Imagine your older workers wearing a lightweight sensor system — like a fitness tracker, but smarter — that watches how they move, lift, and bend throughout the day. When the system spots a risky motion that could lead to a back injury or muscle strain, it sends an instant alert to the worker's phone. It even coaches them on safer ways to do the task, using game-like rewards to keep them engaged. The whole thing was tested on real construction sites and factory floors with actual workers.
Companies in construction and manufacturing face rising costs from musculoskeletal injuries, especially among ageing workers who carry decades of expertise but are increasingly vulnerable to physical strain. Traditional safety measures are reactive — they kick in after an injury has already happened, leading to lost workdays, compensation claims, and the loss of experienced personnel.
The team built a complete wearable body sensor network with an embedded AI processing unit running deep neural networks on-device, a smartphone app for real-time strain estimation and risk alerting, biomechanical parameter estimation software, and data monitoring prototypes that let workers, doctors, and managers share health data under GDPR-compliant controls. In total, 32 deliverables were produced including 6 working demonstrators.
If you are a construction company dealing with rising injury rates among experienced workers over 50 — this project developed a wearable body sensor network with a smartphone app that estimates physical strain in real time and alerts workers before a dangerous movement causes injury. It was validated in real construction workplace environments with 12 consortium partners across 4 countries.
If you are a manufacturer struggling with musculoskeletal disorder claims on your production floor — this project built an embedded AI processing unit with deep neural networks that runs biomechanical analysis directly on the wearable device, no cloud needed. The system monitors body posture and movement, providing risk detection and online alerting through a dedicated app.
If you are an occupational health provider looking for data-driven tools to assess ergonomic risk — this project created data monitoring prototypes for workers, doctors and managers, with GDPR-compliant data sharing controls. Workers manage exactly what health data gets shared with whom, built around personal data protection from the ground up.
The project received EUR 3,995,195 in EU funding across 12 partners to develop the technology. Per-unit pricing for the body sensor network hardware and software is not published in available project data. You would need to contact the consortium for commercial licensing or deployment costs.
The embedded AI processing unit runs deep neural networks directly on the wearable hardware, which means it works autonomously without constant cloud connectivity. The network application layer software provides a clear API for data exchange from sensors to applications. This architecture supports scaling across sites, though large-scale deployment beyond the pilot validation would need further discussion with the developers.
The project was a Research and Innovation Action (RIA) coordinated by DFKI (German Research Center for Artificial Intelligence) with 12 partners. IP is typically shared among consortium members under Horizon 2020 rules. With 4 SMEs and 6 industry partners in the consortium, there are likely commercial licensing pathways already considered.
Results were validated in real workplace environments by two prominent European enterprises from the Construction and the Manufacturing sectors. The project produced 6 demonstrator deliverables including a functional embedded AI unit, a smartphone alerting app, and data monitoring prototypes for workers, doctors and managers.
Data protection and GDPR compliance were core design principles, listed among the project keywords. A dedicated prototype for data management and sharing was built that allows workers to manage and control the sharing of their data captured by the body sensor network with other entities such as doctors and managers.
The system uses configurable Body Sensor Networks with NFMI (Near Field Magnetic Induction) communication between sensor modules. The embedded AI processing unit integrates dedicated hardware for running deep-learning algorithms efficiently, ensuring autonomy of the overall network without external processing dependency.
The project ran from January 2019 to March 2022 and is now closed. It produced 32 deliverables in total, including 6 demonstrator prototypes. The technology readiness would need to be confirmed with the consortium for current commercialization status.
This is a strong industry-oriented consortium with 12 partners across Germany, Greece, Spain, and the Netherlands. Half the consortium (6 partners) comes from industry, and 4 of those are SMEs — a good sign that commercial application was a priority from day one. The coordinator, DFKI, is Germany's leading AI research center with deep experience bringing research to market. With 2 universities and 2 research institutes rounding out the team, plus validation at two major European enterprises in construction and manufacturing, the consortium was clearly designed to bridge the gap between wearable technology research and real workplace deployment.
DFKI (German Research Center for Artificial Intelligence), Germany — contact through SciTransfer for introduction
Want to explore how BIONIC's wearable injury prevention technology could work for your workforce? SciTransfer can arrange a direct introduction to the research team and help evaluate fit for your specific workplace environment.
