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- Creators: Huang, Huei-Ping
Description
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.
ContributorsYi, Chong-hwan (Author) / Jindrich, Devin L. (Thesis advisor) / Artemiadis, Panagiotis K. (Thesis advisor) / Phelan, Patrick (Committee member) / Santos, Veronica J. (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2014
Description
The ultimate goal of human movement control research is to understand how natural movements performed in daily reaching activities, are controlled. Natural movements require coordination of multiple degrees of freedom (DOF) of the arm. Patterns of arm joint control were studied during daily functional tasks, which were performed through the rotation of seven DOF in the arm. Analyzed movements which imitated the following 3 activities of daily living: moving an empty soda can from a table and placing it on a further position; placing the empty soda can from initial position at table to a position at shoulder level on a shelf; and placing the empty soda can from initial position at table to a position at eye level on a shelf. Kinematic and kinetic analyses were conducted for these three movements. The studied kinematic characteristics were: hand trajectory in the sagittal plane, displacements of the 7 DOF, and contribution of each DOF to hand velocity. The kinetic analysis involved computation of 3-dimensional vectors of muscle torque (MT), interaction torque (IT), gravity torque (GT), and net torque (NT) at the shoulder, elbow, and wrist. Using the relationship NT = MT + GT + IT, the role of active control and passive factors (gravitation and inter-segmental dynamics) in rotation of each joint by computing MT contribution (MTC) to NT was assessed. MTC was computed using the ratio of the signed MT projection on NT to NT magnitude. Despite a variety of joint movements available across the different tasks, 3 patterns of shoulder and elbow coordination prevailed in each movement: 1) active rotation of the shoulder and predominantly passive rotation of the elbow; 2) active rotation of the elbow and predominantly passive rotation of the shoulder; and 3) passive rotation of both joints. Analysis of wrist control suggested that MT mainly compensates for passive torque and provides adjustment of wrist motion according to requirements of each task. In conclusion, it was observed that the 3 shoulder-elbow coordination patterns (during which at least one joint moved) passively represented joint control primitives, underlying the performance of well-learned arm movements, although these patterns may be less prevalent during non-habitual movements.
ContributorsSansgiri, Dattaraj (Author) / Dounskaia, Natalia (Thesis advisor) / Schaefer, Sydney (Thesis advisor) / Buneo, Christopher (Committee member) / Arizona State University (Publisher)
Created2018