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Vital-Jacket®: A wearable wireless vital signs monitor for patients' mobility in cardiology and sports
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The Vital Jacket<sup>®</sup> (VJ) is a wearable vital signs monitoring system that joins textiles with microelectronics. After several years of development within the university lab, it has been licensed to a start-up company. Its evolutions have focused on cardiology and sports and scaled down from a jacket to a single T-shirt. The VJ manufacturing process has recently been certified to comply with the standards ISO9001 and ISO13485 and the cardiology version was approved as a Medical Device for the European market compliant with the MDD directive 42/93/CE, holding the CE1011 mark. The authors intend to wear VJs during the days of the congress to demonstrate its usefulness in first hand and will exemplify the different scenarios of use of this innovative wearable intelligent garment.
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... HT, diabetes) [26], diagnosis of arrhythmias and/or signs of volume overload in patients with implantable cardiac devices [27][28][29][30][31][32][33], early detection and treatment of decompensation episodes in HF patients (thus preventing (re)hospitalizations) [27-29, 34, 35], and monitoring of patients participating in home-based cardiac tele-rehabilitation programs [36 -38]. TM programs often have an educational component, enabling cardiovascular prevention (primary or secondary), which is largely centred on regular counselling (face-to-face or virtual) and allows patients to better manage their health condition as well as participate in their health care [39]. ...
... Vital Jacket The VitalJacket [87], presented in Figure 5a, is a wearable device developed by researchers from the IEETA research unit at the University of Aveiro and commercialized by Biodevices SA [88]. It is designed to continuously record high-quality ECG and other vital signals in various clinical and everyday settings. ...
In the last decades, researchers have shown the potential of using Electrocardiogram (ECG) as a biometric trait due to its uniqueness and hidden nature. However, despite the great number of approaches found in the literature, no agreement exists on the most appropriate methodology. This paper presents a systematic review of data acquisition methods, aiming to understand the impact of some variables from the data acquisition protocol of an ECG signal in the biometric identification process. We searched for papers on the subject using Scopus, defining several keywords and restrictions, and found a total of 121 papers. Data acquisition hardware and methods vary widely throughout the literature. We reviewed the intrusiveness of acquisitions, the number of leads used, and the duration of acquisitions. Moreover, by analyzing the literature, we can conclude that the preferable solutions include: (1) the use of off-the-person acquisitions as they bring ECG biometrics closer to viable, unconstrained applications; (2) the use of a one-lead setup; and (3) short-term acquisitions as they required fewer numbers of contact points, making the data acquisition of benefit to user acceptance and allow faster acquisitions, resulting in a user-friendly biometric system. Thus, this paper reviews data acquisition methods, summarizes multiple perspectives, and highlights existing challenges and problems. In contrast, most reviews on ECG-based biometrics focus on feature extraction and classification methods.
... Participants were exposed to three environments: baseline, virtual and real. In the RE, the only equipment used by participants, besides the firefighter uniform, was the Vital Jacket [54], a wearable platform that captures electrocardiogram (ECG) exams in real-time. In the VE, participants used: a Vital Jacket to allow the recording of their HRV; an HTC Vive HMD, to allow them to explore the VE with six degrees of freedom; Bose QuietComfort 25 headphones with acoustic noise cancellation technology, to absorb realworld sounds and allow the user to focus on VE sounds; a self-contained breathing apparatus, to make participants carry the same weight as in the real environment; finally, a pair of gloves and shoe-wraps with passive retroreflective markers, to allow the tracking of the position and orientation of participants' hands and feet ( Fig. 1 (a)). ...
The use of Virtual Reality (VR) technology to train professionals has increased over the years due to its advantages over traditional training. This paper presents a study comparing the effectiveness of a Virtual Environment (VE) and a Real Environment (RE) designed to train firefighters. To measure the effectiveness of the environments, a new method based on participants Heart Rate Variability (HRV) was used. This method was complemented with self-reports, in the form of questionnaires, of fatigue, stress, sense of presence, and cybersickness. An additional questionnaire was used to measure and compare knowledge transfer enabled by the environments. The results from HRV analysis indicated that participants were under physiological stress in both environments, albeit with less intensity on the VE. Regarding reported fatigue and stress, the results showed that none of the environments increased such variables. The results of knowledge transfer showed that the VE obtained a significant increase while the RE obtained a positive but non-significant increase (median values, VE: before 4 after 7, p = .003; RE: before 4 after 5, p =.375). Lastly, the results of presence and cybersickness suggested that participants experienced high overall presence and no cybersickness. Considering all results, the authors conclude that the VE provided effective training but that its effectiveness was lower than that of the RE.
... Nowadays, the variety of smart clothing that serve as health monitors like smart shirts [21,22], underwear [23] and even smart socks [50] with embed cardiac, breathing and activity sensors, can be found on the market. Company Hexoskin has developed a collection of smart shirts with embedded lightweight, machine washable, textile sensors to monitor heart, respiration, and activity [24], Figure 1. ...
