Hierarchical Temporal Convolution Network: Towards Privacy-Centric Activity Recognition

In response to the healthcare issues associated with the ageing population, various ambient assisted living technologies are being developed. To mitigate privacy concerns related to cloud-based data processing, recent methods have shifted towards using edge devices for local data processing. Despite their perceived benefits, the limited computational resources of these edge devices present a significant challenge for real-time performance, which is often an imperative requirement. However, recent computer vision-based methods for recognising activities of daily living for the elderly suffer from increased computational complexity when capturing multi-scale temporal context that is essential for accurate activity recognition. This paper proposes HT-ConvNet (Hierarchical Temporal Convolution Network) for activity recognition to capture multi-scale temporal information without increasing computational complexity. HT-ConvNet employs exponentially increasing receptive fields across successive convolution layers to enable efficient hierarchical extraction of temporal features. Furthermore, HT-ConvNet provides an adaptive weighting mechanism to emphasise the most important features. Experimental results show that the multi-scale temporal feature extraction and the feature-weighted fusion mechanisms outperform existing methods in enhancing accuracy without increasing model complexity.