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Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning
We aim to develop a model that learns strong representations from accelerometer signals, in order to perform robust human activity classification, while reducing the model's reliance on class labels.
Wearable-based Classification of Running Styles with Deep Learning
To develop a system capable of classifying running styles using wearables, we collect a dataset from 10 healthy runners performing 8 different pre-defined running styles.
Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion
We propose the use of self-supervised learning for human activity recognition with smartphone accelerometer data.
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