... A number of the prototypes are used as stand-alone devices to record ECG signals in a clinical environment, rather than as part of an integrated tele-monitoring system [12]- [13]. Meanwhile, other works have presented remote tele-monitoring systems focused on collecting ECG signals and other important biosignals, such as those related to electromyography (EMG) [14], breathing [15]- [17], accelerometery [12], [18], and galvanic skin response [13]. The systems presented in [13] also provide tools for off-line digital signal processing, gathering the principal parameters assessed from signals, including heart rate, blood pressure, respiratory rate, and activity classification. ...
p class="Abstract">The paper presents a new e-textile-based system, named SWEET Shirt, for the remote monitoring of biomedical signals. The system includes a textile sensing shirt, an electronic unit for data transmission, a custom-made Android application for real-time signal visualisation and a software desktop for advanced digital signal processing. The device allows for the acquisition of electrocardiographic, bicep electromyographic and trunk acceleration signals. The sensors, electrodes, and bus structures are all integrated within the textile garment, without any discomfort for users. A wide-ranging set of algorithms for signal processing were also developed for use within the system, allowing clinicians to rapidly obtain a complete and schematic overview of a patient’s clinical status. The aim of this work was to present the design and development of the device and to provide a validation analysis of the electrocardiographic measurement and digital processing. The results demonstrate that the information contained in the signals recorded by the novel system is comparable to that obtained via a standard medical device commonly used in clinical environments. Similarly encouraging results were obtained in the comparison of the variables derived from the signal processing.</p
The book presents the state of the art of the Internet of Things (IoT), applied to Human-Centered Design (HCD) projects addressed to ageing users, from the perspective of health, care and well-being. The current focus on the ageing population is opening up new opportunities for the development of niche solutions aimed at the niche category of older users who are beginning to experience physical and cognitive decline but are still independent and need to maintain their autonomy for as long as possible. The combination between the needs expressed by older users and the opportunities offered by the recent innovative technologies related to the Internet of Things allows research institutions, stakeholders, and academia to target and design new solutions for older users, safeguarding their well-being, health, and care, improving their quality of life.
This book discusses and analyses the most recent services, products, systems and environments specifically conceived for older users, in order to enhance health, care, well-being and improve their quality of life. This approach is coherent with the percept of AAL or enhanced living environment, looking to the users’ comfort, autonomy, engagement and healthcare. The book describes and analyses aspects of HCD with older users looking to the emerging technologies, products, services, and environments analysed in their actual application in different areas, always concerning the design for the elderly related to the IoT, just as the development of biomonitoring devices, tools for activity recognition and simulation, creation of smart living environments, solutions for their autonomy, assistance and engagement enhancing health, care and wellbeing. The book is intended for researchers, designers, engineers, and practitioners in healthcare to connect academia, stakeholders, and research institutions to foster education, research and innovation.
Recent years observed massive growth in wearable technology, everything can be smart: phones, watches, glasses, shirts, crutches, etc. These technologies are prevalent in various fields: from wellness, sports, and fitness to the healthcare domain. The spread of this phenomenon led the World Health Organization to define the term “mHealth” as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices”. Furthermore, mHealth solutions are suitable to perform real-time wearable biofeedback systems: sensors in the body area network connected to a processing unit (smartphone) and a feedback device (loudspeaker) to measure human functions and return them to the user as (bio)feedback signal. Considering the COVID-19 pandemic emergency, never as today, we can say that the integration of mHealth systems in our society may contribute to a new era of clinical practice. After reporting a brief description of mHealth system architecture, this chapter explores several opportunities where innovative mHealth solutions could improve assessment and rehabilitation strategies for aging people and persons with Parkinson's disease. This chapter presents solutions that need medical support in a clinical context and others that can be self-administered and require only a smartphone as a stand-alone system. Finally, the Discussion highlights the challenges for future research and development of innovative mHealth systems.KeywordsMobile Health applications (mHealth apps)Wearable inertial sensorsAssessmentRehabilitationIoTGaitBiofeedback
This paper presents a model of a smart healthcare service for stress management in dental patients during the interventions. The main goal is to provide dental clinics with a model that enables introducing a stress management service into everyday practice and provides patients with a better experience in a typically stressful situation. The approach is based on employing wearable sensors for monitoring physiological parameters, and a mobile application for progressive muscle relaxation therapy. Dental patients were divided into experimental and control groups. Participants from the experimental group were treated with progressive muscle relaxation through mobile health application with audio content, and patients from the control group were not exposed to any relaxation method. Heart rate was measured in both groups through three test phases: pre-intervention, intervention, and post-intervention. Evaluation of the anxiety level was performed using the STAI test. Results show that the measured heart rate in the post-intervention phase is lower than in the intervention phase in both testing groups, as well as in the pre-intervention phase. STAI scores were significantly higher in the control group through all test phases. The research found that the proposed system applied to dentist patients may relieve their anxiety symptoms and decrease stress level, which improves the patients’ experience and leads to higher patients’ satisfaction.
The use of Wireless Body Area Networks (WBANs) in healthcare for pervasive monitoring enhances the lives of patients and allows them to fulfill their daily life activities while being monitored. Various non-invasive sensors are placed on the skin to monitor several physiological attributes, and the measured data are transmitted wirelessly to a centralized processing unit to detect changes in the health of the monitored patient. However, the transferred data are vulnerable to various sources of interference, sensor faults, measurement faults, injection and alteration by malicious attackers, etc. In this article, we propose a change point detection model based on a Markov chain for centralized anomaly detection in WBANs. The model is derived from the Root Mean Square Error (RMSE) between the forecasted and measured values for whole attributes. The RMSE transforms the monitored attributes into a univariate times series which is divided into overlapping sliding window. The joint probability of the sequence of RMSE values in each sliding window is calculated to decide whether a change has occurred or not. When an effective change is detected over
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consecutive windows, the number of deviated attributes is used to distinguish faulty measurements from a health emergency. We apply our proposed approach on real physiological data from the Physionet database and compare it with existing approaches. Our experimental results prove the effectiveness of our proposed approach, as it achieves high detection accuracy with a low false alarm rate (5.2%).
