Wireless body sensor networks for health-monitoring applications

This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01

A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors

Multimodal biometrics are promising for providing a strong security level for personal authentication, yet the implementation of a multimodal biometric system for practical usage need to meet such criteria that multimodal biometric signals should be easy to acquire but not easily compromised. We developed a wearable wrist band integrated with multispectral skin photomatrix (MSP) and electrocardiogram (ECG) sensors to improve the issues of collectability, performance and circumvention of multimodal biometric authentication. The band was designed to ensure collectability by sensing both MSP and ECG easily and to achieve high authentication performance with low computation, efficient memory usage, and relatively fast response. Acquisition of MSP and ECG using contact-based sensors could also prevent remote access to personal data. Personal authentication with multimodal biometrics using the integrated wearable wrist band was evaluated in 150 subjects and resulted in 0.2% equal error rate ( EER ) and 100% detection probability at 1% FAR (false acceptance rate) ( PD.1 ), which is comparable to other state-of-the-art multimodal biometrics. An additional investigation with a separate MSP sensor, which enhanced contact with the skin, along with ECG reached 0.1% EER and 100% PD.1 , showing a great potential of our in-house wearable band for practical applications. The results of this study demonstrate that our newly developed wearable wrist band may provide a reliable and easy-to-use multimodal biometric solution for personal authentication

Sensing with Earables: A Systematic Literature Review and Taxonomy of Phenomena

Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform

Heart rhythm assessment in elite endurance athletes: A better method?

BAKGRUNN: Hjertearytmier, spesielt atrieflimmer (AF) er normalt blant profesjonelle utholdenhetsutøvere. Konvensjonelle diagnostiseringsverktøy for evaluering av rytmeforstyrrelser er begrenset med tanke på tilgjengelighet, begrenset testvarighet og brukervennlighetsutfordringer. Denne studien sikter mot å evaluere (1) forholdet mellom treningskarateristikk og helseerfaringer i et større, representativt utvalg av utholdenhetsutøvere (2) validiteten og funksjonaliteten av det nye utviklede ECG247 Smart Heart Sensor™ i en profesjonell utøvers treningsmiljø. METODE: Et digitalt spørreskjema som kvantifiserer sammenhengen mellom treningskarakteristikk og helseerfaringer ble utviklet og utlevert i digitalt format. Totalt 1802 menn og kvinner svarte på spørreskjemaet. I tillegg ble totalt 13 profesjonelle syklister ved UNO-X Pro Cycling Team undersøkt kontinuerlig med ECG247 Smart Heart Sensor™ under en treningssamling i Spania, desember 2021 ved bruk av automatisert algoritmisk deteksjon av arytmihendelser. De samme EKG-dataene ble også analysert av sertifiserte kardiologer ved Sørlandet Sykehus Arendal. Idrettsutøvere fullførte et kort spørreskjema som registrerte treningen deres (fra overvåkingsenheter på sykkel), og utførte en egenvurdering av brukervennlighetsparameterne til EKG-enheten og applikasjonen etter testen. RESULTATER: Diagnostisert AF ble rapportert av 52 av de 1802 utholdenhetsutøvere (2.9%). Alder, årlig utholdenhetstreningsvolum og subjektivt prestasjonsnivå var signifikante prediktorer (p=0.001) for diagnostisert AF. Under EKG-testen var gjennomsnittlig testvarighet var 8924 timer, inkludert et gjennomsnitt på 155 treningstimer under hver test. EKG-kvaliteten fra alle tester ble ansett som tilfredsstillende for rytmeanalyse, også under trening. Den rapporterte brukervennligheten til ECG247 Smart Heart Sensor var høy. Den automatiske arytmialgoritmen rapporterte mulige arytmihendelser i 13 (62%) tester; 9 atrieflutter og 4 supraventrikulær takykardi. Retrospektiv manuell vurdering av leger avslørte normal sinusrytme i alle tester med disse falske positive hendelsene observert under trening når hjertefrekvensen var forhøyet. Ingen falske negative funn ble observert. KONKLUSJON: Prevalensen av diagnostisert AF steg ved prestasjonsnivå og årlig treningstimer, noe som støtter behovet for et bedre EKG-målingsverktøy. ECG247 Smart Heart Sensor™ viste høykvalitets EKG-opptak under intensiv trening. Den integrerte arytmianalyseringsalgoritmen kan bli optimalisert for denne gruppen for å redusere antallet falske positive tilfeller assosiert med normal «takykardi» av utholdenhetstrening. NØKKELORD: Utholdenhet, Utholdenhetsutøvere, Atrieflimmer, Elektrokardiogram, Kardiologisk helsesjekk, kardiovaskulære lidelser i utøvere

PhysioDroid: Combining Wearable Health Sensors and Mobile Devices for a Ubiquitous, Continuous, and Personal Monitoring

