Self-Aware Machine Learning for Multimodal Workload Monitoring During Manual Labor on Edge Wearable Sensors

The design of reliable wearable technologies for real-time and long-term monitoring presents a major challenge. Self-awareness is a promising solution that enables the system to monitor itself in interaction with the environment and to manage its resources more efficiently. In this work, we aim to utilize the notion of self-awareness to improve the battery life of edge wearable sensors for multimodal health and workload monitoring. Specifically, we consider cognitive workload detection during manual labor as a case study to illustrate the impact of our proposed technique in wearable technologies. Our multimodal machine-learning algorithm is able to detect cognitive workload during manual labor with a performance of 81.75%. By adopting the notion of self-awareness, we achieve an improvement of 27.6% in energy consumption, with less than 6% of performance loss.