An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring of inhabitants is possible even without hospitalization. The advancement of sensing technologies, embedded systems, wireless communication technologies, nano technologies, and miniaturization makes it possible to develop smart systems to monitor activities of human beings continuously. Wearable sensors detect abnormal and/or unforeseen situations by monitoring physiological parameters along with other symptoms. Therefore, necessary help can be provided in times of dire need. This paper reviews the latest reported systems on activity monitoring of humans based on wearable sensors and issues to be addressed to tackle the challenges.

Wearable Sensors for Human Activity Monitoring: A Review

Current progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to a base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments.

A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments

Technology has become ubiquitous, it is all around us and is becoming part of us. Togetherwith the rise of the Internet of Things (IoT) paradigm and enabling technologies (e.g., Augmented Reality (AR), Cyber-Physical Systems, Artificial Intelligence (AI), blockchain or edge computing), smart wearables and IoT-based garments can potentially have a lot of influence by harmonizing functionality and the delight created by fashion. Thus, smart clothes look for a balance among fashion, engineering, interaction, user experience, cybersecurity, design and science to reinvent technologies that can anticipate needs and desires. Nowadays, the rapid convergence of textile and electronics is enabling the seamless and massive integration of sensors into textiles and the development of conductive yarn. The potential of smart fabrics, which can communicate with smartphones to process biometric information such as heart rate, temperature, breathing, stress, movement, acceleration, or even hormone levels, promises a new era for retail. This article reviews the main requirements for developing smart IoT-enabled garments and shows smart clothing potential impact on business models in the medium-term. Specifically, a global IoT architecture is proposed, the main types and components of smart IoT wearables and garments are presented, their main requirements are analyzed and some of the most recent smart clothing applications are studied. In this way, this article reviews the past and present of smart garments in order to provide guidelines for the future developers of a network where garments will be connected like other IoT objects: the Internet of Smart Clothing.

https://www.mdpi.com/2079-9292/7/12/405/pdf

Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles

Innovative technology approaches have been increasingly investigated for the last two decades aiming at human-being long-term monitoring. However, current solutions suffer from critical limitations. In this paper, a complete system for contactless health-monitoring in home environment is presented. For the first time, radar, wireless communications, and data processing techniques are combined, enabling contactless fall detection and tagless localization. Practical limitations are considered and properly dealt with. Experimental tests, conducted with human volunteers in a realistic room setting, demonstrate an adequate detection of the target's absolute distance and a success rate of 94.3% in distinguishing fall events from normal movements. The volunteers were free to move about the whole room with no constraints in their movements.

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Analysis of an Indoor Biomedical Radar-Based System for Health Monitoring

Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

https://www.mdpi.com/1424-8220/20/6/1796/pdf

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges.

This paper presents a multimodal system for seamless surveillance of elderly people in their living environment. The system uses simultaneously a wearable sensor network for each individual and premise-embedded sensors specific for each environment. The paper demonstrates the benefits of using complementary information from two types of mobility sensors: visual flow-based image analysis and an accelerometer-based wearable network. The paper provides results for indoor recognition of several elementary poses and outdoor recognition of complex movements. Instead of complete system description, particular attention was drawn to a polar histogram-based method of visual pose recognition, complementary use and synchronization of the data from wearable and premise-embedded networks and an automatic danger detection algorithm driven by two premise- and subject-related databases. The novelty of our approach also consists in feeding the databases with real-life recordings from the subject, and in using the dynamic time-warping algorithm for measurements of distance between actions represented as elementary poses in behavioral records. The main results of testing our method include: 95.5% accuracy of elementary pose recognition by the video system, 96.7% accuracy of elementary pose recognition by the accelerometer-based system, 98.9% accuracy of elementary pose recognition by the combined accelerometer and video-based system, and 80% accuracy of complex outdoor activity recognition by the accelerometer-based wearable system.

https://www.mdpi.com/1424-8220/14/5/7831/pdf

Seamless tracing of human behavior using complementary wearable and house-embedded sensors.

