AIfES demonstrators

In order to show the endless options with AIfES, we develop constantly new demonstrators to learn and extend AIfES. Demonstrators offers us the possibility to share in an easy manner what AIfES can do. AIfES is the property of Fraunhofer IMS for the time being, however you can incorporate AIfES into your products as part of a common project. If you have any questions on this topic, please feel free to contact us.

Wireless current sensor for condition monitoring

This exhibit was developed together with the business unit "Wireless and Transponder Systems". Here AIfES was used to learn the states of a device based on its power consumption. AIfES was integrated on an ATMega32U4 including an learning algorithm. Further information about the demonstrator can be found here: info sheet.

Recognition of handwritten digits on a microcontroller

With this demonstrator we wanted to answer the question whether it is possible to integrate handwriting recognition (for digits) on a microcontroller. The digits are drawn on a standard PS/2 touchpad, then recognized and the result presented on an LC-Display. We have successfully integrated all functions on an Arduino UNO by means of a feature extraction specially developed for this purpose. The Arduino UNO reads in the sensor values of the touchpad, carries out the number recognition and outputs the result on the display. Despite the low CPU clock frequency of 16 MHz, the number recognition is carried out in approx. 20 to 25ms. Further information can be found here: info sheet.

Personalizable gesture recognition

Demonstrator for a personalizable gesture recognition which can be trained directly in the system. The Fraunhofer IMS is researching on a personalizable artificial intelligence (AI), which offers the possibility that devices can be adapted or optimized to their user by means of training. The AIfES development team has used our gesture recognition demonstrator as a basis to show the potential of a personalizable AI.

Recognition of humans by means of embedded AI: Project "noKat"

In the project "noKat" (development of a neural optical camera tracker for the detection of approaching persons), which is funded by the German Federal Ministry for Economic Affairs and Energy as part of the "Central Innovation Program for SMEs" (ZIM), Fraunhofer IMS is developing an optical proximity sensor together with the partner company van Rickelen GmbH & Co. KG. The optical proximity sensor that is able to detect approaching persons by means of artificial intelligence (AI) from RGB images of a low-cost camera.

Artificial intelligence for digit recognition
© Fraunhofer IMS
Artificial intelligence for digit recognition
© Fraunhofer IMS
AIfES digit recognition demonstrator

Technical Details

Platform independency and special learning techniques distinguish AIfES

Range of Application

Human-Technology Interaction, Industry 4.0, Metrology, Medical Technology, Machine Learning Algorithms and Hardware Accelerators.


How can I use AIfES and what services does Fraunhofer IMS offer?

Our applications - Examples of what we can do for you

Decentralized AI systems and AIfES platform

AI Framework, open roberta, Arduino

Recognition of humans by means of embedded AI


Federated learning for resource-constrained systems


Personalizable AI

Individually trainable gesture recognition

Our fields of application - Our expertise for you

Sustainable Production

  • Optimization of raw material and energy use
  • Use of alternative energy sources and energy-autonomous sensors
  • Green ICT

Mobile autonomous Manufacturing

  • Sensors / Control for Robots / Cobots
  • Industrial transport systems (AGV)
  • Human-Machine Interaction


  •  Decentralized AI systems and platforms
  • Sensor/actuator optimization and cost efficiency through local AI.
  • Pattern recognition methods

Trustworthy Electronics

  • Protection against product piracy / Counterfeit-proof labeling
  • Tamper-proof and fail-safe electronics
  • Trustworthy supply chains

Industry (Home)

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