<p> A better understanding of human hand functionality can help improve robotic and prosthetic hand capability, as well as having benefits for rehabilitation or device design. While the human hand has been studied extensively in various fields, fewer existing works study the human hand within frameworks which can be easily applied to robotic applications, or attempt to quantify complex human hand functionality in real-world environments or with tasks approaching real-world complexity. This dissertation presents a study of human hand functionality from the multiple angles of high level classification methods, whole-hand grasp usage, and precision manipulation, where a small object is repositioned in the fingertips.</p><p> Our manipulation classification work presents a motion-centric scheme which can be applied to any human or hand-based robotic manipulation task. Most previous classifications are domain specific and cannot easily be applied to both robotic and human tasks, or can only be applied to a certain subset of manipulation tasks. We present a number of criteria which can be used to describe manipulation tasks and understand differences in the hand functionality used. These criteria are then applied to a number of real world example tasks, including a description of how the classification state can change over time during a dynamic manipulation task.</p><p> Next, our study of real-world grasping contributes to an understanding of whole-hand usage. Using head mounted camera video from two housekeepers and two machinists, we analyze the grasps used in their natural work environments. By tagging both grasp state and objects involved, we can measure the prevalence of each grasp and also understand how the grasp is typically used. We then use the grasp-object relationships to select small sets of versatile grasps which can still handle a wide variety of objects, which are promising candidates for implementation in robotic or prosthetic manipulators. </p><p> Following the discussion of overall hand shapes, we then present a study of precision manipulation, or how people reposition small objects in the fingertips. Little prior work was found which experimentally measures human capabilities with a full multi-finger precision manipulation task. Our work reports the size and shape for the precision manipulation workspace, and finds that the overall workspace is small, but also has a certain axis along which more object movement is possible. We then show the effect of object size and the number of fingers used on the resulting workspace volume – an ideal object size range is determined, and it is shown that adding additional fingers will reduce workspace volume, likely due to the additional kinematic constraints. Using similar methods to our main precision manipulation investigation, but with a spherical object rolled in the fingertips, we also report the overall fingertip surface usage for two- and three-fingered manipulation, and show a shift in typical fingertip area used between the two and three finger cases. </p><p> The experimental precision manipulation data is then used to refine the design of an anthropomorphic precision manipulator. The human precision manipulation workspace is used to select suitable spring ratios for the robotic fingers, and the resulting hand is shown to achieve about half of the average human workspace, despite using only three actuators.</p><p> Overall, we investigate multiple aspects of human hand function, as well as constructing a new framework for analyzing human and robotic manipulation. This work contributes to an improved understanding of human grasp usage in real-world environments, as well as human precision manipulation workspace. We provide a demonstration of how some of the studied aspects of human hand function can be applied to anthropomorphic manipulator design, but we anticipate that the results will also be of interest in other fields, such as by helping to design devices matched to hand capabilities and typical usage, or providing inspiration for future methods to rehabilitate hand function.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10584937 |
| Date | 27 July 2017 |
| Creators | Bullock, Ian Merrill |
| Publisher | Yale University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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