A Reliable and Reconfigurable Signal Processing Framework for Estimation of Metabolic Equivalent of Task in Wearable Sensors

Wearable motion sensors are widely used to estimate metabolic equivalent of task (MET) values associated with physical activities. However, one major obstacle in widespread adoption of current wearables is that any changes in configuration of the network requires new data collection and re-training of the underlying signal processing algorithms. For any wearable-based MET estimation framework to be considered a viable platform, it needs to be reconfigurable, reliable, and power-efficient. In this paper, we aim to address the issues of sensor misplacement, power efficiency, and new sensor addition and propose a reliable and reconfigurable MET estimation framework. We introduce a power-aware sensor localization approach that allows users to wear the sensors on different body locations without need for adhering to a specific installation protocol. Furthermore, we propose a novel transductive transfer learning approach, which gives end-users the ability to add new sensors to the network without need for collecting new training data. This is accomplished by transferring the knowledge of already trained sensors to the untrained sensors in real-time. Our experiments demonstrate that our sensor localization algorithm achieves an accuracy of 90.8% in detecting location of the wearable sensors. The integrated model of sensor localization and MET calculation achieves an R² of 0.8 in estimating MET values using a regression-based model. Furthermore, our transfer learning algorithm improves the R² value of MET estimation up to 60%.