Robert Matthew

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2018-143

December 1, 2018

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-143.pdf

This thesis introduces a kinematic and dynamic framework for creating a representative model of an individual. Building on results from geometric robotics, a method for formulating a geometric dynamic identification model is derived. This method is validated on a robotic arm, and tested on healthy subjects to determine the utility as a clinical tool.

The proposed framework was used to augment the five-times sit-to-stand test. This is a clinical test designed to estimate an individual's stability by timing the total time to stand/sit five times. Using the proposed framework, a representative kinematic and dynamic model was obtained which outperformed conventional height/mass scaled models. This allows for rapid, quantitative measurements of an individual, with minimal retraining required for clinicians.

These tools are then used to develop a prescriptive model for developing assistive devices. The recovered models can be used to formulate an optimisation to determine the actuator types and parameters to provide augmentation.

This framework is then used to develop a novel system for human assistance. A prototype device is developed and tested. The prototype is lightweight, uses minimal energy, and can provide an augmentation of 82% for providing hammer curl assistance. The modelling framework is used to analyse the effect this assistance has on compensatory actions of the shoulder.

Advisors: Masayoshi Tomizuka and Ruzena Bajcsy

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BibTeX citation:

@phdthesis{Matthew:EECS-2018-143,
    Author= {Matthew, Robert},
    Editor= {Bajcsy, Ruzena and Tomizuka, Masayoshi and Full, Robert},
    Title= {Better, Faster, Stronger: Measuring and Transcending Your Physical Limits with Wearable Robots},
    School= {EECS Department, University of California, Berkeley},
    Year= {2018},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-143.html},
    Number= {UCB/EECS-2018-143},
    Abstract= {This thesis introduces a kinematic and dynamic framework for creating a representative model of an individual. Building on results from geometric robotics, a method for formulating a geometric dynamic identification model is derived. This method is validated on a robotic arm, and tested on healthy subjects to determine the utility as a clinical tool.

The proposed framework was used to augment the five-times sit-to-stand test. This is a clinical test designed to estimate an individual's stability by timing the total time to stand/sit five times. Using the proposed framework, a representative kinematic and dynamic model was obtained which outperformed conventional height/mass scaled models. This allows for rapid, quantitative measurements of an individual, with minimal retraining required for clinicians.  

These tools are then used to develop a prescriptive model for developing assistive devices.  The recovered models can be used to formulate an optimisation to determine the actuator types and parameters to provide augmentation.

This framework is then used to develop a novel system for human assistance.  A prototype device is developed and tested. The prototype is lightweight, uses minimal energy, and can provide an augmentation of 82% for providing hammer curl assistance. The modelling framework is used to analyse the effect this assistance has on compensatory actions of the shoulder.},
}

EndNote citation:

%0 Thesis
%A Matthew, Robert 
%E Bajcsy, Ruzena 
%E Tomizuka, Masayoshi 
%E Full, Robert 
%T Better, Faster, Stronger: Measuring and Transcending Your Physical Limits with Wearable Robots
%I EECS Department, University of California, Berkeley
%D 2018
%8 December 1
%@ UCB/EECS-2018-143
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-143.html
%F Matthew:EECS-2018-143