Modular Types of Soft Pneumatic Actuators

Soft pneumatic actuators used in robotic rehabilitation gloves are classified into two types: whole-finger actuators with air chambers that cover the entire finger and modular actuators with chambers only above the finger joints. Most existing prototypes provide enough finger flexion support, but insufficient independent thumb abduction or opposition support. Even the latest modular soft actuator realized thumb abduction with a sacrifice of range of motion (RoM). Moreover, the advantages and disadvantages of using the two types of soft actuators for thumb assistance have not been made clear. Without an efficient thumb assist, patients’ options for hand function rehabilitation are very limited. Therefore, the objective of this study was to design a modular actuator (M-ACT) that could support multiple degrees of freedom, compare it with a whole-finger type of thumb actuator with three inner chambers (3C-ACT) in terms of the RoM, force output of thumb flexion, and abduction, and use an enhanced Kapandji test to measure both the kinematic aspect of the thumb (Kapandji score) and thumb-tip pinch force. Our results indicated superior single-DoF support capability of the M-ACT and superior multi-DoF support capability of the 3C-ACT. The use of the 3C-ACT as the thumb actuator and the M-ACT as the four-finger actuator may be the optimal solution for the soft robotic glove. This study will aid in the progression of soft robotic gloves for hand rehabilitation towards real rehabilitation practice.

Thumb-tip Force Prediction Based on Hill’s Muscle Model using Electromyogram and Ultrasound Signal

The use of prostheses is necessary to restore lost limbs to a level of functionality to enable activity of daily living. Many prostheses are now using myoelectric based control techniques to operate. However, to develop a model based controller for the system remains a challenge as accurate model is necessary. This study investigates the use of electromyogram and ultrasound signal to predict thumb tip force based on Hill’s Muscle model. The results obtained has shown a significant improvement in the prediction of thumb tip force as much as 31.45% of average RMSE over the benchmark model that leverages on biomechanics model and active marker to characterize the muscle.

Human Hand Motion Analysis during Different Eating Activities.

The focus of this research is to analyse both human hand motion and force, during eating, with respect to differing food characteristics and cutlery (including a fork and a spoon). A glove consisting of bend and force sensors has been used to capture the motion and contact force exerted by fingers during different eating activities. The Pearson correlation coefficient has been used to show that a significant linear relationship exists between the bending motion of the fingers and the forces exerted during eating. Analysis of variance (ANOVA) and independent samples t-tests are performed to establish whether the motion and force exerted by the fingers while eating is influenced by the different food characteristics and cutlery. The middle finger motion showed the least positive correlation with index fingertip and thumb-tip force, irrespective of the food characteristics and cutlery used. The ANOVA and t-test results revealed that bending motion of the index finger and thumb varies with respect to differing food characteristics and the type of cutlery used (fork/spoon), whereas the bending motion of the middle finger remains unaffected. Additionally, the contact forces exerted by the thumb tip and index fingertip remain unaffected with respect to differing food types and cutlery used.

Variable Thumb Moment Arm Modeling and Thumb-Tip Force Production of a Human-Like Robotic Hand.

The anatomically correct testbed (ACT) hand mechanically simulates the musculoskeletal structure of the fingers and thumb of the human hand. In this work, we analyze the muscle moment arms (MAs) and thumb-tip force vectors in the ACT thumb in order to compare the ACT thumb's mechanical structure to the human thumb. Motion data are used to determine joint angle-dependent MA models, and thumb-tip three-dimensional (3D) force vectors are experimentally analyzed when forces are applied to individual muscles. Results are presented for both a nominal ACT thumb model designed to match human MAs and an adjusted model that more closely replicates human-like thumb-tip forces. The results confirm that the ACT thumb is capable of faithfully representing human musculoskeletal structure and muscle functionality. Using the ACT hand as a physical simulation platform allows us to gain a better understanding of the underlying biomechanical and neuromuscular properties of the human hand to ultimately inform the design and control of robotic and prosthetic hands.

