Time Series Classification on Edge with Lightweight Attention Networks
Shalini Mukhopadhyay, S. Dey, Arijit Mukherjee, Arpan Pal, S. S. Ashwin · 2024
In recent years, wearable devices and sensing systems have become an integral part of the deployment of Artificial Intelligence (AI) on Edge. These intelligent edge devices perform first-level analytics to reduce data transfer, improve response latency, and preserve privacy. Thus, there is a growing focus on the design of machine learning models with high accuracy, and low resource footprint, suitable for a wide range of applications in the domains of healthcare, wellness, and lifestyle, among others. A majority of these tasks comprise the classification of time series signals collected from physiological and inertial sensors on commercially available wearable devices. A suitable solution for time series classification on edge would allow the use of multiple signal modalities on a broad spectrum of constrained devices. Unfortunately, the time series analytics for resource-limited platforms have not reached the same level of maturity as its full-fledged counterpart. For instance, attention-based networks, which form the state-of-the-art for standard time series classification, have no well-performing, generalized implementation for resource-constrained platforms. Towards this, we present a new architecture suited for time series classification using specialized lightweight attention-like blocks. These blocks, inspired by the attention mechanism and attention condensers, effectively learn time series features. We demonstrate the efficiency of the proposed model design and proof-of-concept implementation on a Human Activity Recognition dataset. Moreover, the generalizability of this architecture is highlighted by the reasonably good classification accuracy on various datasets from the UCR Time Series Classification Archive.