Attention-Based Adaptive Sampling for Continuous EMG-Data Streams

Publication Type Conference Paper
Authors Giovanni Schiboni, Juan Carlos Suarez, Rui Zhang, Oliver Amft
Title Attention-Based Adaptive Sampling for Continuous EMG-Data Streams
Abstract This paper presents an online attention-based adaptive sampling approach for EMG data streams. Our sampling strategy is based on dynamically tuning the duty cycle of an EMG monitoring and recognition system. A response model was developed to adjust the EMG system’s sampling rate depending on the signal pattern. The response model was implemented by two sampling rate states. We report a case study of an eyeglasses diet monitoring system that implements the adaptive sampling strategy to monitor the Temporalis muscle activity. We show that the adaptive sampling approach can reduce energy consumption in a freeliving study dataset with ten participants. Compared to a static uniform sampling, our approach yields an energy saving on 70%, while recognition performance remained above 80%.
Date 2019
Proceedings Title 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation
Conference Name IEEE International Conference on Ubiquitous Intelligence and Computing (UIC)
Place Leicester (UK)
Publisher IEEE
Pages 1178-1183
DOI 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00219
Full Text PDF
Friedrich-Alexander-Universität Erlangen-Nürnberg