A sensor-based physical tool adaptation framework for facilitating motor skills learning

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.

Bibliographic Details
Main Author: Qi, Yini
Other Authors: Stefanie Mueller.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2019
Subjects:
Online Access:http://hdl.handle.net/1721.1/120389
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author Qi, Yini
author2 Stefanie Mueller.
author_facet Stefanie Mueller.
Qi, Yini
author_sort Qi, Yini
collection MIT
description Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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spelling mit-1721.1/1203892019-04-11T02:03:20Z A sensor-based physical tool adaptation framework for facilitating motor skills learning Qi, Yini Stefanie Mueller. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 45-46). Many motor skills that people learn throughout their lives involve mastering a physical tool, such as riding a bike, writing with a pen, or playing basketball. When learning these skills, people often use physical learning aids to provide support. However, currently these learning aids only come in predefined levels. For instance, training wheels on a bike are either mounted or taken off. This jump from an easy task to a much harder one makes the transition difficult in learning the skill. In this thesis, we address this challenge by adapting the physical tool according to the learner's progress. For instance, while learning to ride a bike, we monitor learners' balancing skills and as they improve, we gradually lift the training wheels to reduce support and increase the difficulty. Thus, this approach enables a step-by-step transition from an easy to hard level that, like existing adaptive learning systems for math and language skills, is personalized for each individual learner. To illustrate this idea, we built an end-to-end system that allows designers to setup adaptable tools that physically change when a learner's skill level increases. This system uses sensors integrated with the tools to measure progress; parametric 3D modeling to adapt the tool; and either actuation or re-fabrication to deploy the physical change. by Yini Qi. M. Eng. 2019-02-14T15:23:37Z 2019-02-14T15:23:37Z 2018 2018 Thesis http://hdl.handle.net/1721.1/120389 1084661165 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 46 pages application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science.
Qi, Yini
A sensor-based physical tool adaptation framework for facilitating motor skills learning
title A sensor-based physical tool adaptation framework for facilitating motor skills learning
title_full A sensor-based physical tool adaptation framework for facilitating motor skills learning
title_fullStr A sensor-based physical tool adaptation framework for facilitating motor skills learning
title_full_unstemmed A sensor-based physical tool adaptation framework for facilitating motor skills learning
title_short A sensor-based physical tool adaptation framework for facilitating motor skills learning
title_sort sensor based physical tool adaptation framework for facilitating motor skills learning
topic Electrical Engineering and Computer Science.
url http://hdl.handle.net/1721.1/120389
work_keys_str_mv AT qiyini asensorbasedphysicaltooladaptationframeworkforfacilitatingmotorskillslearning
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