Effect of prior experience and task stability on the intrinsic muscle activity of the thumb

Manual techniques involving the use of the thumb are commonly employed by physical therapists for treating patients with vertebral disorders. The demands on the intrinsic muscles of the thumb in these manual tasks are very different from those of the pinch tasks. The aim of this study was to investigate the influence of clinical experience and different mobilization techniques on the electromyographic activity (EMG) of thumb intrinsic muscles. Fifteen participants without exposure to manual techniques (the Novice Group) and fifteen physical therapists with at least 3 years of orthopaedic experience (the Experienced Group) participated. Each participant exerted thumb tip forces with 3 different posterioanterior (PA) glide techniques including unsupported, with digital support and with thumb interphalangeal joint supported by the index finger. The exerted force was increased from 25% to 100% maximum force at 25% increments on a 6 component load cell. The thumb tip force and EMG activity of four intrinsic muscles (flexor pollicis brevis, adductor pollicis, abductor pollicis brevis, first dorsal interosseus) were recorded with surface electrodes. Both experience and technique influenced intrinsic muscle activity of the thumb. While participants of both groups generated the same magnitude of force, experienced participants generated less intrinsic muscle activity while performing PA glide through practice. However, novice participants increased activity of the intrinsic muscles in accordance with the stability status of the technique. PA glide with thumb interphalangeal joint supported by the index finger was a more stable technique as evidenced by smallest relative errors of thumb tip force.

Neuro-Based Thumb-Tip Force and Joint Angle Modelling for Development of Prosthetic Thumb Control

Human fingers have a specific role that contributes to different hand functions. Among these fingers, the thumb plays the most special function as an anchor to many hand activities. As a result, the loss of the thumb due to traumatic accidents can be catastrophic as proper hand function will be severely limited. In order to solve this problem, a prosthetic thumb is developed to be worn in complementing the function of the rest of the fingers. The movement of the prosthetic device can be naturally controlled by using electromyogram (EMG) signals. In this work, the EMG signals from the human muscles were measured in different thumb configurations and thumb-tip forces in flexion movement. The muscles involved are the Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and First Dorsal Interosseous (FDI). The classification of the EMG signals based on different force and thumb configurations is performed using an Artificial Neural Network (ANN). From a series of experiments, the results show that the neural network efficiently classified the signals and a unique set of EMG signals was generated for each thumb movement and force. Therefore, EMG signals were used to control the prosthetic movement with aid from the developed neural network.

Real-time thumb-tip force predictions from noninvasive biosignals and biomechanical models

The opposable thumb has allowed humans to develop accurate fine motor skills and has provided the same level of functionality in artificial prostheses or similar robotic hands. This study describes a real-time isometric thumb-tip force prediction that uses a biomechanical muscle model and surface electromyography signals under four different angle configurations. Of the nine muscles that contribute to the thumb-tip force, the activities of five muscles were measured, and the activities of four muscles were inferred based on measured muscle data. The force exerted by each individual muscle was computed using a Hill-based muscle model. The thumb-tip force in the palmar direction was then estimated based on the contributing ratio of each muscle. The results indicated a high correlation between the thumb-tip force predictions from the model and the measured data. The possible applications of this research include the control of finger-tip forces from noninvasive neurosignals in hand prostheses.

Surface Electromyography (sEMG)-based Thumb-tip Angle and Force Estimation Using Artificial Neural Network for Prosthetic Thumb

Normally, humans were born with five fingers connected to each of the hands. These fingers have their own specific role that contributes to different hand functions. Among the five fingers, the thumb plays the most special function as an anchor to many of hand activities such as turning a key, gripping a ball and holding a spoon for eating. As a result, the lost of thumb due to traumatic accidents could be catastrophic as proper hand function will be severely limited. In order to solve this problem, a prosthetic thumb is developed to be worn in complementing the function of the rest of the fingers. In this work the relationship between the electromyogram (EMG) signals and thumb tip forces are investigated in order to develop a more natural controlled prosthetic thumb. The signals are measured from the thumb intrinsic muscles namely the Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and First Dorsal Interosseous (FDI). Meanwhile the thumb tip force is recorded by using the force sensor (FSR). The classification of the EMG signals based on different force and thumb configuration is performed by using Artificial Neural Network (ANN). A series of experiments have been conducted and preliminary results show the efficacy of ANN to classify the EMG signals.

