NAS

Real-time monitoring of human activities using wearable devices often requires the deployment of machine learning models on resource-constrained edge devices. Stateof- the-art Human Activity Recognition neural network models, while effective, already suffer from excessive size and complexity. Furthermore, our systematic analysis reveals that, even worse, the computational cost and model size of most SOTA HAR models escalate significantly with an increase in sensor channels. In this work, we propose an integrated neural architecture search framework to further lighten HAR models. To counteract the negative effects of scaling at deployment time, the proposed framework simultaneously selects and reduces the number of sensor channels, prunes filters, and decreases the temporal dimensions while training the model on optimized hardware.

An essential task in predictive maintenance is the prediction of the Remaining Useful Life (RUL) through the analysis of multivariate time series. Using the sliding window method, Convolutional Neural Network (CNN) and conventional Recurrent Neural Network (RNN) approaches have produced impressive results on this matter, due to their ability to learn optimized features. To exploit the multi-level temporal information, many approaches are proposed which combine CNN and RNN models. In this work, we propose a new LSTM variant called embedded convolutional LSTM (ECLSTM).