A STUDY ON CLOUD BASED BIO-SIGNALS MANAGEMENT FRAMEWORK

An analytical study of the complete framework for the management of biosignals is done. The framework provides for the acquisition, and storage of the biosignals, along with the associated metadata. It also provides solutions for validation, synchronization of acquired signals, thus allowing error-free signal inputs for further statistical analysis. The model comprises primarily of four layers, namely, acquisition, validation, post-processing and statistical analysis layers. Additionally, a presentation layer is also provided, wherein the appropriate end-user can use a suitable client or Web service to access the results of the statistical analysis. The raw data is deliberately spilt into two: Internal data (actual signal data) and External data (metadata) and they interact only when necessary (e.g. Identifying the biosignal's origin). Microservices are used to compartmentalize the functionalities required in the system. Additional solutions to problems plaguing the present models (like cloud-upload bottleneck) are also discussed.Â

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

Smart Clothing Framework for Health Monitoring Applications

Wearable technologies are making a significant impact on people’s way of living thanks to the advancements in mobile communication, internet of things (IoT), big data and artificial intelligence. Conventional wearable technologies present many challenges for the continuous monitoring of human health conditions due to their lack of flexibility and bulkiness in size. Recent development in e-textiles and the smart integration of miniature electronic devices into textiles have led to the emergence of smart clothing systems for remote health monitoring. A novel comprehensive framework of smart clothing systems for health monitoring is proposed in this paper. This framework provides design specifications, suitable sensors and textile materials for smart clothing (e.g., leggings) development. In addition, the proposed framework identifies techniques for empowering the seamless integration of sensors into textiles and suggests a development strategy for health diagnosis and prognosis through data collection, data processing and decision making. The conceptual technical specification of smart clothing is also formulated and presented. The detailed development of this framework is presented in this paper with selected examples. The key challenges in popularizing smart clothing and opportunities of future development in diverse application areas such as healthcare, sports and athletics and fashion are discussed

Human Factors and Neurophysiological Metrics in Air Traffic Control: a Critical Review

International audienceThis article provides the reader a focused and organised review of the research progresses on neurophysiological indicators, also called “neurometrics”, to show how neurometrics could effectively address some of the most important Human Factors (HFs) needs in the Air Traffic Management (ATM) field. The state of the art on the most involved HFs and related cognitive processes (e.g. mental workload, cognitive training) is presented together with examples of possible applications in the current and future ATM scenarios, in order to better understand and highlight the available opportunities of such neuroscientific applications. Furthermore, the paper will discuss the potential enhancement that further research and development activities could bring to the efficiency and safety of the ATM service

Procedures and Methodologies for the Control and Improvement of Energy-Environmental Quality in Construction

This Special Issue aims at providing the state-of-the-art on procedures and methodologies developed to improve energy and environmental performance through building renovation. We are greatly thankful to our colleagues building physics experts, building technology researchers, and urban environment scholars who contributed to this Special Issue, for sharing their original works in the field

Affect Infusion and Detection through Faces in Computer-mediated Knowledge-sharing Decisions

Faces are important in both human communication and computer-mediated communication. In this study, I analyze the influence of emotional expressions in faces on knowledge-sharing decisions in a computer-mediated environment. I suggest that faces can be used for affect infusion and affect detection, which increases the effectiveness of knowledge-management systems. Using the affect infusion model, I discuss why emotions can be expected to influence knowledge-sharing decisions. Using the two-step primitive emotional contagion framework, I found that emotional facial expression attached to a knowledge-sharing request influenced knowledge-sharing decisions. This influence was mediated by the decision maker’s emotional valence in the facial expression tracked by Face Reader technology and held for females but not males. I discuss implications for designers of emotionally intelligent information systems and research