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Hongjun Choi
Deep learningActivity recognitionComputer scienceFeature extractionAccelerometer
2Publications
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Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and computer vision applications. Key bottlenecks to their large scale adoption are computational expenditure and difficulty in incorporating them in a differentiable architecture. We take an important step in this paper to mitigate these bottlenecks by proposing a novel one-step approach to generate PIs directly from the i...
Jun 1, 2018 in CVPR (Computer Vision and Pattern Recognition)
#1Hongjun ChoiH-Index: 1
#2Qiao Wang (ASU: Arizona State University)H-Index: 3
Last. Anuj Srivastava (FSU: Florida State University)H-Index: 44
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Activity recognition has been receiving significant attention from a variety of research areas such as human performance enhancement, health promotion, and human computer interaction. However, recognizing activities from accelerometer data still remains a challenging problem due to sensitivity to sampling rates, misalignment of data, and increased variability in activities among clinically relevant populations. In order to solve these issues, we adopt methods from functional analysis, which cons...
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