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When a high power output is required in cycling, a spontaneous transition by the cyclist from a seated to a standing position generally occurs. In this study, by varying the cadence and cyclist bodyweight, we tested whether the transition is better explained by the greater power economy of a standing position or by the emergence of mechanical constraints that force cyclists to stand. Ten males participated in five experimental sessions corresponding to different bodyweights (80%, 100%, or 120%) and cadences (50 RPM, 70 RPM, or 90 RPM). In each session, we first determined the seat-to-stand transition power (SSTP) in an incremental test. The participants then cycled at 20%, 40%, 60%, 80%, 100%, or 120% of the SSTP in the seated and standing positions, for which we recorded the saddle forces and electromyogram (EMG) signals of eight lower limb muscles. We estimated the cycling cost using an EMG cost function (ECF) and the minimal saddle forces in the seated position as an indicator of the mechanical constraints. Our results show the SSTP to vary with respect to both cadence and bodyweight. The ECF was lower in the standing position above the SSTP value (i.e., at 120%) in all experimental sessions. The minimal saddle forces varied significantly with respect to both cadence and bodyweight. These results suggest that optimization of the muscular cost function, rather than mechanical constraints, explain the seat-to-stand transition in cycling. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces k′-means algorithm that performs correct clustering without pre-assigning the exact number of clusters. This is achieved by minimizing a suggested cost-function. The cost-function extends the mean-square-error cost-function of k-means. The algorithm consists of two separate steps. The first is a pre-processing procedure that performs initial clustering and assigns at least one seed point to each cluster. During the second step, the seed-points are adjusted to minimize the cost-function. The algorithm automatically penalizes any possible winning chances for all rival seed-points in subsequent iterations. When the cost-function reaches a global minimum, the correct number of clusters is determined and the remaining seed points are located near the centres of actual clusters. The simulated experiments described in this paper confirm good performance of the proposed algorithm. [Copyright &y& Elsevier]