Matching Items (2)
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Description
The human hand comprises complex sensorimotor functions that can be impaired by neurological diseases and traumatic injuries. Effective rehabilitation can bring the impaired hand back to a functional state because of the plasticity of the central nervous system to relearn and remodel the lost synapses in the brain. Current rehabilitation

The human hand comprises complex sensorimotor functions that can be impaired by neurological diseases and traumatic injuries. Effective rehabilitation can bring the impaired hand back to a functional state because of the plasticity of the central nervous system to relearn and remodel the lost synapses in the brain. Current rehabilitation therapies focus on strengthening motor skills, such as grasping, employ multiple objects of varying stiffness and devices that are bulky, costly, and have limited range of stiffness due to the rigid mechanisms employed in their variable stiffness actuators. This research project presents a portable cost-effective soft robotic haptic device with a broad stiffness range that is adjustable and can be utilized in both clinical and home settings. The device eliminates the need for multiple objects by employing a pneumatic soft structure made with highly compliant materials that act as the actuator as well as the structure of the haptic interface. It is made with interchangeable soft elastomeric sleeves that can be customized to include materials of varying stiffness to increase or decrease the stiffness range. The device is fabricated using existing 3D printing technologies, and polymer molding and casting techniques, thus keeping the cost low and throughput high. The haptic interface is linked to either an open-loop system that allows for an increased pressure during usage or closed-loop system that provides pressure regulation in accordance with the stiffness the user specifies. A preliminary evaluation is performed to characterize the effective controllable region of variance in stiffness. Results indicate that the region of controllable stiffness was in the center of the device, where the stiffness appeared to plateau with each increase in pressure. The two control systems are tested to derive relationships between internal pressure, grasping force exertion on the surface, and displacement using multiple probing points on the haptic device. Additional quantitative evaluation is performed with study participants and juxtaposed to a qualitative analysis to ensure adequate perception in compliance variance. Finally, a qualitative evaluation showed that greater than 60% of the trials resulted in the correct perception of stiffness in the haptic device.
ContributorsSebastian, Frederick (Author) / Polygerinos, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Fu, Qiushi (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Daily collaborative tasks like pushing a table or a couch require haptic communication between the people doing the task. To design collaborative motion planning algorithms for such applications, it is important to understand human behavior. Collaborative tasks involve continuous adaptations and intent recognition between the people involved in the task.

Daily collaborative tasks like pushing a table or a couch require haptic communication between the people doing the task. To design collaborative motion planning algorithms for such applications, it is important to understand human behavior. Collaborative tasks involve continuous adaptations and intent recognition between the people involved in the task. This thesis explores the coordination between the human-partners through a virtual setup involving continuous visual feedback. The interaction and coordination are modeled as a two-step process: 1) Collecting data for a collaborative couch-pushing task, where both the people doing the task have complete information about the goal but are unaware of each other's cost functions or intentions and 2) processing the emergent behavior from complete information and fitting a model for this behavior to validate a mathematical model of agent-behavior in multi-agent collaborative tasks. The baseline model is updated using different approaches to resemble the trajectories generated by these models to human trajectories. All these models are compared to each other. The action profiles of both the agents and the position and velocity of the manipulated object during a goal-oriented task is recorded and used as expert-demonstrations to fit models resembling human behaviors. Analysis through hypothesis teasing is also performed to identify the difference in behaviors when there are complete information and information asymmetry among agents regarding the goal position.
ContributorsShintre, Pallavi Shrinivas (Author) / Zhang, Wenlong (Thesis advisor) / Si, Jennie (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2020