Technological advances on the development of mobile devices, medical sensors, and wireless communication systems support a new generation of unobtrusive, portable, and ubiquitous health monitoring systems for continuous patient assessment and more personalized health care. There exist a growing number of mobile apps in the health domain; however, little contribution has been specifically provided, so far, to operate this kind of apps with wearable physiological sensors. The PhysioDroid, presented in this paper, provides a personalized means to remotely monitor and evaluate users’ conditions. The PhysioDroid system provides ubiquitous and continuous vital signs analysis, such as electrocardiogram, heart rate, respiration rate, skin temperature, and body motion, intended to help empower patients and improve clinical understanding. The PhysioDroid is composed of a wearable monitoring device and an Android app providing gathering, storage, and processing features for the physiological sensor data. The versatility of the developed app allows its use for both average users and specialists, and the reduced cost of the PhysioDroid puts it at the reach of most people. Two exemplary use cases for health assessment and sports training are presented to illustrate the capabilities of the PhysioDroid. Next technical steps include generalization to other mobile platforms and health monitoring devices.This work was partially supported by the Spanish CICYT Project SAF2010-20558, Junta de Andalucia Project P09-TIC-175476, and the FPU Spanish Grant AP2009-2244. This work was also supported in part by the INTERREG IV European Project WHM-Wireless Health Monitoring (I-1-02=091) and the European Commission Seventh Framework Programme FP7 Project OPENi-Open-Source, Web-Based, Framework for Integrating Applications with Social Media Services, and Personal Cloudlets under Grant no. 317883

No soldiers left behind: An IoT-based low-power military mobile health system design

© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user\u27s situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information

Anxolotl - An Anxiety Companion App

Dissertação para obtenção do Grau de Mestre em Engenharia Informática e de ComputadoresA Organização Mundial de Saúde apresentou as perturbações mentais como os maiores contribuintes para incapacidade global em 2015, com os distúrbios de ansiedade a ocuparem a sexta posição. Distúrbios de ansiedade têm um alta prevalência na sociedade, e apresentam sintomas precoces que podem ser detetados. Nesta tese, produzimos um sistema capaz de detetar sintomas de distúrbios de ansiedade antes que a doença se instale por completo. Adicionalmente, queremos dar outra opção a portadores, monitorizando o seu estado mental e oferecendo a hipótese de tratarem dos seus níveis de ansiedade antes que apareçam mais sintomas. Aqui introduzimos um sistema de saúde móvel, entitulado de Anxolotl, que pode detetar e classificar níveis de ansiedade multiclasse e detetar níveis binários de estados de pânico . A nossa solução é composta por: datacenter, com o objectivo de guardar dados fisiológicos anónimos, e aplicar modelos de aprendizagem automática; broker de mensagens, que irá providenciar escalaabilidade e habilidade de desacoplamento no sistema; aplicação móvel, que funcionará em conjunto com um wearable para capturar dados fisiológicos. A nossa applicação é capaz de detetar e monitorizar diariamente, os níveis de ansiedade e pânico do utilizador, filtrando dados dúbios com base em atividade física. A aplicação também apresenta múltiplos exercícios de respiração guiados, bem como meditações acompanhadas para vários cenários de saúde mental. O nosso modelo de deteção de ansiedade foi capaz de apresentar uma precisão de 92% e um f1-Score de 90% na classificação de ansiedade multiclasse, treinando com um dataset com 124 entradas, enquanto que o nosso modelo de deteção de pânico apresenta uma precisão de 94% e um f1-Score de 94%. Estes valores foram atingindos utilizando maioritariamente dados de ritmo cardíaco. O código dos modelos está disponível em https://github.com/nunogoms/Anxolotl-engines.World Health Organization referred that common mental health disorders were the biggest contributors to global disability during the year of 2015, with anxiety disorders occupying the 6th position. Currently, anxiety disorders have high prevalence in society, and present early symptoms that are suited to be detected. With this thesis, we intend to produce a system capable of detecting the anxiety disorder early symptoms before the onset of the full range of symptoms. Additionally, we want to give another option to people already affected, in the form of monitoring their mental health, and the ability for them to react to their anxiety state quickly. Herein, we are introducing a mobile health system — Anxolotl, that can detect and classify multi class anxiety levels and detect binary panic states. Our solution is composed by: a datacenter, intended to store anonymous physiological data and applying the machine learning models; a message broker, aiming to provide scalability and decoupling to the system; and, finally a mobile app, which will work in tandem with a wearable to capture physiological data. The app is able to track and monitor, on a daily basis, its user’s anxiety and panic levels, filtering when the data is unreliable based on activity. It also presents the users with guided breathing exercises for multiple mental health scenarios as well as some guided meditations, in an effort to help its users. The Anxiety Engine model provided a 92% accuracy and 90% f1-Score in classifying multi-class anxiety levels, training and testing with a dataset containing 124 entries, and our binary Panic Engine had an accuracy of 94% and a f1-Score of 94%. Both these scenarios were mainly achieved by using heart rate data, activity context was also used in some scenarios. The code for these models is available at https://github.com/nunogoms/Anxolotl-engines.N/