DFKI, as coordinator the EU-funded BIONIC project, is working with ten international partners in medicine, biotechnology, electronics, information technology, and artificial intelligence to develop intelligent solutions that will reduce such health concerns. BIONIC is shorthand for the study of “personalized body sensor networks with built-in intelligence for real-time risk assessment and coaching of ageing workers in all types of working and living environments.” The project results will be validated in pilot tests with construction and factory workers.
Body Sensor Network (BSN) for real time analysis of loads and correction of misalignments Prof. Didier Stricker, project coordinator and Head of DFKI's Augmented Vision/Augmented Reality Labs explains: “Using a network of different sensors worn on the body, we are developing a system that measures the state of the worker's health based on movements over the course of the work day. The analysis is performed by an intelligent chip directly on the body, so that raw data can be preprocessed directly at the ‘source’, enabling the real-time calculation of the data flows. Innovative risk analysis methods provide direct feedback about loads and misalignments. Playful applications and a training app motivate the user to counteract unilateral stress while also providing individualized and medical support for a home workout.”
Further development of project "EASY-IMP" applications
Several partners have successfully worked on the development of a BSN as part of the EU sponsored and DFKI-managed “EASY-IMP” project. The predecessor project used small inertial measurement sensors (IMUs) attached to the clothing or affixed to the skin for the analysis of body movements. BIONIC is further developing the lightweight and modular design of the system to facilitate easier integration.
Biomechanical models and “Deep Learning” for ergonomic risk assessments
Algorithms for ergonomic risk assessment of physical loads are developed using biomechanical models of age-related and chronic impairments. Among the input parameters are things like posture, forces, and torques as well as physiological parameters like heart frequency and body temperature.
Objective and subjective data form the basis of procedures that are then complemented by personalized algorithms for further use in Deep Learning methods. All generated data are stored in accordance with the EU Data Protection Directive.
Project partners:
• German Research Center for Artificial Intelligence (DFKI) (Project coordination)
• Kaiserslautern University of Technology – wearHEALTH Group
• Instituto de Biomechanica de Valencia, Spain
• Roessingh Research and Development, University of Twente, Netherlands
• University of Piraeus – Systems Security Lab, Greece
• Interactive Wear GmbH, Munich
• Hypercliq IKE, Greece
• ACCIONA Construcción S.A. – Spain
• Rolls-Royce Power Systems AG – Friedrichshafen
• Federal German Institute of Occupational Safety and Health (BAuA), Dortmund
• Fundación Laboral de la Construcción – Spain
Project volume:
approx. 4 million euros.
Project term:
3 years (01/2019 – 01/2022)
Contact:
Prof. Dr. Didier Stricker
Head of Augmented Vision Department
German Research Center for Artificial Intelligence (DFKI)
Trippstadter Straße 122
67663 Kaiserslautern
E-Mail: Didier.Stricker@dfki.de
Phone: +49 631 20575 3500
Press contact:
Christian Heyer
Head of Corporate Communications DFKI Kaiserslautern
German Research Center for Artificial Intelligence (DFKI)
Trippstadter Straße 122
67663 Kaiserslautern
E-Mail: Christian.Heyer@dfki.de
Phone: +49 631 20575 1710