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HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data
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2022-09-01
[PDF-632.69 KB]
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Edition:Final Report
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Abstract:The objective of this work is to propose a new method for predicting correct labels of unclassified or partially classified activities. To this end, we propose HAR-GCCN, a deep graph CNN model that leverages the correlation between chronologically adjacent sensor measurements to predict the correct labels for unclassified activities that have at least one activity label. We propose a new training strategy to ensure that the model predicts missing activity labels by leveraging the known ones. HAR-GCCN shows superior performance relative to previously used baseline methods, improving classification accuracy by about 25% and up to 68% on different standard datasets, including the PAMAP dataset.
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