Body bio-impedance is a unique parameter to monitor changes in body composition non-invasively. Continuous measurement of bio-impedance can track changes in body fluid content and cell mass and has widespread applications for physiological monitoring. State-of-the-art implementation of bio-impedance sensor devices is still limited for continuous use, in part, due to artefacts arising at the skin-electrode (SE) interface. Artefacts at the SE interface may arise due to various factors such as motion, applied pressure on the electrode surface, changes in ambient conditions or gradual drying of electrodes. This paper presents a novel bio-impedance sensor node that includes an artefact aware method for bio-impedance measurement. The sensor node enables autonomous and continuous measurement of bio-impedance and SE contact impedance at ten frequencies between 10 kHz to 100 kHz to detect artefacts at the SE interface. Experimental evaluation with SE contact impedance models using passive 2R1C electronic circuits and also with non-invasive in vivo measurements of SE contact impedance demonstrated high accuracy (with maximum error less than 1.5%) and precision of 0.6 Ω. The ability to detect artefacts caused by motion, vertically applied pressure and skin temperature changes was analysed in proof of concept experiments. Low power sensor node design achieved with 50mW in active mode and only 143 μW in sleep mode estimated a battery life of 90 days with a 250 mAh battery and duty-cycling impedance measurements every 60 seconds. Our method for artefact aware bio-impedance sensing is a step towards autonomous and unobtrusive continuous bio-impedance measurement for health monitoring at-home or in clinical environments.
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http://dx.doi.org/10.1109/TBCAS.2020.3021186 | DOI Listing |
ISA Trans
February 2025
School of Automation and Electrical Engineering, Linyi University, Linyi, 276005, China. Electronic address:
The current work presents a distributed estimation approach with a topology-switching structure and introduces an adaptive self-triggered strategy (ASTS) to minimize energy consumption during inter-node communication. In the filter design, the network's communication topology is modeled as a time-varying process, with switching governed by a homogeneous Markov chain and a probabilistic transition matrix containing partially unknown data. Filter design feasibility is verified using Lyapunov stability theory and linear matrix inequality (LMI) method, which are used to determine the filter parameters.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2024
The Internet of Bodies is a body-centric network that connects smart devices to enable real-time monitoring of physiological data, including early detection of cardiovascular diseases through electrocardiograms (ECG) monitoring. In contrast to power-intensive RF transceivers in many ECG wearables, Human Body Communication (HBC) offers an energy-efficient (EE) and secure alternative, utilizing the body as a communication channel. This paper explores the functionality of an IoB sensor node designed for ECG monitoring, leveraging HBC and incorporating energy harvesting techniques to ensure sustained power.
View Article and Find Full Text PDFJ Econ Entomol
February 2025
College of Engineering, University of Georgia, Tifton, GA, USA.
Plant-specific insect scouting and prediction are still challenging in most crop systems. In this article, a machine-learning algorithm is proposed to predict populations during whiteflies (Bemisia tabaci, Hemiptera; Gennadius Aleyrodidae) scouting and aid in determining the population distribution of adult whiteflies in cotton plant canopies. The study investigated the main location of adult whiteflies relative to plant nodes (stem points where leaves or branches emerge), population variation within and between canopies, whitefly density variability across fields, the impact of dense nodes on overall canopy populations, and the feasibility of using machine learning for prediction.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2024
This paper introduces an energy-efficient code-division multiple access (CDMA) architecture for wearable devices for the Internet of Bodies (IoB), which is an emerging data communication framework for connected sensor nodes around the human body. To address the challenge of simultaneous data transmission from multiple transmitters, which often leads to interference, we propose the adoption of standard-basis based CDMA encoding and decoding, which has higher energy efficiencies than the traditional Walsh code-based implementation. We also present a novel architecture for body-worn sensor nodes and data aggregators, where both the sensor nodes and the aggregator possess transceiver functionalities for (1) transmitting data from the sensors to the aggregator, and (2) sending clock synchronization information from the aggregator to the nodes.
View Article and Find Full Text PDFWe propose a dynamic sensor selection approach for deep neural networks (DNNs), which is able to derive an optimal sensor subset selection for each specific input sample instead of a fixed selection for the entire dataset. This dynamic selection is jointly learned with the task model in an end-to-end way, using the Gumbel-Softmax trick to allow the discrete decisions to be learned through standard backpropagation. We then show how we can use this dynamic selection to increase the lifetime of a wireless sensor network (WSN) by imposing constraints on how often each node is allowed to transmit.
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