An Energy-Efficient IoT node for HMI applications based on an ultra-low power Multicore Processor

Developing wearable sensing technologies and unobtrusive devices is paving the way to the design of compelling applications for the next generation of systems for a smart IoT node for Human Machine Interaction (HMI). In this paper we present a smart sensor node for IoT and HMI based on a programmable Parallel Ultra-Low-Power (PULP) platform. We tested the system on a hand gesture recognition application, which is a preferred way of interaction in HMI design. A wearable armband with 8 EMG sensors is controlled by our IoT node, running a machine learning algorithm in real-time, recognizing up to 11 gestures with a power envelope of 11.84 mW. As a result, the proposed approach is capable to 35 hours of continuous operation and 1000 hours in standby. The resulting platform minimizes effectively the power required to run the software application and thus, it allows more power budget for high-quality AFE

Using Low-Power, Low-Cost IoT Processors in Clinical Biosignal Research: An In-depth Feasibility Check

Research on biosignal (ExG) analysis is usually performed with expensive systems requiring connection with external computers for data processing. Consumer-grade low-cost wearable systems for bio-potential monitoring and embedded processing have been presented recently, but are not considered suitable for medical-grade analyses. This work presents a detailed quantitative comparative analysis of a recently presented fully-wearable low-power and low-cost platform (BioWolf) for ExG acquisition and embedded processing with two researchgrade acquisition systems, namely, ANTNeuro (EEG) and the Noraxon DTS (EMG). Our preliminary results demonstrate that BioWolf offers competitive performance in terms of electrical properties and classification accuracy. This paper also highlights distinctive features of BioWolf, such as real-time embedded processing, improved wearability, and energy-efficiency, which allows devising new types of experiments and usage scenarios for medical-grade biosignal processing in research and future clinical studies

An Investigation of Single-Core and Multi-Core Computing Methods for Biosignal Processing

This paper provides a single-core and multi-core processor design for applications involving highly parallel processing and sluggish biosignal events in health surveillance systems. An instruction memory (IM), a data memory (DM), and a processor core (PC) make up the single-core design. In contrast, the multi-core architecture is made up of PCs, separate IMs for each core, a shared DM, and an interconnection cross-bar connecting  the cores and the DM. The power vs. performance compromises for a multi-lead ECG signal conditioning application that takes advantage of near threshold computing are evaluated between both designs. According to the findings, the multi-core system uses 10.4% more power for low processing demands (681 kOps/s) and 66% less power for high processing needs (50.1 MOps/s).   &nbsp

An Energy-Efficient IoT node for HMI applications based on an ultra-low power Multicore Processor

Developing wearable sensing technologies and unobtrusive devices is paving the way to the design of compelling applications for the next generation of systems for a smart IoT node for Human Machine Interaction (HMI). In this paper we present a smart sensor node for IoT and HMI based on a programmable Parallel Ultra-Low-Power (PULP) platform. We tested the system on a hand gesture recognition application, which is a preferred way of interaction in HMI design. A wearable armband with 8 EMG sensors is controlled by our IoT node, running a machine learning algorithm in real-time, recognizing up to 11 gestures with a power envelope of 11.84 mW. As a result, the proposed approach is capable to 35 hours of continuous operation and 1000 hours in standby. The resulting platform minimizes effectively the power required to run the software application and thus, it allows more power budget for high-quality AFE