Purpose: The aim of this systematic review was to determine the number of muscle synergies and the distribution of muscle weightings in stroke patients during gait.
Material and Methods:This review is registered on PROSPERO (number: CRD42018088701) and is written following the PRISMA guidelines. A systematic search was conducted using following databases: PubMed, Web of Science, Naric, Cochrane and PEDro. Methodological quality was assessed by the Newcastle-Ottawa Scale and data extraction (subject characteristics, outcome measures and walking protocols) was performed by two independent researchers. The amount and structure of the muscle synergies were the two main outcome measures.
Results:In total, ten studies were included in this review. While four synergies are common in healthy controls, stroke patients often showed less synergies during gait. Synergies were determined by the number of muscles measured which varied greatly between studies. Only Tibialis Anterior, Soleus, Gastrocnemius and Rectus Femoris were assessed in all studies.
Conclusions:A consensus regarding the amount and composition of muscle synergies in stroke patients is difficult. The majority observed three to four muscle synergies. The decrease in amount of synergies can be explained by merging of synergies, often seen in hip/knee extensors with plantar flexors and hip/knee extensors with knee flexors.
The k-nearest neighbour (kNN) problem appears in many different fields of computer science, such as computer animation and robotics. In crowd simulation, kNN queries are typically used by a collision-avoidance method to prevent unnecessary computations. Many different methods for finding these neighbours exist, but it is unclear which will work best in crowd simulations, an application which is characterised by low dimensionality and frequent change of the data points. We therefore compare several data structures for performing kNN queries. We find that the nanoflann implementation of a k-d tree offers the best performance by far on many different scenarios, processing 100,000 agents in about 35 ms on a fast consumer PC.
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