Emotion Recognition on Edge Devices: Training and Deployment

Emotion recognition, among other natural language processing tasks, has greatly benefited from the use of large transformer models. Deploying these models on resource-constrained devices, however, is a major challenge due to their computational cost. In this paper, we show that the combination of large transformers, as high-quality feature extractors, and simple hardware-friendly classifiers based on linear separators can achieve competitive performance while allowing real-time inference and fast training. Various solutions including batch and Online Sequential Learning are analyzed. Additionally, our experiments show that latency and performance can be further improved via dimensionality reduction and pre-training, respectively. The resulting system is implemented on two types of edge device, namely an edge accelerator and two smartphones.

APPLICATION OF REGULARIZED ONLINE SEQUENTIAL LEARNING FOR HEMATOCRIT ESTIMATION

Hematocrit (HCT) is expressed as the percentage of red blood cells in the whole blood, it is one of the most highly affecting factors which influences the glucose measurement by using handheld device. In this paper, we present an approach for applying the regularized online sequential learning to hematocrit estimation. The input is the transduced current curve which is produced by the chemical reaction during glucose measurement. The experimental results shown that the proposed approach is promising.