Deep ConvLSTM with self-attention for human activity decoding using wearables
This work addresses the need for accurate human activity decoding in healthcare and context awareness applications, representing an incremental advancement by integrating self-attention into existing deep network architectures.
The paper tackles the problem of decoding human activity from wearable sensor data by proposing a deep neural network with self-attention to capture spatio-temporal features and select important time points, resulting in statistically significant performance improvements over state-of-the-art methods on six public datasets.
Decoding human activity accurately from wearable sensors can aid in applications related to healthcare and context awareness. The present approaches in this domain use recurrent and/or convolutional models to capture the spatio-temporal features from time-series data from multiple sensors. We propose a deep neural network architecture that not only captures the spatio-temporal features of multiple sensor time-series data but also selects, learns important time points by utilizing a self-attention mechanism. We show the validity of the proposed approach across different data sampling strategies on six public datasets and demonstrate that the self-attention mechanism gave a significant improvement in performance over deep networks using a combination of recurrent and convolution networks. We also show that the proposed approach gave a statistically significant performance enhancement over previous state-of-the-art methods for the tested datasets. The proposed methods open avenues for better decoding of human activity from multiple body sensors over extended periods of time. The code implementation for the proposed model is available at https://github.com/isukrit/encodingHumanActivity.