Real-time simulation of hand motion for prosthesis control

Blana, Dimitra; Chadwick, Edward K.; Bogert, Antonie J. van den; Murray, Wendy M.

doi:10.1080/10255842.2016.1255943

Cited by 45 publications

(31 citation statements)

References 26 publications

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“…All models are simplifications or approximations of reality, but some approximations are useful. The complex geometric interactions-sliding and wrapping-between muscles and other mechanical body structures pose a considerable computational challenge for real-time applications (Blana et al, 2017). The engineering trade-off between complexity, performance, and accuracy pushed the development of simplified biomechanical limb models that assumed constant moment arm and posture relationships (Crouch and Huang, 2016) or reduced the span of musculotendon anatomy to ease computational demand (Durandau et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials

Sobinov

Boots

Gritsenko

et al. 2019

Preprint

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Computational models of the musculoskeletal system are scientific tools used to study human movement, quantify the effects of injury and disease, and plan surgical interventions. Additionally, these models could also be used to intuitively link biological control signals and realistic high-dimensional articulated prosthetic limbs. However, implementing fast and accurate musculoskeletal computations that can be used to control a prosthetic limb in real-time is a challenging problem. As muscles typically span multiple joints, the wrapping over complex geometrical constraints changes their moment arms and length as a function of joint angle and, thus, their ability to generate joint torques. As a result of these biomechanical complexities, calculating these muscle state variables in real-time is a difficult simulation problem. Here, we report a method to accurately and efficiently calculate these variables for the forearm muscles that actuate the hand and wrist across multiple postures. The posture dependent muscle geometry, moment arms and lengths of modeled muscles, were captured using autogenerating polynomials that expanded their optimal selection of terms using information measurements. The iterative process approximated 33 musculotendon actuators, each spanning up to 6 DOFs in an 18 DOF model of the human arm and hand, defined over the full physiological range of motion. Using these polynomials, the entire forearm anatomy could be computed in <10 µs, which is far better than what is required for real-time performance, and with low errors in moment arms (below 5%) and lengths (below 0.4%). Moreover, we demonstrate that the number of elements in these autogenerating polynomials does not increase exponentially with the increase in complexity of muscles, increasing linearly instead. The similar structure and function of muscles are represented with specific invariant polynomial terms. Dimensionality reduction using the polynomial terms alone resulted in clusters comprised of muscles with similar functions, suggesting that the polynomials themselves captured biologically relevant features of muscle structure and function. We propose that this novel method of describing musculoskeletal biomechanics might further improve the applications of detailed and scalable models for the description of human movement.

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Section: Discussionmentioning

confidence: 99%

Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials

Sobinov

Boots

Gritsenko

et al. 2019

Preprint

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“…In our paradigm, the prosthesis is the physical device that converted EMG-predicted joint moments into joint angles, thus eliminating the need for numerically integrating dynamic equations of motions. This is different from current solutions operating at the kinematic-level, including (1) model-free decoders, sensitive to unseen motor tasks and time scales [5] and model-based methods [21] that integrate forward dynamic equations of motion, which is a computationally expansive and numerically unstable step [23]. talk between DOF-specific command signals, which has shown to be a challenge for regression-based methods [53].…”

Section: Discussionmentioning

confidence: 90%

Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling

et al. 2018

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“…Passive torques are critical to achieve controlled and stabilized dynamic free movements of the wrist and fingers (Babikian et al, 2016; Blana et al, 2016; Charles and Hogan, 2012; Kamper et al, 2002). Additionally, passive coupling of the fingers and wrist is a fundamental component of hand function in the severely disabled hand, such as following tetraplegia (Johanson and Murray, 2002; Su et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

“…The original model included the kinematics of the shoulder, elbow, and wrist, without additional degrees of freedom distal to the wrist. As described previously (Blana et al, 2016), the kinematics of the original model were augmented to include degrees of freedom for digits 1 (thumb) through 5 (pinky finger) (Figure 1). Mass and inertia properties of the individual hand bone segments were distributed (Le Minor and Rapp, 2001; McFadden and Bracht, 2003) such that the sum of the individual bones are equal to the total mass of the hand segment from Saul et al, 2015 (Table 1).…”

Section: Methodsmentioning

confidence: 99%

Incorporating the length-dependent passive-force generating muscle properties of the extrinsic finger muscles into a wrist and finger biomechanical musculoskeletal model

Binder-Markey

Murray

2017

Journal of Biomechanics

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Dynamic movement trajectories of low mass systems have been shown to be predominantly influenced by passive viscoelastic joint forces and torques compared to momentum and inertia. The hand is comprised of 27 small mass segments. Because of the influence of the extrinsic finger muscles the passive torques about each finger joint becomes a complex function dependent on the posture of multiple joints of the distal upper limb. However, biomechanical models implemented for the dynamic simulation of hand movements generally don’t extend proximally to include the wrist and distal upper limb. Thus, they cannot accurately represent these complex passive torques. The purpose of this short communication is to both describe a method to incorporate the length-dependent passive properties of the extrinsic index finger muscles into a biomechanical model of the upper limb and to demonstrate their influence on combined movement of the wrist and fingers. Leveraging a unique set of experimental data, that describes the net passive torque contributed by the extrinsic finger muscles about the metacarpophalangeal joint of the index finger as a function of both metacarpophalangeal and wrist postures, we simulated the length-dependent passive properties of the extrinsic finger muscles. Dynamic forward simulations demonstrate that a model including these properties passively exhibits coordinated movement between the wrist and finger joints, mimicking tenodesis, a behavior that is absent when the length-dependent properties are removed. This work emphasizes the importance of incorporating the length-dependent properties of the extrinsic finger muscles into biomechanical models to study healthy and impaired hand movements.

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Real-time simulation of hand motion for prosthesis control

Cited by 45 publications

References 26 publications

Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials

Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials

Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling

Incorporating the length-dependent passive-force generating muscle properties of the extrinsic finger muscles into a wrist and finger biomechanical musculoskeletal model

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