The common practice of manual synchronization of body- worn, logging accelerometers and video cameras is imprac- tical for integration into everyday practice for applications such as real-world behavior analysis. We significantly ex- tend an existing technique for automatic cross-modal syn- chronization and evaluate its performance in a realistic ex- perimental setting. Distinctive gestures, captured by the camera, are matched with the recorded acceleration sig- nal(s) using cross-correlation based time-delay estimation. PCA-based data pre-processing makes the procedure ro- bust against orientation mismatches between the marking gesture and the camera plane. We evaluated five different marker gestures and report very promising results for ac- tual use.
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