![]() ![]() ![]() Missing marker data could then be reconstructed through a series of coordinate transformations. Two principal component analyses were used to determine how the coordinates of a marker with gaps correlated with the coordinates of the other, gap-free markers. ![]() The underlying idea of the proposed algorithm was that a multitude of internal and external constraints govern human motion and lead to a highly subject-specific movement pattern in which all motion variables are intercorrelated in a specific way. The current paper proposes a conceptually new gap filling algorithm and presents results from a proof-of-principle analysis. Missing marker information caused by occlusions or a marker falling off is a common problem impairing data quality. Marker-based human motion analysis is an important tool in clinical research and in many practical applications. ![]()
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