Stiffness and Pliability

Stiffness and Pliability: Developing an Algorithm to Identify Intrinsic and Reflexive Stiffness during Voluntary Movement and a Shared Mental Model Making Cross-Disciplinary Collaboration Dynamics Meaningful to Engineers

van Koppen, Jorik (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Applied Sciences)

Schouten, A.C. (mentor)
Kalmar, E. (mentor)
Mugge, W. (graduation committee)
van der Sanden, M.C.A. (graduation committee)

Delft University of Technology

Mechanical Engineering

2020-06-22

System identification techniques to analyse movement disorders are in development, but concrete clinical evidence to receive broad support from the clinical world is lacking. This master thesis proposes two ways to accelerate their development as part of the master’s programmes in Mechanical Engineering and Science Communication. The Mechanical Engineering part focuses on the development of system identification techniques themselves. For this study, an ensemble-based algorithm is developed to identify intrinsic and reflexive joint stiffness during voluntary movements. The algorithm combines two methods from previous research: the Parallel-Cascade method to separate intrinsic and reflexive stiffness and an instrumental variable approach to allow subjects to carry out voluntary movements and apply closed-loop identification. A simulation and experimental study are conducted with the human wrist joint in which a sinusoidal angle reference trajectory is followed using multiple realisations of the same trial. To elicit the modulation of intrinsic parameters, a torque field is applied that is linearly dependent on the wrist angle with a greater flexion resulting in a higher torque. In the simulation study, the intrinsic and reflexive pathways are separated and over 98% of the variance is explained. In the experimental study, the modulation of intrinsic stiffness is estimated with an explained variance of over 90%. Intrinsic stiffness is highest during the peaks of the trajectory and lower during the transition phase from one peak to another. However, the torque response of the experimental study shows that no significant extra torque is added during the reflexive EMG peak. The obtained results underline the promising potential of the algorithm and open up new possibilities once the algorithm is tested on an experimental task where more reflexes are elicited. The Science Communication part focuses on developing a shared mental model to transfer knowledge on the processes behind cross-disciplinary collaborations to mechanical engineers, as devoting time and effort to the metacognitive process helps in coping with it. However, most theories on cross-disciplinarity are written from a social sciences paradigm and are inherently difficult to interpret from a mechanical engineering paradigm. An analogy is developed to link concepts from both epistemologies together. This is done by assembling a theoretical framework containing concepts on the processes behind cross-disciplinary collaborations. These concepts are linked to analogous counterparts from mechanical engineering by relying on conceptual similarities between mechanical stiffness and collaborative pliability. As a result, knowledge of cross-disciplinary collaborations can be immersed with existing conceptual knowledge of mechanical stiffness. Collaboration theories become part of the primary discipline instead of being treated as a separate discipline. The model, called the Collaborative Pliability Model, is assessed by a group of six mechanical engineering students. The students considered the model to be an effective tool for learning about cross-disciplinary collaborations. By using the model, they came up with numerous new factors affecting the success of their collaborative process. This gives a promising outlook for the future and opens the door for new applications for developing an analogy used to transfer knowledge between specific target groups.

Stiffness
Pliability
Wrist Joint Stiffness
Parallel-Cascade
Instrumental Variable
System Identification
Shared Mental Model
Analogy
Interdisciplinary
Cross-disciplinary
Transdisciplinary
Multidisciplinary

http://resolver.tudelft.nl/uuid:7bb3e0a7-f7ec-4299-b8d2-b4bbfae15d59

Student theses

master thesis

© 2020 Jorik van Koppen