  Human activity recognition (HAR) through wearable devices has received much
interest due to its numerous applications in fitness tracking, wellness
screening, and supported living. As a result, we have seen a great deal of work
in this field. Traditional deep learning (DL) has set a state of the art
performance for HAR domain. However, it ignores the data's structure and the
association between consecutive time stamps. To address this constraint, we
offer an approach based on Graph Neural Networks (GNNs) for structuring the
input representation and exploiting the relations among the samples. However,
even when using a simple graph convolution network to eliminate this shortage,
there are still several limiting factors, such as inter-class activities
issues, skewed class distribution, and a lack of consideration for sensor data
priority, all of which harm the HAR model's performance. To improve the current
HAR model's performance, we investigate novel possibilities within the
framework of graph structure to achieve highly discriminated and rich activity
features. We propose a model for (1) time-series-graph module that converts raw
data from HAR dataset into graphs; (2) Graph Convolutional Neural Networks
(GCNs) to discover local dependencies and correlations between neighboring
nodes; and (3) self-attention GNN encoder to identify sensors interactions and
data priorities. To the best of our knowledge, this is the first work for HAR,
which introduces a GNN-based approach that incorporates both the GCN and the
attention mechanism. By employing a uniform evaluation method, our framework
significantly improves the performance on hospital patient's activities dataset
comparatively considered other state of the art baseline methods.
