A musculoskeletal model of the human hand to improve human-device interaction

abstract: Multi-touch tablets and smart phones are now widely used in both workplace and consumer settings. Interacting with these devices requires hand and arm movements that are potentially complex and poorly understood. Experimental studies have revealed differences in performance that could potentially be associated with injury risk. However, underlying causes for performance differences are often difficult to identify. For example, many patterns of muscle activity can potentially result in similar behavioral output. Muscle activity is one factor contributing to forces in tissues that could contribute to injury. However, experimental measurements of muscle activity and force for humans are extremely challenging. Models of the musculoskeletal system can be used to make specific estimates of neuromuscular coordination and musculoskeletal forces. However, existing models cannot easily be used to describe complex, multi-finger gestures such as those used for multi-touch human computer interaction (HCI) tasks. We therefore seek to develop a dynamic musculoskeletal simulation capable of estimating internal musculoskeletal loading during multi-touch tasks involving multi digits of the hand, and use the simulation to better understand complex multi-touch and gestural movements, and potentially guide the design of technologies the reduce injury risk. To accomplish these, we focused on three specific tasks. First, we aimed at determining the optimal index finger muscle attachment points within the context of the established, validated OpenSim arm model using measured moment arm data taken from the literature. Second, we aimed at deriving moment arm values from experimentally-measured muscle attachments and using these values to determine muscle-tendon paths for both extrinsic and intrinsic muscles of middle, ring and little fingers. Finally, we aimed at exploring differences in hand muscle activation patterns during zooming and rotating tasks on the tablet computer in twelve subjects. Towards this end, our musculoskeletal hand model will help better address the neuromuscular coordination, safe gesture performance and internal loadings for multi-touch applications.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201

Doctor of Philosophy

dissertationModeling the human hand's tendon system can bring better understanding to roboticists trying to create tendon based robotic hands and clinicians trying to identify new surgical solutions to hand tendon injuries. Accurate modeling of the hand's tendon system is complex due to the intricate nature of how tendons route and attach to each other and the skeleton system. These tendon complexities have restricted previous tendon models to single finger models with limited anatomical accuracy and no ability to depict fingertip contact force with external surfaces. This dissertation outlines the use of bond graph modeling to create and improve upon previous tendon models of the single finger. This bond graph tendon model of the single finger is the first model to incorporate many anatomical features, including tendon interconnections and anatomical stiffness, of the tendon system. A graphical user interface is presented to visually explore the relationship between tendon input and finger posture. The bond graph tendon model is validated using cadaver and in vivo experiments, along with the Anatomically Correct Testbed (ACT) Hand, which is a biologically inspired robotic hand that accurately mimics the bone structure, joints, and tendons of the human hand. Comparisons of the bond graph tendon model to in vivo data on finger joint coupling and fingertip pinch force, and cadaver data on the tendon system showed strong correlation in trends and magnitudes. A motion experiment, comparing the joint angle results of tendon excursions of the bond graph tendon model and the ACT Hand, and a force experiment, comparing the fingertip force generation of the two systems, were devised to validate the bond graph tendon model. The results of the motion experiments showed close agreement between the two systems (< 8 joint angle error), while the results of the force experiments showed a larger range correlation between the two systems (8-42% difference). The result of the validation experiments showed that the bond graph tendon model is able to accurately represent the tendon system of the finger. The model is also the first tendon model to allow for exploration of the effects of fingertip contact on the tendon system

Bio-inspired robotic joint and manipulator : from biomechanical experimentation and modeling to human-like compliant finger design and control

textOne of the greatest challenges in controlling robotic hands is grasping and manipulating objects in unstructured and uncertain environments. Robotic hands are typically too rigid to react against unexpected impacts and disturbances in order to prevent damage. The human hands have great versatility and robustness due, in part, to the passive compliance and damping. Designing mechanical elements that are inspired by the nonlinear joint compliance of human hands is a promising solution to achieve human-like grasping and manipulation. However, the exact role of biomechanical elements in realizing joint stiffness is unknown. We conducted a series of experiments to investigate nonlinear stiffness and damping of the metacarpophalangeal (MCP) joint at the index finger. We designed a custom-made mechanism to integrate electromyography sensors (EMGs) and a motion capture system to collect data from 19 subjects. We investigated the relative contributions of muscle-tendon units and the MCP capsule ligament complex to joint stiffness with subject-specific modeling. The results show that the muscle-tendon units provide limited contribution to the passive joint compliance. This findings indicate that the parallel compliance, in the form of the capsule-ligament complex, is significant in defining the passive properties of the hand. To identify the passive damping, we used the hysteresis loops to investigate the energy dissipation function. We used symbolic regression and principal component analysis to derive and interpret the damping models. The results show that the nonlinear viscous damping depends on the cyclic frequency, and fluid and structural types of damping also exist at the MCP joint. Inspired by the nonlinear stiffness of the MCP joint, we developed a miniaturized mechanism that uses pouring liquid plastic to design energy storing elements. The key innovations in this design are: a) a set of nonlinear elasticity of compliant materials, b) variable pulley configurations to tune the stiffness profile, and c) pretension mechanism to scale the stiffness profile. The design exhibits human-like passive compliance. By taking advantage of miniaturized joint size and additive manufacturing, we incorporated the novel joint design in a novel robotic manipulator with six series elastic actuators (SEA). The robotic manipulator has passive joint compliance with the intrinsic property of human hands. To validate the system, we investigated the Cartesian stiffness of grasping with low-level force control. The results show that that the overall system performs a great force tracking with position feedback. The parallel compliance decreases the motor efforts and can stabilize the system.Mechanical Engineerin