Course: Wearable and Implantable Computing [WIC]

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Course description

The course provides an overview on the design of wearable and implantable computing systems. The overview is structured in three parts: context recognition, electronic systems and sensors, and additive manufacturing methods. Pattern analysis and machine learning methods to process and infer context information are discussed. On the system functional level, frequent sensors and actuators and their design for on-body and implantable systems are analysed. Powering and energy management concepts will be detailed, including processing and task scheduling, sparse sampling and sparse sample signal processing. Energy harvesting methods for wearable and implantable systems are presented. Electronic design topics will be addressed, including bioelectronics, flexible electronics, electronics textile integration, multiprocess additive manufacturing. Principles of biocompatibility and system validation for remote health monitoring are covered. In exercise blocks along the lectures concrete design problems related to context recognition, energy-efficient processing, energy harvesting, and 2D/3D printing will be demonstrated, as well as realised and discussed in prototypes.

Learning objectives

  • Organise methods for context awareness, sensors and actuators for context management in body-worn embedded systems.
  • Describe design concepts and apply/analyse wearable and implantable system design methods and electronics encapsulation.
  • Analyse the electrical and physical principles, select and optimise on-body energy harvesting and power management techniques.
  • Implement methods for continuous context recognition and energy-efficient processing using sparse sampling, related signal and pattern processing methods.
  • Apply system evaluation methods, assess and design for biocompatibility and medical certification.
  • Create digital models and physical prototypes of wearable systems using additive manufacturing principles.

Course data

Presence time
Lecture: 2 SWS, Exercise: 2 SWS
Useful knowledge
Basics of Python. Basics in signal processing, materials, sensors.
Winter term 2021/22
Presence time
Mostly virtual course, working from remote. Details to be announced.
Obligatory, via StudOn.
First meeting First lecture on October 19, 08:15

Registration via StudOn obligatory. Please observe the registration times on StudOn.


Up-to-date literature recommendations are provided during the lectures.

Curriculum assignment

  • WPF MT-MA-BDV ab 1
  • WPF MT-MA-MEL ab 1
  • WPF MT-MA-GPP ab 1
  • WPF-DS-MA ab 1
  • WPF MT-BA ab 5


e-exam, to be confirmed.


Prof. Dr. Oliver Amft

  • Job title: Director
  • Phone number: +49 9131 85-23601
Friedrich-Alexander-Universität Erlangen-Nürnberg