Neuroprosthetics: Artificial Limbs
Module IN2405
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Basic Information
IN2405 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Focus Area Bio-Sensors in M.Sc. Biomedical Engineering and Medical Physics
| Total workload | Contact hours | Credits (ECTS) |
|---|---|---|
| 150 h | 60 h | 5 CP |
Content, Learning Outcome and Preconditions
Content
The module will present the following topics:
- History and current evolution of neuroprosthetics, emerging trends in the field, opportunities and challenges.
- Anatomy of the upper limb, general principles associated with the production of movements and muscle synergies, main causes of upper limb amputation, overview about different level of upper extremity amputation, current challenges.
- Upper limb and hand bionic prostheses: (1) current strategies for the development of neuroprostheses; (2) myoelectric control; (3) advanced methods: invasive and non-invasive techniques; (4) overview about interfaces and novel surgical techniques.
- Anatomy of the lower limb and main causes of amputation: general anatomy of the upper limb, general principles associated with locomotion, main causes of lower limb amputation, overview about different level of lower extremity amputation, current challenges.
- Lower limb bionic prostheses: (1) current strategies for the development of neuroprostheses for the restoration of walking; (2) myoelectric control; (3) advanced methods: invasive and non-invasive techniques; (4) overview about interfaces and novel surgical techniques.
- Haptic feedback: (1) current strategies for the development of systems for the restoration of haptic feedback; (2) advanced methods: invasive and non-invasive techniques.
- Functional assessments: current methods to evaluate the performance of neuroprostheses, Cybathlon, emerging trends in the field, opportunities and challenges
- History and current evolution of neuroprosthetics, emerging trends in the field, opportunities and challenges.
- Anatomy of the upper limb, general principles associated with the production of movements and muscle synergies, main causes of upper limb amputation, overview about different level of upper extremity amputation, current challenges.
- Upper limb and hand bionic prostheses: (1) current strategies for the development of neuroprostheses; (2) myoelectric control; (3) advanced methods: invasive and non-invasive techniques; (4) overview about interfaces and novel surgical techniques.
- Anatomy of the lower limb and main causes of amputation: general anatomy of the upper limb, general principles associated with locomotion, main causes of lower limb amputation, overview about different level of lower extremity amputation, current challenges.
- Lower limb bionic prostheses: (1) current strategies for the development of neuroprostheses for the restoration of walking; (2) myoelectric control; (3) advanced methods: invasive and non-invasive techniques; (4) overview about interfaces and novel surgical techniques.
- Haptic feedback: (1) current strategies for the development of systems for the restoration of haptic feedback; (2) advanced methods: invasive and non-invasive techniques.
- Functional assessments: current methods to evaluate the performance of neuroprostheses, Cybathlon, emerging trends in the field, opportunities and challenges
Learning Outcome
The module aims to provide knowledge about the main aspects, methodologies and tools for the design, control, testing and evaluation of robotic prosthetic systems, for upper and lower limbs. Particular emphasis is given to methods for mechatronic design, including integration with control interfaces and sensory feedback, and the development of novel functional assessments. On the basis of this knowledge, students are able to develop novel mechatronic systems, analyze electromyographic signals to control multi degrees of freedom devices and improve standard assessments.
Preconditions
Bachelor or intermediate diploma in informatics, computer science or engineering. Solid programming skills and knowledge of Matlab/Python and Unity3D is highly recommended.
Additionally, knowledge of the following modules is strongly recommended:
- Signal Theory (EI00330)
- Automatic Control (MW9020, MW2022, MW1530)
- Medical Augmented Reality (IN2293)
- Advanced Practical Course Games Engineering: Augmented Reality (IN7106)
- Robotics (IN2067)
Additionally, knowledge of the following modules is strongly recommended:
- Signal Theory (EI00330)
- Automatic Control (MW9020, MW2022, MW1530)
- Medical Augmented Reality (IN2293)
- Advanced Practical Course Games Engineering: Augmented Reality (IN7106)
- Robotics (IN2067)
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VO | 4 | Neuroprosthetics - Artificial Limbs (IN2405) | Capsi Morales, P. Piazza, C. Spiegeler Castaneda, T. Toker, B. |
Tue, 14:00–16:00, MI 03.13.010 and singular or moved dates |
Learning and Teaching Methods
The teaching methods will include:
- Lectures (2 SWS),
- Exercise and application-specific tutorials (2 SWS): design principles, control methods (based on surface EMG sensors), sensory feedback, virtual reality environment development
- Lectures (2 SWS),
- Exercise and application-specific tutorials (2 SWS): design principles, control methods (based on surface EMG sensors), sensory feedback, virtual reality environment development
Media
PowerPoint, Exercises in Matlab or Python
Literature
1) Aszmann, Oskar C., and Dario Farina. (2021). Bionic Limb Reconstruction. Springer
2) Merletti, R., & Farina, D. (Eds.). (2016). Surface electromyography: physiology, engineering, and applications. John Wiley & Sons.
3) M. Bishop, Neural networks for pattern recognition, Oxford university press, 1995.
4) Muzumdar, Powered Upper Limb Prostheses: Control, Implementation and Clinical Application, Springer Science & Business Media, 2004.
2) Merletti, R., & Farina, D. (Eds.). (2016). Surface electromyography: physiology, engineering, and applications. John Wiley & Sons.
3) M. Bishop, Neural networks for pattern recognition, Oxford university press, 1995.
4) Muzumdar, Powered Upper Limb Prostheses: Control, Implementation and Clinical Application, Springer Science & Business Media, 2004.
Module Exam
Description of exams and course work
Type of Assessment:
The examination consists of a written test of 90 minutes at the end of the course. In case of a low number of participants, the exam can be conducted orally.
In addition, the students will have to provide their solutions to practical exercises to the teaching assistant for evaluation, in the form of a written report and a presentation of contents and results. These exercises may focus on the application of design principles, myoelectric control methods or creation of virtual environments. The students can work on the practical exercises in small groups. The students will be awarded a bonus in case of excellent completion of the exercises.
Reasons for it:
To certify a deep understanding of what presented during the lectures. In addition, during the practical exercises, the students will have the opportunity to apply state-of-the-art methods to real world problems.
The examination consists of a written test of 90 minutes at the end of the course. In case of a low number of participants, the exam can be conducted orally.
In addition, the students will have to provide their solutions to practical exercises to the teaching assistant for evaluation, in the form of a written report and a presentation of contents and results. These exercises may focus on the application of design principles, myoelectric control methods or creation of virtual environments. The students can work on the practical exercises in small groups. The students will be awarded a bonus in case of excellent completion of the exercises.
Reasons for it:
To certify a deep understanding of what presented during the lectures. In addition, during the practical exercises, the students will have the opportunity to apply state-of-the-art methods to real world problems.
Exam Repetition
The exam may be repeated at the end of the semester.