Lameness scoring is a routine procedure in dairy industry to screen the herds for new cases of lameness. Subjective lameness scoring, which is the most popular lameness detection and screening method in dairy herds, has several limitations. They include low intra‐observer and inter‐observer agreement and the discrete nature of the scores which limits its usage in monitoring the lameness. The aim of this study is to develop an automated lameness scoring system comparable with conventional subjective lameness scoring by means of artificial neural networks. The system is composed of four balanced force plates installed in a hoof‐trimming box. A group of 105 dairy cows was used for the study. Twenty‐three features extracted from ground reaction force (GRF) data were used in a computer training process which was performed on 60 per cent of the data. The remaining 40 per cent of the data were used to test the trained system. Repeatability of the lameness scoring system was determined by GRF samples from 25 cows, captured at two different times from the same animals. The mean sd was 0.31 and the mean coefficient of variation was 14.55 per cent, which represents a high repeatability in comparison with subjective vision‐based scoring methods. Although the highest sensitivity and specificity values were seen in locomotion score groups 1 and 4, the automatic lameness system was both sensitive and specific in all groups. The sensitivity and specificity were higher than 72 per cent in locomotion score groups 1 to 4, and it was 100 per cent specific and 50 per cent sensitive for group 5.

Objective . In this paper, we propose a nonlinear dimensionality reduction algorithm for the manifold of symmetric positive definite (SPD) matrices that considers the geometry of SPD matrices and provides a low-dimensional representation of the manifold with high class discrimination in a supervised or unsupervised manner. Approach . The proposed algorithm tries to preserve the local structure of the data by preserving distances to local means (DPLM) and also provides an implicit projection matrix. DPLM is linear in terms of the number of training samples. Main results . We performed several experiments on the multi-class dataset IIa from BCI competition IV and two other datasets from BCI competition III including datasets IIIa and IVa. The results show that our approach as dimensionality reduction technique—leads to superior results in comparison with other competitors in the related literature because of its robustness against outliers and the way it preserves the local geometry of the data. Significance . The experiments confirm that the combination of DPLM with filter geodesic minimum distance to mean as the classifier leads to superior performance compared with the state of the art on brain–computer interface competition IV dataset IIa. Also the statistical analysis shows that our dimensionality reduction method performs significantly better than its competitors.