Acquiring variable moment arms for index finger using a robotic testbed

Analysis on synergistic cocontraction of extrinsic finger flexors and extensors during flexion movements: A finite element digital human hand model

Fine hand movements require the synergistic contraction of intrinsic and extrinsic muscles to achieve them. In this paper, a Finite Element Digital Human Hand Model (FE-DHHM) containing solid tendons and ligaments and driven by the Muscle-Tendon Junction (MTJ) displacements of FDS, FDP and ED measured by ultrasound imaging was developed. The synergistic contraction of these muscles during the finger flexion movements was analyzed by simulating five sets of finger flexion movements. The results showed that the FDS and FDP contracted together to provide power during the flexion movements, while the ED acted as an antagonist. The peak stresses of the FDS, FDP and ED were all at the joints. In the flexion without resistance, the FDS provided the main driving force, and the FDS and FDP alternated in a "plateau" of muscle force. In the flexion with resistance, the muscle forces of FDS, FDP, and ED were all positively correlated with fingertip forces. The FDS still provided the main driving force, but the stress maxima occurred in the FDP at the DIP joint.

A four-tendon robotic finger with tendon transmission inspired by the human extensor mechanism

This paper presents a tendon-driven robotic finger with its inspiration derived from the human extensor mechanism. The analytical model presented relates the contractions of the intrinsic muscles of the human hand to abduction-adduction and coordinated motion of proximal and distal interphalangeal joints. The design presented is simplified from the complex webs of fibers appearing in prior works, but preserves the dual role the interossei have of abducting/adducting the finger and flexing it at the metacarpal-phalangeal joint with the finger outstretched. The anatomical feature in our design is that the proximal interphalangeal joint passes through a set of lateral bands as the finger flexes. We discovered that by including a mechanical stop that causes the lateral bands to 'fold' at large enough flexion aids coordinated movements of the two interphalangeal joints as the finger flexes. Because it involves engineering running and sliding fits, this finger admits a concise kinematic model, which accurately predicts the tendon excursions from a known pose. In this work, however, we evaluate what happens when the model is used to search for a sequence of tendon excursions corresponding to a desired movement. We perform several such sequences of tendon excursions experimentally and present the poses that result using motion capture. We also demonstrate executing several types of grasps on an underactuated robotic hand that incorporates this finger design.

Simultaneous Kinematic and Contact Force Modeling of a Human Finger Tendon System Using Bond Graphs and Robotic Validation

This paper aims to use bond graph modeling to create the most comprehensive finger tendon model and simulation to date. Current models are limited to either free motion without external contact or fixed finger force transmission between tendons and fingertip. The forward dynamics model, presented in this work, simultaneously simulates the kinematics of tendon-finger motion and contact forces of a central finger given finger tendon inputs. The model equations derived from bond graphs are accompanied by nonlinear relationships modeling the anatomical complexities of moment arms, tendon slacking, and joint range of motion (ROM). The structure of the model is validated using a robotic testbed, Utah's Anatomically correct Robotic Testbed (UART) finger. Experimental motion of the UART finger during free motion (no external contact) and surface contact are simulated using the bond graph model. The contact forces during the surface contact experiments are also simulated. On average, the model was able to predict the steady-state pose of the finger with joint angle errors less than 6 deg across both free motion and surface contact experiments. The static contact forces were accurately predicted with an average of 11.5% force magnitude error and average direction error of 12 deg.

Modeling musculoskeletal kinematic and dynamic redundancy using null space projection

The coordination of the human musculoskeletal system is deeply influenced by its redundant structure, in both kinematic and dynamic terms. Noticing a lack of a relevant, thorough treatment in the literature, we formally address the issue in order to understand and quantify factors affecting the motor coordination. We employed well-established techniques from linear algebra and projection operators to extend the underlying kinematic and dynamic relations by modeling the redundancy effects in null space. We distinguish three types of operational spaces, namely task, joint and muscle space, which are directly associated with the physiological factors of the system. A method for consistently quantifying the redundancy on multiple levels in the entire space of feasible solutions is also presented. We evaluate the proposed muscle space projection on segmental level reflexes and the computation of the feasible muscle forces for arbitrary movements. The former proves to be a convenient representation for interfacing with segmental level models or implementing controllers for tendon driven robots, while the latter enables the identification of force variability and correlations between muscle groups, attributed to the system’s redundancy. Furthermore, the usefulness of the proposed framework is demonstrated in the context of estimating the bounds of the joint reaction loads, where we show that misinterpretation of the results is possible if the null space forces are ignored. This work presents a theoretical analysis of the redundancy problem, facilitating application in a broad range of fields related to motor coordination, as it provides the groundwork for null space characterization. The proposed framework rigorously accounts for the effects of kinematic and dynamic redundancy, incorporating it directly into the underlying equations using the notion of null space projection, leading to a complete description of the system.

