Personalized Activity Recognition with Deep Triplet Embeddings

15 Jan 2020David M. BurnsCari M. Whyne

A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data between individual users, resulting in very poor performance of impersonal algorithms for some subjects. We present an approach to personalized activity recognition based on deep embeddings derived from a fully convolutional neural network... (read more)

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