From Lab to Life: Bridging Gaps in Motion Capture to Increase Public Usability through Integrated Hardware and Software Solutions

This Master’s thesis delves into the initial stages of the Lab-In-A-Box (LabX) project, an initiative within MIT’s Center for Clinical and Translational Research (CCTR). LabX is dedicated to simplifying the incorporation of Motion Capture (MoCap) technology into home environments. The project’s primary aim is to create portable and accurate MoCap systems, utilizing less intrusive technology (such as RADAR signals instead of traditional IR or visible light) for capturing motion of individuals in their everyday lives. This approach seeks to revolutionize MoCap’s applicability, making it more accessible and user-friendly for public use. The central focus of this research is the development of a portable and stable sensor rig, which is crucial to LabX’s mission. Designed for precise data capture, the rig emphasizes ease of deployment and versatility, ensuring that it can be effectively used in various settings outside of specialized laboratories. In addressing the challenges presented by traditional MoCap systems, the thesis details the hardware development process, focusing around the creation of the project’s sensor rig, and incorporating sensor fusion technology. This enhancement allows simultaneous data capture at different locations, emphasizing stability and portability for versatile application in various public settings. The thesis extends its focus to LabX’s overarching goal of enhancing MoCap’s public accessibility through integrated hardware and software solutions. A holistic approach is emphasized, encompassing sensor fusion and machine learning components. This integration aims to bridge gaps in traditional setups and render MoCap technology more inclusive and widely applicable. This research significantly contributes to advancing user-friendly MoCap technology, signifying a transition from controlled laboratory environments to real-world applications. The incorporation of hardware, sensor fusion, and machine learning solutions in LabX establishes a foundation for future advancements, ultimately enriching public interaction with motion capture and seamlessly integrating it into everyday life.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Lonni, Pierre

Advisor dc:contributor.advisor

• Anthony, Brian

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/153731

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/153731