Video of nematodes/roundworms was analyzed using persistent homology to study locomotion and behavior. In each frame, URLanism's body posture was represented by a high-dimensional vector. By concatenating points in fixed-duration segments of this time series, we created a sliding window embedding (sometimes called a time delay embedding)where each point corresponds to a sequence of postures of URLanism. Persistent homology on the points in this time series detected behaviors and comparisons of these persistent homologycomputations detected variation in their corresponding behaviors. We used average persistence landscapes and machine learning techniques to study changes in locomotion and behavior in varying environments .
We apply topological data analysis to video of C. elegans, a widely-studied URLanism in biology. We demonstrate that each of the bars in the resulting barcode may be visualized as a synthetic video of a characteristic behavior. Our main goal is to develop a quantitative summary of complex behavior that may be applied to high-throughput data. Our method is able to distinguish and classify videos from various environmental conditions. We use sliding window embeddings (time delay embeddings), persistent homology, average persistence landscapes , and support vector machines .
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Video of nematodes/roundworms was analyzed using persistent homology to study locomotion and behavior. In each frame, URLanism's body posture was represented by a high-dimensional vector. By concatenating points in fixed-duration segments of this time series, we created a sliding window embedding (sometimes called a time delay embedding)where each point corresponds to a sequence of postures of URLanism. Persistent homology on the points in this time series detected behaviors and comparisons of these persistent homologycomputations detected variation in their corresponding behaviors. We used
We apply topological data analysis to video of C. elegans, a widely-studied URLanism in biology. We demonstrate that each of the bars in the resulting barcode may be visualized as a synthetic video of a characteristic behavior. Our main goal is to develop a quantitative summary of complex behavior that may be applied to high-throughput data. Our method is able to distinguish and classify videos from various environmental conditions. We use sliding window embeddings (time delay embeddings), persistent homology,
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and machine learning techniques to study changes in locomotion and behavior in varying environments
, and support vector machines