With the recent advancement in wearable computing, sensor technologies, and data processing approaches, it is possible to develop smart clothing that integrates sensors into garments. The main objective of this study was to develop the method of automatic recognition of sedentary behavior related to cardiovascular risk based on quantitative measurement of physical activity. The solution is based on the designed prototype of the smart shirt equipped with a processor, wearable sensors, power supply and telemedical interface. The data derived from wearable sensors were used to create feature vector that consisted of the estimation of the user-specific relative intensity and the variance of filtered accelerometer data. The method was validated using an experimental protocol which was designed to be safe for the elderly and was based on clinically validated short physical performance battery (SPPB) test tasks. To obtain the recognition model six classifiers were examined and compared including Linear Discriminant Analysis, Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Binary Decision Trees and Artificial Neural Networks. The classification models were able to identify the sedentary behavior with an accuracy of 95.00% ± 2.11%. Experimental results suggested that high accuracy can be obtained by estimating sedentary behavior pattern using the smart shirt and machine learning approach. The main advantage of the developed method to continuously monitor patient activities in a free-living environment and could potentially be used for early detection of increased cardiovascular risk.

Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk.

With recent advances in microprocessor chip technology, wireless communication, and biomedical engineering it is possible to develop miniaturized ubiquitous health monitoring devices that are capable of recording physiological and movement signals during daily life activities. The aim of the research is to implement and test the prototype of health monitoring system. The system consists of the body central unit with Bluetooth module and wearable sensors: the custom-designed ECG sensor, the temperature sensor, the skin humidity sensor and accelerometers placed on the human body or integrated with clothes and a network gateway to forward data to a remote medical server. The system includes custom-designed transmission protocol and remote web-based graphical user interface for remote real time data analysis. Experimental results for a group of humans who performed various activities (eg. working, running, etc.) showed maximum 5% absolute error compared to certified medical devices. The results are promising and indicate that developed wireless wearable monitoring system faces challenges of multi-sensor human health monitoring during performing daily activities and opens new opportunities in developing novel healthcare services.

Monitoring activities of daily living based on wearable wireless body sensor network.

Abstract The authors describe experimental and theoretical analyses of faults of power transformer winding. Faults were caused by mechanical effect of short-circuit currents. Measurements of transformer were carried out in high-voltage laboratory. Frequency and time diagnostic methods (method SFRA - Sweep Frequency Response Analysis, impact test) were used for the analyses. Coils of transformer windings were diagnosed by means of the SFRA method and the time impact test. The analyzed methods had a significant sensitivity to a relatively small deformation of coil. In the analysis a new technique for analyzing the effects of short-circuit currents is introduced. This technique is developed for high-voltage transformers (different types of power). The proposed analyses show that it is necessary to analyze the value of short-circuit current. Short-circuit current represents a danger for the operation of the power transformer. The proposed approach can be used for other types of transformers. Moreover, the presented techniques have a potential application for fault diagnosis of electrical equipment such as: transformers and electrical machines.

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Frequency and time fault diagnosis methods of power transformers

Health monitoring and body area network (BAN) applications require wireless intelligent monitoring devices and information systems The aim of our research is to propose a prototype of wearable wireless monitoring device optimized to supervising the patient and examine the influence of movement on the heart rate during normal daily activities Main purpose of the proposed system consists in simultaneous acquisition and automatic analysis of two bipolar ECG and three-axes acceleration (ACC) signals measured by means of wireless, battery-operated prototype of Revitus ECG module The processing of the ECG and ACC data is performed by the custom-developed software installed on PC All recorded information is uploaded to a purposely-designed medical web server for the storage and display as a web page for authorized doctors or patient's family The system was tested on 10 healthy volunteers Each of them was monitored during common daily activities

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Eliasz Kantoch

Papers

Seamless tracing of human behavior using complementary wearable and house-embedded sensors.

Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk.

Monitoring activities of daily living based on wearable wireless body sensor network.

Frequency and time fault diagnosis methods of power transformers

Wireless body area network system based on ECG and accelerometer pattern