Intersegmental kinetics significantly impact mapping from finger musculotendon forces to fingertip forces

Predicting the fingertip force vector resulting from excitation of a given muscle remains a challenging but essential task in finger biomechanical modeling. While the conversion of musculotendon force to fingertip force can significantly be affected by finger posture, current techniques utilizing geometric moment arms may not capture such complex postural effects. Here, we attempted to elucidate the postural effects on the mapping between musculotendon force and fingertip force through in vitro experiments. Computer-controlled tendon loading was implemented on the 7 index finger musculotendons of 5 fresh-frozen cadaveric hands across different postures. The resulting fingertip forces/moments were used to compute the effective static moment arm (ESMA), relating tendon force to joint torque, at each joint. The ESMAs were subsequently modeled in three different manners: independent of joint angle; dependent only upon the corresponding joint angle; or dependent upon all joint angles. We found that, for the reconstruction of the fingertip force vector, the multi-joint ESMA model yielded the best outcome, both in terms of direction and magnitude of the vector (mean reconstruction error <4° in direction and <2% in the magnitude), which indicates that intersegmental force transmission through a joint is affected by the posture of neighboring joints. Interestingly, the ESMA model that considers geometric changes of individual joints, the standard model used in biomechanical stimulations, often yielded worse reconstruction results than the simple constant-value ESMA model. Our results emphasize the importance of accurate description of the multi-joint dependency of the conversion of tendon force to joint moment for proper prediction of fingertip force direction.

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.

Control strategies for the index finger of a tendon-driven hand

To understand how versatile dexterity is achieved in the human hand and to achieve it in a robotic form, we have constructed an anatomically correct testbed (ACT) hand. This paper focuses on the development of control strategies for the index finger motion and implementation of joint passive behavior in the ACT hand. A direct muscle position control and a force-optimized joint control are implemented for position tracking through muscle force control. The relationships between the muscle and joint motions play a critical role in both of the controllers and we implemented a Gaussian process regression technique to determine these relationships. Our experiments demonstrate that the direct muscle position controller allows for fast position tracking, while the force-optimized joint controller allows for the exploitation of actuation redundancy in the finger critical for this redundant system. We demonstrate that by implementing a passive force–length relationship at each muscle we are able to precisely match joint stiffness of the metacarpophalangeal (MCP) joint of the ACT to that of a human MCP joint. We also show the results from improved position tracking when implemented in the presence of passive muscle control schemes. The control schemes for position tracking and passive behavior are inspired by human neuromuscular control, and form the building blocks for developing future human-like control approaches.

Muscle-tendon units provide limited contributions to the passive stiffness of the index finger metacarpophalangeal joint

The passive stiffness at the MCP joint is a result of the elasticity of muscle-tendon units (MTUs) and capsule ligament complex (CLC), however, the relative contributions of these two components are unknown. We hypothesize that the MTUs provide the majority of the contributions to the joint stiffness by generating resistive forces when the MCP joint is flexed or extended. We used the work done by passive moments as a measure for the determination of the contributions to the joint stiffness. We conducted experiments with ten human subjects and collected joint angle and finger tip force data. The total passive moment and joint angle data were fitted with a double exponential model, and the passive moments due to the MTUs were determined by developing subject-specific models of the passive force-length change relationships. Our results show that for all the subjects, the work done by the passive moments from the MTUs is less than 50% of the total work done, and the CLC provides dominant contributions to the joint stiffness throughout the flexion-extension range of the joint angle. Therefore, the hypothesis that the MTUs provide the majority of the contributions to the MCP joint stiffness is not supported. We also determined that the majority of the MTUs passive moment was generated by the extrinsic MTUs and the contributions of the intrinsic MTUs was negligible.

Moment arms of the human digital flexors

For the extrinsic hand flexors (flexor digitorum profundus, FDP; flexor digitorum superficialis, FDS; flexor pollicis longus, FPL), moment arm corresponds to the tendon's distance from the center of the metacarpalphalangeal (MP), proximal interphalangeal (PIP), or distal interphalangeal (DIP) joint. The clinical value of establishing accurate moment arms has been highlighted for biomechanical modeling, the development of robotic hands, designing rehabilitation protocols, and repairing flexor tendon pulleys (Brand et al., 1975; An et al., 1983; Thompson and Giurintano, 1989; Deshpande et al., 2010; Wu et al., 2010). In this study, we define the moment arms for all of the extrinsic flexor tendons of the hand across all digital joints for all digits in cadaveric hands.