Artificial Intelligence and Machine Learning (AI&ML)

Find and use data patterns.

Our scope of services

Artificial intelligence methods, in particular machine learning, allow computer systems to improve themselves automatically on the basis of sample data. Explicit programming is no longer required, not only allowing the performance of already existing functions to be optimized but also enabling access to entirely new activities.



At Fraunhofer ISST, application-specific AI and ML pipelines (i.e. interlinked processing steps) are researched and developed for the areas of healthcare, logistics and data management. Depending on the application, these can be based on different data sources such as biosignals (such as measurements using EKG or 3D acceleration sensors), audio, images, videos, texts or a combination of several data sources. We support our partners along the entire pipeline, starting with the identification and feasibility assessment of ML application scenarios, through the preprocessing of the (raw) data to the selection and training of suitable models and their evaluation based on application-specific performance criteria. Depending on the requirements, classical learning methods (such as Support Vector Machines, Decision Tree) are used as well as Deep Learning approaches.

The services offered by Fraunhofer ISST include identification and feasibility assessment of machine learning (ML) deployment scenarios, data pre-processing, selection and hyperparameter optimization of suitable ML models and their evaluation.


Identification and feasibility assessment of ML application scenarios

  • Identification of application-specific optimization potentials as well as new areas of responsibility through the use of AI or ML
  • Feasibility assessment of the use of AI or ML based on the available data


Preliminary processing of data         

  • Clean up the raw data
  • Feature calculation based on biosignal data (e.g. 3D acceleration, EKG, audio) from the time and frequency domains
  • Use of feature selection and extraction methods

Training and evaluation of ML models

  • Conception of AI/ML-based applications
  • Selection from different learning approaches, e.g. classic classification methods, deep learning, association analysis and clusters
  • Hyper-parameter optimization, evaluation based on application-specific performance metrics
  • Use of windowing methods, time series analysis
  • Use of techniques for dealing with imbalanced data, e.g. data augmentation, cost-sensitive classification



Artificial intelligence methods, in particular machine learning, can be used to support various industries or completely open up new areas of responsibility. Whether for automated quality control in logistics, the diagnosis of diseases, the real-time detection of critical situations in the health sector or for the extraction of information from documents, the possibilities are only limited by the availability of data.


Here you will find a selection of approved application examples from the “AI&ML” area of expertise from the past few years. Are you looking for more information? Just get in touch with us – our contact partners will be happy to answer your questions and talk to you.


Example 1:

Electronic pallet record

Determining the quality of Euro-pallets

In the “electronic pallet record” project within the silicon economy framework, the age of pallets is determined using recurrent convolutional neural networks based on a photo taken with an app. The pallets are first identified and then evaluated. Background recognition helps to better assess various isolated attributes (such as pallet brightness)..

External project page



Example 2:

Metropolis Ruhr

Digital model destination North Rhine-Westphalia

The core objective of the project “Metropolis Ruhr: Digital model destination North Rhine-Westphalia” of Ruhr Tourismus GmbH is to establish a data hub for tourism data in the Ruhr region. This also includes a central media database that is intended to replace the previous storage location for tourist images. The targeted search for images is to be made easier in the future, among other things, by automatically generated keywords. As part of the project, existing models for object recognition on images were further developed.

Internal project page


Example 3:


Using services with data sovereignty in the urban district of the future

The QuarZ project – “Quartier der Zukunft” – aims to improve the everyday lives of people in urban districts. Services, for example in the smart home, smart invoice, district network, and mobility fields, are being developed for this purpose within the project framework. The platform being created in the project converges the data of the increasingly networked urban living space, links them, and thereby makes them usable for additional smart services. Among others, the elements of the networked urban district include the installation of sensors for weather, environmental, and city data as well as a software platform to converge and use the data from these sensors, supplemented with data from other sources. A portal for tenants with an interface for smart home applications makes it easy to access the services in the home.

Internal project page


Example 4:


Mobile health system to assist Parkinson’s patients

The objective of the project PCompanion subsidized by the Federal Ministry is to develop the first patient-friendly, mobile screening and monitoring system for the early diagnosis of Parkinson’s disease. The focus is on the early detection of disturbances in REM sleep and the vegetative nervous system with the help of a sensor close to the body.

Internal project page

External project page


Example 5:


Nursing support for people with epilepsy through innovative ear sensors

The aim of the “EPItect” project is to develop an in-ear sensor that can detect the occurrence of epileptic seizures based on the measured biosignals. The documented data is made available to selected people via mobile devices, which means that the caring environment can also be included if necessary. For this purpose, new models for seizure detection based on machine learning processes are being developed in the project.

Internal project page

External project page



Example 6:


Mobile, smart neuro-sensor system for the detection and documentation of epileptic seizures in everyday life

Within the “MOND” project, a conceptual proof (proof-of-concept) for an AI-based sensor system for the automated detection of epileptic seizures in everyday life is sought. The data acquisition should take place via mobile sensors worn on the ear, which should also enable a mobile recording of an electroencephalogram (EEG) with a special focus. The project is based on the results of the “EPItect” project.

Internal project page


Example 7:

Digital Angel

Strengthening the interaction work of caregivers through the use of digital assistants

In the research project “Digital Angel”, possible uses of digital assistants in the field of nursing are examined. For this purpose, ML models for the detection of stress in nursing staff based on a mobile EKG are being developed. The aim is to relieve caregivers in their daily work and make the nursing profession more attractive in the long term.

Internal project page

External project page



Example 8:


Automated audio analysis of carotid artery blood flow sounds

The objective of the “Body Tune” project is to use the automated analysis of body sounds, using carotid stenosis as an example, to improve the early diagnosis of illness and care for at-risk patients on the one hand and, on the other hand, to individualize therapy and improve patient compliance and inclusion. For this purpose, ML models are being developed which allow a statement to be made about the state of health of a person based on the blood flow sounds of the carotid artery.

Internal project page

External project page




List of scientific publications

HENZE, Jasmin; HOUTA, Salima; SURGES, Rainer; KREUZER, Johannes; BISGNI, Pinar. Multimodal Detection of Tonic-Clonic Seizures Based on 3D Acceleration and Heart Rate Data from an In-Ear-Sensor. In: Del Bimbo A. et al. (eds) Pattern Recognition. ICPR International Workshops and Achallenges. ICPR 2021. Lecture Notes in Computer Science, vol 12661. Springer, Cham. 2021. ISBN: 978-3-030-68762-5

BISGIN, Pinar; BURMANN Anja, LENFERS, Tim. REM Sleep Stage Detection of Parkinson’s Disease Patients with RBD. In: International Conference on Business Information Systems. Springer, Cham, 2020. S. 35-45. ISBN: 978-3-030-53337-3

MEISTER, Sven; HOUTA, Salima; BISGIN, Pinar. Mobile Health und digitale Biomarker: Daten als „neues Blut “für die P4-Medizin bei Parkinson und Epilepsie. In: mHealth-Anwendungen für chronisch Kranke. Springer Gabler, Wiesbaden, 2020. S. 213-233. ISBN: 978-3-658-29133-4

HOUTA, Salima; BISGIN, Pinar; DULICH, Pascal. Machine Learning Methods for Detection of Epileptic Seizures with Long-Term Wearable Devices. In: Elev Int Conf EHealth, Telemedicine, Soc Med. 2019. S. 108-13. ISBN: 978-1-61208-688-0

BISGIN, P.; MEISTER, S.; HAUBRICH, C. Erkennen von parkinsonassoziierten Mustern im Schlaf und Neurovegetativum, 64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), Dortmund, 2019. Abstract 44.