MaDlrProsthetics2020

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Internship/Msc: Interaction Design for Upper-Limb Prosthetics Description of work

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Abstract

Despite decades of active research in material science, machine learning, signal processing and biomedical robotics myocontrol, that is natural control of an upper-limb prosthesis via bodily signals, is still unreliable, almost primitive. The main reason for this impasse is, we think, that myocontrol needs constant update and feedback from the user, in order to become stable and safe in all possible situations of the everyday life of an amputated person. It is not a standard machine-learning problem, in which you gather data, pass it through a neural network, and then use the obtained model to control the prosthesis forever. No, here something more interactive is needed. Actually, in order to close this gap, a few years ago in our lab we introduced the concept of incremental myocontrol, meaning a myocontrol system which can readily incorporate new information on-demand without forgetting the past one (or forgetting it selectively), thus increasing its reliability along time. The user is called upon to provide novel data as the system deems that it is the case; as well, the system is ready to grow its dataset whenever the user independently deems that the performance in a specific situation is no longer acceptable.

We have the technology: wearable and effective sensors, a realistic prosthetic setup, virtual reality as a start, knowledge on how to tune our favourite machine-learning approach. But we are missing the interaction protocol! What should the prosthesis look like, what functions and affordances should it provide? What should the timing be? What metrics to determine whether the interaction is effective?

Starting out from Jean Piaget and Ernst Von Glasersfeld's tuition on Constructivist Psychology, and going through Don Norman's lessons on the Design of Everyday Things, you are required to conceive and build an interaction interface and protocol for upper-limb prosthesis users, which will improve the reliability of the device. You will need to prove that your idea works by carrying out a comparative user study.

--++ Work breakdown literature analysis: upper-limb prosthetics; (Radical) Constructivism as a paradigm for HRI for amputees; HRI in myocontrol; interaction design in HRI familiarisation: VR setup, prosthetic setup (desired: familiarity with C# and Unity) designing a comparative user-study: does better interaction design lead to more reliable myocontrol? analysis of the data and experimental results. is our hypothesis confirmed?

Literature

Ernst von Glasersfeld, Learning as Constructive Activity, Proceedings of the 5th Annual Meeting of the North American Group of Psychology in Mathematics Education, 1995 Donald A. Norman, The design of everyday things: revised and expanded edition, New York: Basic Books, 2013, Philipp Beckerle, Claudio Castellini and Bigna Lenggenhager, Robotic interfaces for cognitive psychology and embodiment research: a research roadmap, Wiley Interdisciplinary Reviews - Cognitive Science, 2018 Claudio Castellini, Incremental learning of muscle synergies: from calibration to interaction, Ch.11 in Human and robot hands: sensorimotor synergies to bridge the gap between neuroscience and robotics, Springer International Publishing, 2016 Markus Nowak, Claudio Castellini and Carlo Massironi, Applying Radical Constructivism to machine learning: a pilot study in assistive robotics, Constructivist Foundations, 2018

--++ Contact person(s) Claudio Castellini DLR - German Aerospace Center Institute of Robotics and Mechatronics Wessling / Oberpfaffenhofen

Students.ProjectForm
Title: Interaction Design for Upper-Limb Prosthetics Description of work
Abstract: Despite decades of active research in material science, machine learning, signal processing and biomedical robotics myocontrol, that is natural control of an upper-limb prosthesis via bodily signals, is still unreliable, almost primitive. The main reason for this impasse is, we think, that myocontrol needs constant update and feedback from the user, in order to become stable and safe in all possible situations of the everyday life of an amputated person. It is not a standard machine-learning problem, in which you gather data, pass it through a neural network, and then use the obtained model to control the prosthesis forever. No, here something more interactive is needed. Actually, in order to close this gap, a few years ago in our lab we introduced the concept of incremental myocontrol, meaning a myocontrol system which can readily incorporate new information on-demand without forgetting the past one (or forgetting it selectively), thus increasing its reliability along time. The user is called upon to provide novel data as the system deems that it is the case; as well, the system is ready to grow its dataset whenever the user independently deems that the performance in a specific situation is no longer acceptable. We have the technology: wearable and effective sensors, a realistic prosthetic setup, virtual reality as a start, knowledge on how to tune our favourite machine-learning approach. But we are missing the interaction protocol! What should the prosthesis look like, what functions and affordances should it provide? What should the timing be? What metrics to determine whether the interaction is effective? Starting out from Jean Piaget and Ernst Von Glasersfeld's tuition on Constructivist Psychology, and going through Don Norman's lessons on the Design of Everyday Things, you are required to conceive and build an interaction interface and protocol for upper-limb prosthesis users, which will improve the reliability of the device. You will need to prove that your idea works by carrying out a comparative user study. https://www.youtube.com/watch?v=y6uI5FLB9Q4
Student:  
Director: Prof. Dr. Nassir Navab
Supervisor: Claudio Castellini (claudio.castellini {[at]} dlr.de), Dr. Ulrich Eck
Type: Master Thesis
Area: Registration / Visualization, Machine Learning
Status: open
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Finish:  
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