Self-Supervised Transformers for Activity Classification using Ambient Sensors

Ambient sensing facilitates non-intrusive data collection within sensitive environments, but also escalates the complexity in associated classification. In this paper, we propose a methodology based on Transformer Neural Networks to classify activities performed by a resident of an ambient sensor based environment. We also propose a methodology to pre-train Transformers in a self-supervised manner, as a hybrid autoencoder-classifier model instead of using contrastive loss. By having consistency in ambient data collection, the quality of data is considerably more reliable, presenting the opportunity to perform classification with enhanced accuracy. Therefore, in this research we look to find an optimal way of using deep learning to classify human activity with ambient sensor data.