Dataset from spirometer and sEMG wireless sensor for diaphragmatic respiratory activity monitoring

none7noWe introduce a dataset to provide insights into the relationship between the diaphragm surface electromyographic (sEMG) signal and the respiratory air flow. The data presented had been originally collected for a research project jointly developed by the Department of Information Engineering and the Department of Industrial Enginering and Mathematical Sciences, Polytechnic University of Marche, Ancona, Italy. This article describes data recorded from 8 subjects, and includes 8 air flow and 8 surface electromyographic (sEMG) signals for diaphragmatic respiratory activity monitoring, measured with a sampling frequency of 2 kHz.openBiagetti, Giorgio; Carnielli, Virgilio Paolo; Crippa, Paolo; Falaschetti, Laura; Scacchia, Valentina; Scalise, Lorenzo; Turchetti, ClaudioBiagetti, Giorgio; Carnielli, Virgilio Paolo; Crippa, Paolo; Falaschetti, Laura; Scacchia, Valentina; Scalise, Lorenzo; Turchetti, Claudi

Low Latency Protocols Investigation for Event-Driven Wireless Body Area Networks

Nowadays distributed electronic health and fitness monitoring are hot-topics in bio-engineering, however common solutions for Wireless Body Area Networks (WBANs) featuring high-density sampled data transmission still stumbles over the trade-off among data rate, application throughput, and latency. Therefore, the Bluetooth Low Energy (BLE) and the IEEE 802.15.4 protocols are here investigated, with the aim of developing an event-driven WBAN to support a threshold-crossing surface ElectroMyoGraphy (sEMG) acquisition approach. We then implemented a custom protocol to overcome their limitations and fulfil all the requirements, resulting in a transmission latency of 0.856 ms ± 1 µs and enabling a functional operating time up to 110 h

ECG-Based Arrhythmia Classification using Recurrent Neural Networks in Embedded Systems

Cardiac arrhythmia is one of the most important cardiovascular diseases (CVDs), causing million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently and as such requires the analysis of large amount of data, collected during the daily life of patients. An important tool for CVD diagnosis is the analysis of electrocardiogram (ECG), because of its non-invasive nature and simplicity of acquisition. In this work we propose a classification algorithm for arrhythmia based on recurrent neural networks (RNNs) that operate directly on ECG data, exploring the effectiveness and efficiency of several variations of the general RNN, in particular using different types of layers implementing the network memory. We use the MIT-BIH arrhythmia database and the evaluation protocol recommended by the Association for the Advancement of Medical Instrumentation (AAMI). After designing and testing the effectiveness of the different networks, we then test its porting to an embedded platform, namely the STM32 microcontroller architecture from ST, using a specific framework to port a pre-built RNN to the embedded hardware, convert it to optimized code for the platform and evaluate its performance in terms of resource usage. Both in binary and multiclass classification, the basic RNN model outperforms the other architectures in terms of memory storage (∼117 KB), number of parameters (∼5 k) and inference time (∼150 ms), while the RNN LSTM-based achieved the best accuracy (∼90%)

A ZigBee-based wireless biomedical sensor network as a precursor to an in-suit system for monitoring astronaut state of health

Master of ScienceDepartment of Electrical and Computer EngineeringSteven WarrenNetworks of low-power, in-suit, wired and wireless health sensors offer the potential to track and predict the health of astronauts engaged in extra-vehicular and in-station activities in zero- or reduced- gravity environments. Fundamental research questions exist regarding (a) types and form factors of biomedical sensors best suited for these applications, (b) optimal ways to render wired/wireless on-body networks with the objective to draw little-to-no power, and (c) means to address the wireless transmission challenges offered by a spacesuit constructed from layers of aluminized mylar. This thesis addresses elements of these research questions through the implementation of a collection of ZigBee-based wireless health monitoring devices that can potentially be integrated into a spacesuit, thereby providing continuous information regarding astronaut fatigue and state of health. Wearable biomedical devices investigated for this effort include electrocardiographs, electromyographs, pulse oximeters, inductive plethysmographs, and accelerometers/gyrometers. These ZigBee-enabled sensors will form the nodes of an in-suit ZigBee Pro network that will be used to (1) establish throughput requirements for a functional in-suit network and (2) serve as a performance baseline for future devices that employ ultra-low-power field-programmable gate arrays and micro-transceivers. Sensor devices will upload data to a ZigBee network coordinator that has the form of a pluggable USB connector. Data are currently visualized using MATLAB and LabVIEW

Stand-alone wearable system for ubiquitous real-time monitoring of muscle activation potentials

Wearable technology is attracting most attention in healthcare for the acquisition of physiological signals. We propose a stand-alone wearable surface ElectroMyoGraphy (sEMG) system for monitoring the muscle activity in real time. With respect to other wearable sEMG devices, the proposed system includes circuits for detecting the muscle activation potentials and it embeds the complete real-time data processing, without using any external device. The system is optimized with respect to power consumption, with a measured battery life that allows for monitoring the activity during the day. Thanks to its compactness and energy autonomy, it can be used outdoor and it provides a pathway to valuable diagnostic data sets for patients during their own day-life. Our system has performances that are comparable to state-of-art wired equipment in the detection of muscle contractions with the advantage of being wearable, compact, and ubiquitous

