Synchronised Video, Motion Capture and Force Plate Dataset for Validating Markerless Human Movement Analysis.

The BioCV dataset is a unique combination of synchronised multi-camera video, marker based optical motion capture, and force plate data, observing 15 healthy participants (7 males, 8 females) performing controlled and repeated motions (walking, running, jumping and hopping), as well as photogrammetry scan data for each participant. The dataset was created for the purposes of developing and validating the performance of computer vision based markerless motion capture systems with respect to marker based systems.

Automating Video-Based Two-Dimensional Motion Analysis in Sport? Implications for Gait Event Detection, Pose Estimation, and Performance Parameter Analysis.

Background: Two-dimensional (2D) video is a common tool used during sports training and competition to analyze movement. In these videos, biomechanists determine key events, annotate joint centers, and calculate spatial, temporal, and kinematic parameters to provide performance reports to coaches and athletes. Automatic tools relying on computer vision and artificial intelligence methods hold promise to reduce the need for time-consuming manual methods.

Label-free detection and profiling of individual solution-phase molecules.

Most chemistry and biology occurs in solution, in which conformational dynamics and complexation underlie behaviour and function. Single-molecule techniques are uniquely suited to resolving molecular diversity and new label-free approaches are reshaping the power of single-molecule measurements. A label-free single-molecule method capable of revealing details of molecular conformation in solution would allow a new microscopic perspective of unprecedented detail.