Multifinger manipulation requires interactions among numerous muscles to produce the necessary fingertip motions and forces. The muscles of the fingers (including the thumb) are considered redundant. Redundancy, which has long been called the central problem of motor control, also suggests robustness to loss of some muscles. Our work to date compels and enables us to systematically establish how the interactions among muscles within a finger, and the fingers within a grasp, fail to be robust to muscle dysfunction. Each Hypothesis and Aim addresses the production of a fundamental, yet currently not understood, component of multifinger grasp: slow finger motions and grasp equilibrium. To test each Hypothesis we will apply and extend our integrative approach combining behavioral studies, cadaveric experiments, mathematical analysis, and biomechanical modeling in two complementary Aims.
Aim 1 : Characterize the necessary mechanical interactions among finger muscles for motion;and quantify their robustness to loss of specific muscles. We will focus on how tendon tensions affect the feasibility of slow motions of the thumb, index and middle fingers for grasp acquisition. Hypothesis I: Lack of redundancy for grasp acquisition and release: Slow finger motions are not robust to loss of some muscles.
Aim 2 : Characterize the necessary interactions among tendon tensions for multifinger grasp equilibrium;and quantify their robustness to loss of specific muscles. We will focus on how tensions from individual muscles affect the equilibrium of two- and three-finger precision grasps (using the tips of thumb:index;and thumb:index:middle). We will also explore how simulated signal-dependent noise affects grasp equilibrium. Primary Hypothesis IIa: Lack of redundancy for static grasp forces: Grasp equilibrium is not robust to loss of some muscles. Secondary Hypothesis Hypothesis IIb: Lack of redundancy for static grasp forces: Muscle Signal- Dependent Noise further compromises grasp equilibrium. The understanding gained about multifinger grasp in this project will provide the much-needed neuro- mechanical foundation to understand the vulnerability (and rehabilitation) of dexterous manipulation in specific orthopedic/neurologic diseases, development and aging.
Using our fingers to grasp and hold objects is necessary for us to perform most of our activities of daily living. However, because the anatomy of the hand is so complex, there is little known about how each muscle of the fingers contributes to grasping function. Our proposed project will combine anatomical studies, engineering science and clinical know-how to establish which muscles are most important, and therefore should be most protected, to improve the hand function of people suffering from the many disabilities of the hand.
|Peppoloni, Lorenzo; Lawrence, Emily L; Ruffaldi, Emanuele et al. (2017) Characterization of the disruption of neural control strategies for dynamic fingertip forces from attractor reconstruction. PLoS One 12:e0172025|
|Jalaleddini, Kian; Minos Niu, Chuanxin; Chakravarthi Raja, Suraj et al. (2017) Neuromorphic meets neuromechanics, part II: the role of fusimotor drive. J Neural Eng 14:025002|
|Hagen, Daniel A; Valero-Cuevas, Francisco J (2017) Similar movements are associated with drastically different muscle contraction velocities. J Biomech 59:90-100|
|Valero-Cuevas, Francisco J; Santello, Marco (2017) On neuromechanical approaches for the study of biological and robotic grasp and manipulation. J Neuroeng Rehabil 14:101|
|Lawrence, Emily L; Peppoloni, Lorenzo; Valero-Cuevas, Francisco J (2017) Sex differences in leg dexterity are not present in elite athletes. J Biomech 63:1-7|
|Niu, Chuanxin M; Jalaleddini, Kian; Sohn, Won Joon et al. (2017) Neuromorphic meets neuromechanics, part I: the methodology and implementation. J Neural Eng 14:025001|
|Brock, Oliver; Valero-Cuevas, Francisco (2016) Transferring synergies from neuroscience to robotics: Comment on ""Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands"" by M. Santello et al. Phys Life Rev 17:27-32|
|Inouye, Joshua M; Valero-Cuevas, Francisco J (2016) Muscle Synergies Heavily Influence the Neural Control of Arm Endpoint Stiffness and Energy Consumption. PLoS Comput Biol 12:e1004737|
|Valero-Cuevas, Francisco J; Klamroth-Marganska, Verena; Winstein, Carolee J et al. (2016) Robot-assisted and conventional therapies produce distinct rehabilitative trends in stroke survivors. J Neuroeng Rehabil 13:92|
|Valero-Cuevas, F J; Cohn, B A; Yngvason, H F et al. (2015) Exploring the high-dimensional structure of muscle redundancy via subject-specific and generic musculoskeletal models. J Biomech 48:2887-96|
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