Practical Insights on Incremental Learning of New Human Physical Activity on the Edge
This work addresses incremental learning for human physical activity recognition on edge devices, which is an incremental contribution to the domain of Edge ML.
The paper tackled the challenges of Edge Machine Learning, such as constrained data storage, limited computational power, and the number of learning classes, by conducting experiments with the MAGNETO system on human activity data, providing practical insights without specific numerical results.
Edge Machine Learning (Edge ML), which shifts computational intelligence from cloud-based systems to edge devices, is attracting significant interest due to its evident benefits including reduced latency, enhanced data privacy, and decreased connectivity reliance. While these advantages are compelling, they introduce unique challenges absent in traditional cloud-based approaches. In this paper, we delve into the intricacies of Edge-based learning, examining the interdependencies among: (i) constrained data storage on Edge devices, (ii) limited computational power for training, and (iii) the number of learning classes. Through experiments conducted using our MAGNETO system, that focused on learning human activities via data collected from mobile sensors, we highlight these challenges and offer valuable perspectives on Edge ML.