Diminished capacity to modulate motor activation patterns according to task contributes to thumb deficits following stroke

The objective of this study was to explore motor impairment of the thumb following stroke. More specifically, we quantitatively examined kinetic deficits of the thumb. We anticipated that force deficits would be nonuniformly distributed across the kinetic workspace, due in part to varying levels of difficulty in altering the motor activation pattern to meet the task. Eighteen stroke survivors with chronic hemiparesis participated in the trials, along with nine age-matched controls. Of the stroke-survivor group, nine subjects had moderate hand impairment, and the other nine subjects had severe hand impairment. Subjects were instructed to generate maximal isometric thumb-tip force, as measured with a load cell, in each of six orthogonal directions with respect to the thumb tip. Activity of three representative thumb muscles was monitored through intramuscular and surface electrodes. Univariate split-plot analysis of variance revealed that clinical impairment level had a significant effect on measured force (P < 0.001), with the severely impaired group producing only 13% of the control forces, and the moderately impaired group generating 32% of control forces, on average. Weakness in the moderately impaired group exhibited a dependence on force direction (P = 0.015), with the least-relative weakness in the medial direction. Electromyographic recordings revealed that stroke survivors exhibited limited modulation of thumb-muscle activity with intended force direction. The difference in activation presented by the control group for a given muscle was equal to 40% of its full activation range across force directions, whereas this difference was only 26% for the moderately impaired group and 15% for the severely impaired group. This diminished ability to modify voluntary activation patterns, which we observed previously in index-finger muscles as well, appears to be a primary factor in hand impairment following stroke.

The sensitivity of endpoint forces produced by the extrinsic muscles of the thumb to posture

This study utilizes a biomechanical model of the thumb to estimate the force produced at the thumb-tip by each of the four extrinsic muscles. We used the principle of virtual work to relate joint torques produced by a given muscle force to the resulting endpoint force and compared the results to two separate cadaveric studies. When we calculated thumb-tip forces using the muscle forces and thumb postures described in the experimental studies, we observed large errors. When relatively small deviations from experimentally reported thumb joint angles were allowed, errors in force direction decreased substantially. For example, when thumb posture was constrained to fall within ±15° of reported joint angles, simulated force directions fell within experimental variability in the proximal–palmar plane for all four muscles. Increasing the solution space from ±1° to an unbounded space produced a sigmoidal decrease in error in force direction. Changes in thumb posture remained consistent with a lateral pinch posture, and were generally consistent with each muscle’s function. Altering thumb posture alters both the components of the Jacobian and muscle moment arms in a nonlinear fashion, yielding a nonlinear change in thumb-tip force relative to muscle force. These results explain experimental data that suggest endpoint force is a nonlinear function of muscle force for the thumb, support the continued use of methods that implement linear transformations between muscle force and thumb-tip force for a specific posture, and suggest the feasibility of accurate prediction of lateral pinch force in situations where joint angles can be measured accurately.

Effect of general flexibility on thumb-tip force generation - implication for mobilization and manipulation

Pain involving basal joints of the thumb is one of the major occupation-related disorders for orthopedic physiotherapists and manual therapists. The thumb-tip force generation while performing manual techniques may be influenced not only by the specific manual techniques employed but also by general flexibility of the therapist. The purpose of this study was to investigate the influence of general flexibility and different techniques on thumb-tip force generation. Twenty-three subjects with no exposure to manual techniques and 15 physical therapy clinicians with at least 3 years of orthopedic experience participated. The general flexibility of each subject was assessed by Beighton score (BS). Each subject was requested to exert a maximal force on a six-component load cell with the thumb unsupported (T1), with the rest of digits supported (T2), and with interphalangeal joint of the thumb supported by the index (T3).The thumb-tip force was normalized by body weight. The thumb-tip force generation is influenced not only by the differences in technique employed by the therapists, but also by the general flexibility of the therapists. Physiotherapists with excessive thumb flexibility are advised to perform PA glide with IP joint supported to protect the thumb joints from injury.

Use of intrinsic thumb muscles may help to improve lateral pinch function restored by tendon transfer

Background For surgical reconstruction of lateral pinch following tetraplegia, the function of the paralyzed flexor pollicis longus is commonly restored. The purpose of this study was to investigate if one of the intrinsic muscles could generate a more suitably directed thumb-tip force during lateral pinch than that of flexor pollicis longus. Methods Endpoint force resulting from 10 N applied to each thumb muscle was measured in eleven upper extremity cadaveric specimens. We utilized the Kruskal–Wallis test ( α = 0.05) to determine whether thumb-tip forces of intrinsic muscles were less directed toward the base of the thumb, i.e., proximally directed, than the thumb-tip force produced by flexor pollicis longus. Additionally, a biomechanical model was used to assess the effect of an increase in tendon force on intrinsic muscle endpoint forces. Findings All of the intrinsic muscles produced thumb-tip force vectors, ranging from 127° to 156°, that were significantly ( P < 0.009) less proximally directed than that of flexor pollicis longus (66° (46°)). A biomechanical model predicted that intrinsic muscle thumb-tip forces would vary non-linearly with tendon force. A 2-fold increase in tendon force produced, on average, a 2.3-fold increase in force magnitude and an 8° shift in force direction across all intrinsic muscles. Interpretation This study suggests the possibility of using an intrinsic muscle, e.g., the flexor pollicis brevis (ulnar head), instead of flexor pollicis longus, to produce a more advantageously directed thumb-tip force during lateral pinch in the surgically-reconstructed tetraplegic thumb and thus potentially enhance function.

