Demonstration of a Radar Enabled weArable platforM
Grant period
2020-10-01 - 2022-03-31
Funding body
European Union
Call number
ERC-2020-PoC
Grant number
957570
Identifier
G:(EU-Grant)957570
Note: Sport research and procedures that aim to increase performance or assist recovery after an injury often require capture of athlete’s motion. However, implementing a motion capture system in routine practice takes significant effort. Capture volume, weather conditions, motion dynamics and athletes' timing (where timing defines coincide movements in relation to external factors) are the key parameters for outdoor sport activities (e.g. track and field or football), yet no available motion capture system is suitable for much needed high accuracy absolute position measurements of body segments in outdoor conditions with minimal setup effort for large capture volumes, which is capable of measuring the athletes' timing. Therefore, there is a significant unmet need for a portable motion capture system that can perform accurate infield measurements on a large volume, even in diverse weather conditions (e.g. sunlight, fog, rain) with the capability of the athletes' timing analysis, which is a combination of reaction time, decision -making and co-ordination in relation to external factors.
DREAM aims to generate a motion capture and mapping system (minimum viable product) that creates a complete orientation and absolute position measurement with no external position reference, provides reliable orientation of body segments even for high dynamic motions, and has a small form factor, which is minimally invasive for the athlete. Further, the offered solution is complementary to existing solutions and operates in real time. DREAM’s value proposition is a cost-effective, robust and accurate infield motion capture and mapping system with minimal setup effort, for large field coverage in any place and in any condition. The uniqueness of the product stems from the complementarity between radar and IMU sensors, enhanced athletes timing analysis, all enabled through an innovative
algorithm developed in the AGNOSTIC ERC Advanced grant (ID 742648).
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