Reliable and energy efficient scheduling protocols for wireless body area networks (WBAN)

Wireless Body Area Network (WBAN) facilitates efficient and cost-effective e-health care and well-being applications. The WBAN has unique challenges and features compared to other Wireless Sensor Networks (WSN). In addition to battery power consumption, the vulnerability and the unpredicted channel behavior of the Medium Access Control (MAC) layer make channel access a serious problem.MAC protocols based on Time Division Multiple Access (TDMA) can improve the reliability and efficiency of WBAN. However, conventional static TDMA techniques adopted by IEEE 802.15.4 and IEEE 802.15.6 do not sufficiently consider the channel status or the buffer requirements of the nodes within heterogeneous contexts. Although there are some solutions that have been proposed to alleviate the effect of the deep fade in WBAN channel by adopting dynamic slot allocation, these solutions still suffer from some reliability and energy efficiency issues and they do not avoid channel deep fading.This thesis presents novel and generic TDMA based techniques to improve WBAN reliability and energy efficiency. The proposed techniques synchronise nodes adaptively whilst tackling their channel and buffer status in normal and emergency contexts. Extensive simulation experiments using various traffic rates and time slot lengths demonstrate that the proposed techniques improve the reliability and the energy efficiency compared to the de-facto standards of WBAN, i.e. the IEEE 802.15.4 and the IEEE 802.15.6. In normal situations, the proposed techniques reduce packet loss up to 61% and 68% compared to the IEEE 802.15.4 and IEEE 802.15.6 respectively. They also reduce energy consumption up to 7.3%. In emergencies, however, the proposed techniques reduce packets loss up to 63.4% and 90% with respect to their counterparts in IEEE 802.15.4 and 802.15.6. The achieved results confirm the significant enhancements made by the developed scheduling techniques to promote the reliability and energy efficiency of WBAN, opening up promising doors towards new horizons and applications

Vitals Recorder : sistema móvel para apoiar a realização de estudos de psicofisiologia

A realização de estudos experimentais com recolha de dados fisiológicos dos participantes, obriga frequentemente os investigadores a usar equipamentos “fechados”, com poucas possibilidades de sair do laboratório ou adaptar os protocolos de recolha. O objetivo deste trabalho consiste no desenvolvimento de um sistema composto maioritariamente por aplicações móveis, para apoiar a recolha e agregação de dados fisiológicos, usando dispositivos acessíveis. Para além de permitir a utilização de um leque extensível de sensores de recolha, a solução deverá permitir aos investigadores monitorizar as experiências em curso, especialmente as recolhas realizadas em grupo, com vários participantes. O sistema desenvolvido, Vitals Recorder, inclui dois módulos principais: uma aplicação Android para a recolha dos dados fisiológicos, que corre num smartphone associado a um participante (VR-Unit); uma aplicação de monitorização, que corre num tablet associado ao investigador (VR-Remote). Neste módulo, o investigador pode gerir o grupo de participantes, marcar eventos de interesse e inspecionar, em tempo real, os dados dos vários participantes. Os dados das experiências são consolidados num backend, que permite a exportação e pré-visualização na web. As aplicações desenvolvidas foram utilizadas em estudos-piloto de psicofisiologia, com sessões de grupos, recolhendo ECG, EDA, Áudio e eventos marcados pelo utilizador. A solução desenvolvida permite uma fácil extensão para incluir novos sensores, e facilita diferentes tipos de protocolos nos estudos de psicologia (individuais, grupos). Para além disso, pode ser usada em novos domínios, como a aquisição de dados fisiológicos de bombeiros no terreno ou desportistas praticantes de fitness.The acquisition of physiological data collection in experimental research studies is often dependent on proprietary, closed equipment, with little chance for out of the lab observation or adaption the acquisition protocols. The objective of this work is to develop a system composed mainly by mobile components to support the collection and aggregation of physiological data using accessible devices. The solution should make it easy to extend the array of supported sensors and monitor ongoing group experiments with several participants. The developed system, Vitals Recorder, includes two main modules: an Android application for the collection of physiological data, running on a smartphone associated with an individual (VR-Unit); a monitoring application that runs on a tablet associated with the coordinating researcher (VR-Remote). In this module, the researcher can manage the group of participants, mark events of interest and inspect, in real time, the data of the various participants. Data from different sessions is consolidated in a backend, which allows previewing on the web and export to convenient formats. The developed applications were used in pilot studies of psychophysiology, with group sessions, collecting ECG, EDA, Audio and marked events from the researchers. The solution can be extended extend to incorporate new sensors and facilitates different types of protocols in psychology studies (individual, groups). In addition, it can be used in new domains, such as the acquisition of physiological data from firefighters on the field or fitness scenarios.Mestrado em Engenharia de Computadores e Telemátic