The effect of percutaneous pin fixation of the interphalangeal joint on the thumb-tip force produced by the flexor pollicis longus: A cadaver study

Interphalangeal joint stabilization often is performed concomitantly with tendon transfers that restore key pinch (lateral pinch) to the paralyzed thumb. The goal of this study was to measure the effect of interphalangeal joint stabilization via percutaneous pin fixation on the thumb-tip force produced by the flexor pollicis longus (FPL). We applied 10 N of force to the tendon of the FPL in 7 cadaveric specimens and measured the resulting thumb-tip force in the intact thumb and after stabilization of the interphalangeal joint. The nominal thumb-tip force was approximately 6 times less than the applied force and was directed primarily in the thumb's plane of flexion-extension at an oblique angle of 44 degrees relative to the palmar direction (the direction that is perpendicular to the thumb tip in the plane). Joint stabilization increased significantly the nominal force and oriented the force more toward the palmar direction (ie, decreased the obliqueness of the force). After paralysis and a tendon transfer to the paralyzed FPL the FPL is often the only muscle actuating the thumb. We conclude that the oblique nominal force direction is prone to cause the thumb to slip during pinch. Joint stabilization, however, has the capacity to reduce the tendency for slippage because it rotates the force toward the palmar direction.

Quantifying deficits in the 3D force capabilities of a digit caused by selective paralysis: application to the thumb with simulated low ulnar nerve palsy

We present the development of a vision-feedback method to characterize how selective paralysis distorts the three-dimensional (3D) volume representing digit-tip force production capability and its application to healthy individuals producing thumb-tip force with and without simulated low ulnar nerve palsy (LUNP). Subjects produced maximal static voluntary force spanning the transverse, sagittal and frontal planes of the thumb (16, 15 and 10 subjects, respectively). Subjects produced thumb-tip force tasks in guided and self-selected directions. The envelope (convex hull) of extreme forces in each plane approximated that cross-section of the 3D volume of force capability. Some subjects repeated the tasks with a temporary ulnar nerve block applied at the wrist to simulate complete acute LUNP. Three geometric properties of the force convex hull characterized each cross-section's shape: the ratios of its principal moments of inertia (RPMIs), the orientation of its principal axis (OPA), and its centroid location. Our results show that force production in the thumb's sagittal plane may be a reproducible and objective test to grade motor impairment in LUNP: paired t-tests of the larger RPMI in this plane best distinguished the nerve-blocked cases from the control cases in the guided task ( p=0.012), and Discriminant Analysis of the centroid location for the self-selected task in this plane correctly classified 94.7% of subjects into the control and ulnar nerve-blocked groups. We show that our method measures and detects changes in a digit's force production capabilities. Towards a clinical test of motor impairment in LUNP, this biomechanical study dictates which 3D thumb-tip forces to measure (those in the sagittal plane) and how to measure them (using the self-selected task).

