Attention-based adaptive sampling for continuous EMG data streams - Schiboni, Giovanni; Suarez Mora, Juan Carlos; Zhang, Rui; Amft, Oliver (2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2019)
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 free-living 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%.
CRIS-Team Scopus Importer:2024-12-16
Keywords: Automatic dietary monitoring; Energy saving; Pattern spotting

Dyndse: Automated multi-objective design space exploration for context-adaptivewearable IOT edge devices - Schiboni, Giovanni; Suarez Mora, Juan Carlos; Zhang, Rui; Amft, Oliver (Sensors, 2020)
We describe a simulation-based Design Space Exploration procedure (DynDSE) for wearable IoT edge devices that retrieve events from streaming sensor data using context-adaptive pattern recognition algorithms. We provide a formal characterisation of the design space, given a set of system functionalities, components and their parameters. An iterative search evaluates configurations according to a set of requirements in simulations with actual sensor data. The inherent trade-offs embedded in conflicting metrics are explored to find an optimal configuration given the application-specific conditions. Our metrics include retrieval performance, execution time, energy consumption, memory demand, and communication latency. We report a case study for the design of electromyographic-monitoring eyeglasses with applications in automatic dietary monitoring. The design space included two spotting algorithms, and two sampling algorithms, intended for real-time execution on three microcontrollers. DynDSE yielded configurations that balance retrieval performance and resource consumption with an F1 score above 80% at an energy consumption that was 70% below the default, non-optimised configuration. We expect that the DynDSE approach can be applied to find suitable wearable IoT system designs in a variety of sensor-based applications.
CRIS-Team Scopus Importer:2020-11-13
Keywords: Automatic dietary monitoring; Embedded machine learning; Energy saving; Health monitoring; Pattern spotting; Physiological sensing