  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. We experiment
with both categorical cross entropy loss and triplet loss for training the
embedding, and describe a novel triplet loss function based on subject
triplets. We evaluate these methods on three publicly available inertial human
activity recognition data sets (MHEALTH, WISDM, and SPAR) comparing
classification accuracy, out-of-distribution activity detection, and embedding
generalization to new activities. The novel subject triplet loss provides the
best performance overall, and all personalized deep embeddings out-perform our
baseline personalized engineered feature embedding and an impersonal fully
convolutional neural network classifier.
