Effects of mirror training on motor performance in healthy individuals: a systematic review and meta-analysis

ObjectiveMirror training (MTr) is a rehabilitation technique for patients with neurological diseases. There is no consensus on its effects on motor function in healthy individuals. This systematic review and meta-analysis considers the effects of MTr on motor function in healthy individuals.DesignThis is a systematic review and meta-analysis.Data sourcesWe searched six databases for studies assessing the effects of MTr on motor function in healthy individuals, published between January 1995 and December 2018. The Cochrane risk of bias was used to assess the quality of the studies. A meta-analysis was conducted with narrative synthesis.Eligibility criteria for selecting studiesEnglish-language randomised controlled trials reporting the behavioural results in healthy individuals were included.ResultsFourteen randomised controlled trials involving 538 healthy individuals were eligible. Two short-term studies showed MTr was inferior to passive vision pattern (standardised mean difference 0.57 (95% CI 0.06 to 1.08), I2=0%, p=0.03). The methods varied and there is limited evidence supporting the effectiveness of MTr compared with three alternative training patterns, with insufficient evidence to support analyses of age, skill level or hand dominance.ConclusionThe limited evidence that MTr affects motor performance in healthy individuals is weak and inconsistent among studies. It is unclear whether the effects of MTr on motor performance are more pronounced than the direct vision pattern, passive vision pattern or action observation. Further studies are needed to explore the short-term and long-term benefits of MTr and its effects on motor learning in healthy individuals.PROSPERO registration numberCRD42019128881.

Representations of Face Images and Collaborative Representation Classification for Face Recognition

Collaborative representation classification (CRC) was firstly proposed by Zhang et al. [L. Zhang, M. Yang, X. Feng, Y. Ma and D. Zhang, Collaborative Representation based Classification for Face Recognition, Computer Science, 2014]. It was an excellent algorithm for solving face recognition problems. The method suggests that the combination of all original training samples can approach the test samples accurately. But in fact, this does not mean it can well solve complex face recognition problems in some special situation, such as face recognition with varying illuminations and facial expressions. In the paper, we proposed an improvement to previous CRC method. By using a dedicated algorithm to combine the linear combinations of the original and their mirror training samples to represent the test samples, we can get more accurate recognition of test samples. The experimental results show that the proposed method does obtain notable accuracy improvement in comparison with the previous method.