The fundamental thumb-tip force vectors produced by the muscles of the thumb

A rigorous description of the magnitude and direction of the 3D force vector each thumb muscle produces at the thumb-tip is necessary to understand the biomechanical consequences to pinch of a variety of paralyses and surgical procedures (such as tendon transfers). In this study, we characterized the 3D force vector each muscle produces at the thumb-tip, and investigated if these thumb-tip force vectors scaled linearly with tendon tension. In 13 cadaver specimens, we measured the output 3D thumb-tip force vector produced by each tendon acting on the thumb, plus two common tendon transfers, as a function of input tendon tension. After fixing the hand to a rigid frame, we mounted the thumb by configuring it in standardized key or opposition pinch posture and coupling the thumb-tip to a rigidly held 6 degree-of-freedom force/torque sensor. Linear actuators applied tension to the distal tendons of the four extrinsic thumb muscles, and to six Nylon cords reproducing the lines of action of (i) the four intrinsic thumb muscles and (ii) two alternative tendon transfers commonly used to restore thumb opposition following low median nerve palsy. Each computer-controlled linear actuator ramped tendon tension from zero to 1/3 of predicted maximal muscle force expected at each tendon, and back to zero, while we measured the 3D force vector at the thumb-tip. In test/re-test trials, we saw thumb-tip force vectors were quite sensitive to mounting procedure, but also sensitive to variations in the seating of joint surfaces. We found that: (i) some thumb-tip force vectors act in unexpected directions (e.g., the opponens force vector is parallel to the distal phalanx), (ii) the two tendon transfers produced patently different force vectors, and (iii) for most muscles, thumb-tip force vectors do not scale linearly with tendon tension––likely due to load-dependent viscoelastic tendon paths, joint seating and/or bone motion. Our 3D force vector data provide the first quantitative reference descriptions of the thumb-tip force vectors produced by all thumb muscles and two tendon transfers. We conclude that it may not be realistic to assume in biomechanical models that thumb-tip force vectors scale linearly with tendon tensions, and that our data suggest the thumb may act as a “floating digit” affected by load-dependent trapezium motion.

Towards a realistic biomechanical model of the thumb: the choice of kinematic description may be more critical than the solution method or the variability/uncertainty of musculoskeletal parameters

A biomechanical model of the thumb can help researchers and clinicians understand the clinical problem of how anatomical variability contributes to the variability of outcomes of surgeries to restore thumb function. We lack a realistic biomechanical model of the thumb because of the variability/uncertainty of musculoskeletal parameters, the multiple proposed kinematic descriptions and methods to solve the muscle redundancy problem, and the paucity of data to validate the model with in vivo coordination patterns and force output. We performed a multi-stage validation of a biomechanical computer model against our measurements of maximal static thumbtip force and fine-wire electromyograms (EMG) from 8 thumb muscles in each of five orthogonal directions in key and opposition pinch postures. A low-friction point-contact at the thumbtip ensured that subjects did not produce thumbtip torques during force production. The 3-D, 8-muscle biomechanical thumb model uses a 5-axis kinematic description with orthogonal and intersecting axes of rotation at the carpometacarpal and metacarpophalangeal joints. We represented the 50 musculoskeletal parameters of the model as stochastic variables based on experimental data, and ran Monte Carlo simulations in the “inverse” and “forward” directions for 5000 random instantiations of the model. Two inverse simulations (predicting the distribution of maximal static thumbtip forces and the muscle activations that maximized force) showed that: the model reproduces at most 50% of the 80 EMG distributions recorded (eight muscle excitations in 5 force directions in two postures); and well-directed thumbtip forces of adequate magnitude are predicted only if accompanied by unrealistically large thumbtip torques (0.64±0.28 N m). The forward simulation (which fed the experimental distributions of EMG through random instantiations of the model) resulted in misdirected thumbtip force vectors (within 74.3±24.5° from the desired direction) accompanied by doubly large thumbtip torques (1.32±0.95 N m). Taken together, our results suggest that the variability and uncertainty of musculoskeletal parameters and the choice of solution method are not the likely reason for the unrealistic predictions obtained. Rather, the kinematic description of the thumb we used is not representative of the transformation of net joint torques into thumbtip forces/torques in the human thumb. Future efforts should focus on validating alternative kinematic descriptions of the thumb.

Activation patterns of the thumb muscles during stable and unstable pinch tasks.

The ability to direct forces between the thumb and fingers is important to secure objects in the hand. We compared the coordination of thumb musculature in key and opposition pinch postures between stable and unstable tasks. The unstable task (producing thumb-tip force wearing a beaded thimble) required well-directed forces; the stable task (producing thumb-tip force against a pinch meter) did not. Fine-wire electromyography of thumb muscles and thumb-tip force magnitudes were recorded. We found no statistical differences in thumb-tip force between postures or stable versus unstable tasks, indicating that the highest magnitudes of force can be accurately directed. Abductor pollicis brevis and extensor pollicis longus were significantly more activated in the unstable tasks, suggesting their importance in directing thumb-tip force. Understanding how pinch forces are directed might influence the choice of muscle-tendon transfers performed to restore function to the severely paralyzed thumb. We introduce a device to quantify the ability to control pinch force magnitude and direction simultaneously.