Summary
The ability to reliably track 3D human motion in real-time is crucial for a wide range of applications across various fields, such as healthcare, entertainment, industry, and sports. Occupational ergonomics, as the intersection of industry and healthcare, could particularly benefit from such a solution, as recent reports indicate that physical and cognitive ergonomics issues at work contribute to major economic losses in Europe. However, the existing commercial human motion capture (MoCap) systems often exploit multiple markers or inertial measurement units (IMUs), which, in addition to their high costs, suffer from many technical and usability issues. These include lengthy set-up times, compromised user comfort and bothersome wearability needs, and in case of IMU based Mo-Cap systems, large drifts and inaccuracies over time. Some of these issues can be dealt with by using vision-based human skeletal trackers, however, high vulnerability to occlusions, and often, the missing or inaccurate 3D tracking capacity have severely depreciated their reliability-of-use for real-world applications. These leave us with no existing market solutions for marker-less (comfortable), robust, economic, and real-time 3D human motion tracking. Real-Move aims to create such a solution by exploiting multi-camera views and AI algorithms. The system will be capable of tracking multiple people in the scene with high accuracy and, in addition to motion tracking data, be able to provide application-driven data services such as human factors. Real-Move has the highest innovation potential to become a key player in the multi-Billion Euros MoCap and workplace safety markets. In addition, by providing quick and easy access to vision-based risk monitoring tools, it can make a huge socio-economic impact by improving the quality of life of the individuals at work while saving a large amount of money by cutting waste.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101136845 |
| Start date: | 01-01-2024 |
| End date: | 30-06-2025 |
| Total budget - Public funding: | - 150 000,00 Euro |
Cordis data
Original description
The ability to reliably track 3D human motion in real-time is crucial for a wide range of applications across various fields, such as healthcare, entertainment, industry, and sports. Occupational ergonomics, as the intersection of industry and healthcare, could particularly benefit from such a solution, as recent reports indicate that physical and cognitive ergonomics issues at work contribute to major economic losses in Europe. However, the existing commercial human motion capture (MoCap) systems often exploit multiple markers or inertial measurement units (IMUs), which, in addition to their high costs, suffer from many technical and usability issues. These include lengthy set-up times, compromised user comfort and bothersome wearability needs, and in case of IMU based Mo-Cap systems, large drifts and inaccuracies over time. Some of these issues can be dealt with by using vision-based human skeletal trackers, however, high vulnerability to occlusions, and often, the missing or inaccurate 3D tracking capacity have severely depreciated their reliability-of-use for real-world applications. These leave us with no existing market solutions for marker-less (comfortable), robust, economic, and real-time 3D human motion tracking. Real-Move aims to create such a solution by exploiting multi-camera views and AI algorithms. The system will be capable of tracking multiple people in the scene with high accuracy and, in addition to motion tracking data, be able to provide application-driven data services such as human factors. Real-Move has the highest innovation potential to become a key player in the multi-Billion Euros MoCap and workplace safety markets. In addition, by providing quick and easy access to vision-based risk monitoring tools, it can make a huge socio-economic impact by improving the quality of life of the individuals at work while saving a large amount of money by cutting waste.Status
SIGNEDCall topic
ERC-2023-POCUpdate Date
12-03-2024
Images
No images available.
Geographical location(s)
