• basic maths, especially statistics

• fundamentals of signal processing and machine learning

• mid-level programming (Python, C# or similar)

• fundamentals of experimental psychology
  

• Introduction to upper-limb prosthetics (ULPs): background, motivation, body- vs. self-powered; state of the art

• ULPs as robotic arms: challenges and open questions

• Human-machine interfaces for ULPs

• Sensor modalities: surface electromyography and more

• Intent detection for ULPs: reliability, dexterity, pattern recognition, incrementality, interactive machine learning

• Feedback and sensory substitution

• Human-Machine Interaction in ULPs

• Designing ULP experiments

• The clinical perspective: impacting on the amputee’s everyday life

In the exercises, problems will be solved by working out code.

• [2002] Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal, M. Zecca, S. Micera, M. C. Carrozza and P. Dario.

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

• [2011] Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use, E. Scheme and K. Englehart.

• [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] 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.

• [2016] New developments in prosthetic arm systems, I. Vujaklija, D. Farina and O.C. Aszmann.

• [2019] Upper-limb active prosthetics: an overview, C. Castellini.