Intent Detection and Feedback (IDF)5 ECTS
(englische Bezeichnung: Intent Detection and Feedback)
(Prüfungsordnungsmodul: Intent Detection and Feedback)

Lehrveranstaltungen:

• • Intent Detection and Feedback (L)
    (Vorlesung, 2 SWS, Claudio Castellini, Do, 14:15 - 15:45, K2-119)
  • Intent Detection and Feedback (E)
    (Übung, 2 SWS, Fabio Andre Egle, Fr, 10:15 - 11:45, K2-119)

Empfohlene Voraussetzungen:

Recommended: basic maths, especially statistics; fundamentals of signal processing and machine learning; mid-level programming Python, C# or similar; fundamentals of experimental psychology

Inhalt:

• Introduction to the problems of intent detection and somatosensory feedback: motivation, taxonomy, historical background.

• Intent detection: theory and philosophical issues; defining the problem and the ground truth; success metrics; signals for intent detection; sensors for intent detection; feature extraction; applications of machine learning to the problem.

• Somatosensory feedback: theory and physiology; sensory substitution; embodiment and agency induced by it; modalities of actuation; practical issues and metrics of performance.

• Intent detection and somatosensory feedback in prosthetics: usefulness, success and challenges.

• Intent detection and somatosensory feedback in rehabilitation and exoskeletons: usefulness, success and challenges.

• Intent detection and somatosensory feedback in gaming and non-reha fields.
  

Lernziele und Kompetenzen:

Students who have followed the module

• have a broad understanding of intent detection and somatosensory feedback, especially in the frame of Rehabilitation and Assistive Robotics

• can conceive and design a research project in the related subfield of the subject

• have knowledge about the clinical and industrial situation of intent detection and feedback, especially including the problems and challenges of each technique and method

• can tackle previously unknown problems
  

Literatur:

• [2010] Control of Hand Prostheses Using Peripheral Information, S. Micera, J. Carpaneto and S. Raspopović.

• [2012] Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review, A. Fougner, Ø. Stavdahl, P. J. Kyberd, Y. G. Losier and P. A. Parker.

• [2015] Michael R Tucker et al., Control strategies for active lower extremity prosthetics and orthotics: a review, JNER 12:1

• [2015] A survey of sensor fusion methods in wearable robotics, D. Novak and R. Riener

• [2016] Incremental Learning of Muscle Synergies: From Calibration to Interaction, C. Castellini.

• [2018] JA Spanias, AM Simon, SB Finucane, EJ Perreault and LJ Hargrove, Online adaptive neural control of a robotic lower limb prosthesis, J Neural Eng. 15(1)

• [2020] Jacob Rosen and Peter Walker Ferguson (eds.), Wearable Robotics – Systems and Applications, Academic Press Elsevier

• [2021] Michele Xiloyannis, Ryan Alicea, Anna-Maria Georgarakis, Florian L. Haufe, Peter Wolf, Lorenzo Masia and Robert Riener, Soft robotic suits: State of the art, core technologies and open challenges, IEEE Transactions on Robotics
  

Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:

1. Artificial Intelligence (Master of Science)
   (Po-Vers. 2021s | TechFak | Artificial Intelligence (Master of Science) | Gesamtkonto | Wahlpflichtmodulbereich | AI Systems and Applications | Intent Detection and Feedback)

Studien-/Prüfungsleistungen:

Intent Detection and Feedback (Prüfungsnummer: 76161)

(englischer Titel: Intent Detection and Feedback)

Prüfungsleistung, Klausur, Dauer (in Minuten): 60, benotet, 5 ECTS

Anteil an der Berechnung der Modulnote: 100.0 %

Prüfungssprache: Englisch

Erstablegung: SS 2022, 1. Wdh.: WS 2022/2023

1. Prüfer:

Claudio Castellini

Termin: 02.08.2022
Termin: 02.08.2022