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This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

The purpose of this paper is to design a low-power human physiological parameters monitoring system which can monitor six vital parameters simultaneously based on wearable body sensor network.

This paper presents a low-power multiple physiological parameters monitoring system (MPMS) which comprises four subsystems. These are: electrocardiogram (ECG)/respiration (RESP) parameters monitoring subsystem with embedded algorithms; blood oxygen (SpO₂)/pulse rate (PR)/body temperature (BT)/blood pressure (BP) parameters monitoring subsystem with embedded algorithms; main control subsystem which is in charge of system-level power management, communication and interaction design; and upper computer software subsystem which manipulates system function and analyzes data.

Results have successfully demonstrated monitoring human ECG, RESP, PR, SpO₂, BP and BT simultaneously using the MPMS device. In addition, the power reduction technique developed in this work at the physical/hardware level is effective. Reliability of algorithms developed for monitoring these parameters is assessed by Fluke Prosim8 Vital Signs Simulators (produced by Fluke Corp. USA).

The MPMS device provides long-term health monitoring without interference from normal personal activities, which potentially allows applications in real-time daily healthcare monitoring, chronic diseases monitoring, elderly monitoring, human emotions recognization and so on.

First, a power reduction technique at the physical/hardware level is designed to realize low power consumption. Second, the proposed MPMS device enables simultaneously monitoring six key parameters. Third, unlike most monitoring systems in bulk size, the proposed system is much smaller (118 × 58 × 18.5 mm3, 140 g total weight). In addition, a comfortable smart shirt is fabricated to accommodate the portable device, offering reliable measurements.

The recent advancement in gait analysis combines internet of things that provides better observations of person living behavior. The biomechanical model used for elderly and physically challenged persons is related to gait-related parameters, and the accuracy of the existing systems significantly varies according to different person abilities and their challenges. The paper aims to discuss these issues.

Deployment of wearable sensors in gait analysis provides a better solution while tracking the changes of the personal style, and this proposed model uses an electronics system using force sensing resistor and body sensors.

Experimental results provide an average gait recognition of 95 percent compared to the existing neural network-based gait analysis model based on the walking speeds and threshold values.

The sensors are used to monitor and update the predicted values of a person for analysis. Using IoT a communication process is performed in the research work by identifying a physically challenged person even in crowded areas.

The purpose of this paper is to demonstrate how commercially‐off‐the‐shelf sensors and stimulators, such as infrared rangers and vibrators, can be retrofitted as a useful assistive technology in real and virtual environments.

The paper describes how a wearable range‐vibrotactile device is designed and tested in the real‐world setting, as well as thorough evaluations in a virtual environment for complicated navigation tasks and neuroscience studies.

In the real‐world setting, a person with normal vision who has to navigate their way around a room with their eyes closed will quickly rely on their arms and hands to explore the room. The authors’ device allows a person to “feel” their environment without touching it. Due to inherent difficulties in testing human subjects when navigating a real environment, a virtual environment affords us an opportunity to scientifically and extensively test the prototype before deploying the device in the real‐world.

This project serves as a starting‐point for further research in benchmarking assistive technology for the visually impaired and to eventually develop a man‐machine sensorimotor model that will improve current state‐of‐the‐art technology, as well as a better understanding of neural coding in the human brain.

Based on 2012 World Health Organization, there are 39 million blind people. This project will have a direct impact on this community.

The paper demonstrates a low cost design of assistive technology that has been tested and evaluated in real and virtual environments, as well as integration of sensor designs and neuroscience.

The purpose of this paper is to explain, in brief, about smart, intelligent system which actively monitors the wellness of the elderly and will also send necessary alarms to the caretakers or doctors during an emergency because nowadays most of elderly people wish to stay alone independently. It is necessary to monitor their health conditions and activities continuously to prevent occurrence of health problems and also be able to provide medical assistance to them during emergencies.

The review paper describes the development of a methodology to monitor elderly continuously with a combination of advanced intelligent sensors, networking technologies and data processing system.

This paper identified various sensors used in smart home such as a pressure sensor, temperature sensor, etc., for monitoring elders health and their characteristics and also the cost, model number, etc., of various sensors available in the market.

This paper contains the comparison of various sensors available in the market that can be used in the smart home and also where we can use those sensors in smart home based on their characteristics.

The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.

This data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.

The proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.

The research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.

The emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.

In this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

The purpose of this paper is to design a human health monitoring system (HHMS) which helps in improving diagnostics at an earlier stage and monitoring after recoup.

The methodology involves a combination of three subsystems which monitors the human parameters such as temperature, heart rate, SpO2, fall and location of the person. Various sensors are used to extract the human parameters, and the data are analysed in a computer subsystem, through Global System for Mobile Communications (GSM) and Internet of Things (IoT) subsystem; the parameters measured are communicated to the caregiver and doctor.

Results have successfully demonstrated monitoring human temperature human temperature, heart rate, SpO2 and fall and location continuously using the HHMS prototype. Reliability of the technique used for monitoring these parameters is assessed by Proteus Professional 8 and LabVIEW simulators.

The HHMS enables long-term monitoring without any sort of interference from regular activities and allows daily health monitoring, elderly monitoring and so on.

First, the proposed HHMS simultaneously monitors five human parameters. Second, unlike most monitoring systems which uses older communication module, the proposed system is made smart using IoT. The proposed method has been made into a prototype system as detailed in this paper. The proposed HHMS can achieve high detection accuracy. Therefore, this system can be reliably deployed into a consumer product for use as monitoring device with high accuracy.

The purpose of this paper is to develop a framework for an interactive clothing that offers a self-directed learning environment in which learners can practice exercises in a time and cost-efficient manner.

To verify the validity of the framework, an interactive shirt has been developed that can help its wearer practicing certain motor skills in a self-directed manner. This shirt enables the wearer to set reference body postures and to compare current posture with them and can notify whether its wearer repeats them correctly or not through vibrotactile feedback.

The interactive shirt prototype developed in this study will offer an environment in which learners can practice exercises in a time and cost-efficient manner.

The smart garment framework developed in this study consists of sensor-actuator module, switch device and control software. As this framework is easily scalable, it is expected that it can be used for various smart garment projects where an interaction between the garment and its wearer is needed.

Construction action recognition is essential to efficiently manage productivity, health and safety risks. These can be achieved by tracking and monitoring construction work. This study aims to examine the performance of a variant of deep convolutional neural networks (CNNs) for recognizing actions of construction workers from images of signals of time-series data.

This paper adopts Inception v1 to classify actions involved in carpentry and painting activities from images of motion data. Augmented time-series data from wearable sensors attached to worker's lower arms are converted to signal images to train an Inception v1 network. Performance of Inception v1 is compared with the highest performing supervised learning classifier, k-nearest neighbor (KNN).

Results show that the performance of Inception v1 network improved when trained with signal images of the augmented data but at a high computational cost. Inception v1 network and KNN achieved an accuracy of 95.2% and 99.8%, respectively when trained with 50-fold augmented carpentry dataset. The accuracy of Inception v1 and KNN with 10-fold painting augmented dataset is 95.3% and 97.1%, respectively.

Only acceleration data of the lower arm of the two trades were used for action recognition. Each signal image comprises 20 datasets.

Little has been reported on recognizing construction workers' actions from signal images. This study adds value to the existing literature, in particular by providing insights into the extent to which a deep CNN can classify subtasks from patterns in signal images compared to a traditional best performing shallow network.

The purpose of this paper is to understand what dimensions consumers prefer to track using wearable technology to achieve a healthier lifestyle and how these tracking dimensions are related.

An online survey was conducted with potential consumers in the USA, and a series of Pearson’s correlation and regression analysis and multiple regressions was conducted.

The most preferred self-tracking dimensions, tracking dimensions on others, most private tracking dimensions, most variable dimensions, and the dimensions that need to be improved were identified. The results of this study showed positive relationships overall among similar types of tracking dimensions, such as among dimensions of physical health condition (disease and disorder symptoms and general vital signs), mental health condition (stress level and mood/feeling), healthy lifestyle (fitness, and pose and posture), and productivity and task management (work productivity, location, and time management).

Designers are encouraged to make wearable technology products that are durable, easy to care for, attractive in design, comfortable to wear and use, able to track preferred dimensions, appropriate for various consumers, unobtrusive, portable, and small. This research will guide wearable technology and fashion industry professionals in the development process of wearable technology to benefit consumers by helping them be more self-aware, empowering them to develop a healthier lifestyle, and ultimately increasing their quality of life and well-being.