no code implementations • 26 Aug 2022 • Setareh Rahimi Taghanaki, Michael Rainbow, Ali Etemad
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.
no code implementations • 1 Sep 2021 • Setareh Rahimi Taghanaki, Michael Rainbow, Ali Etemad
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.
no code implementations • 21 Oct 2020 • Setareh Rahimi Taghanaki, Michael Rainbow, Ali Etemad
We propose the use of self-supervised learning for human activity recognition with smartphone accelerometer data.