Identification of Optimal Data Augmentation Techniques for Multimodal Time-Series Sensory Data: A Framework

Human activity recognition(HAR) analyzes human activity information through sensors in wearable devices. Research on HAR based on deep learning has been widely used. The deep ensemble model enhances the generalization performance of the model based on the deep model. However,the deep ensemble model must train multiple models simultaneously, which requires a large amount of computing resources and time. In this paper, semi-feature shared blocks are used to construct correlated redundant features on the feature map so that the model training of each path in the deep ensemble model can generate input diversity. At the same time, we perform feature fusion on the base classifiers of the model so that the deep ensemble model can learn deeper data features while adding base classifiers. We use depthwise convolution instead of traditional convolution to reduce the model's computational complexity without changing the baseline model's structure. Compared with conventional ensemble learning methods, our proposed model has better results in terms of parameters and computation.

The development of wearable sensor devices brings significant benefits to patients by offering real-time healthcare via wireless body area networks (WBANs). These wearable devices have gained significant traction due to advantageous features, including their lightweight nature, comfortable feel, stretchability, flexibility, low power consumption, and cost-effectiveness. Wearable devices play a pivotal role in healthcare, defence, sports, health monitoring, disease detection, and subject tracking. However, the irregular nature of the human body poses a significant challenge in the design of such wearable systems. This manuscript provides a comprehensive review of recent advancements in wearable and flexible smart sensor devices that can support the next generation of such sensor devices. Further, the development of direct ink writing (DIW) and direct writing (DW) methods has revolutionised new high-resolution integrated smart structures, enabling the design of next-generation soft, flexible, and stretchable wearable sensor devices. Recognising the importance of keeping academia and industry informed about cutting-edge technology and time-efficient fabrication tools, this manuscript also provides a thorough overview of the latest progress in various fabrication methods for wearable sensor devices utilised in WBAN and their evaluation using body phantoms. An overview of emerging challenges and future research directions is also discussed in the conclusion.

In recent years, intelligent wearable sensor devices have developed rapidly and can be seen everywhere in daily life. With the rapid development of electronic components and the continuous improvement of their performance, intelligent wearable intelligent products have gradually become possible and have shown explosive growth. In addition, intelligent wearable electronic devices have many advantages that traditional devices do not have. With the popularity of fitness wearable devices, intelligent wearable devices can also be used for real-time heart rate and dynamic electrocardiogram (ECG) monitoring during marathon sports. It can effectively prevent sudden death. During marathon training and other health services, it is very important to use intelligent wearable sensor devices to monitor heart function. This paper puts forward a heart function monitoring system for marathon training based on intelligent wearable sensor, expounds the origin of marathon sports and the importance of heart function monitoring for marathon athletes during training. This paper discusses the technology and construction method of heart rate monitoring system based on intelligent wearable sensor device. At the same time, relevant experiments are carried out to verify the relevant performance of the intelligent wearable sensor device in the algorithm. The results show that the R wave detection accuracy of wearable devices based on traditional algorithms is usually between 92% and 93%. The R wave detection accuracy of the intelligent wearable sensor device improved by the algorithm in this paper has been improved to more than 97%, and the R wave detection accuracy of the algorithm in this paper is much higher than that of the traditional algorithm. This also reflects the effectiveness of the intelligent wearable sensor device of the algorithm during the training of marathon athletes.

The smart wearable device is a new breed of mobile device that offers diversified utilities for health, sport, and finance for consumers worldwide. The current study aims to investigate the provocation of the intention to use smart wearable payment devices among Malaysian consumers. The unified theory of technology acceptance and use of technology (UTAUT) was employed with the cross-sectional survey-based data to explain the adoption of the smart wearable payment device. Furthermore, the UTAUT model was extended with trust and lifestyle compatibility factors to investigate smart wearable payment device adoption. The survey-based data were collected through the online survey and analyzed through the symmetrical modeling approach of partial least squares structural education modeling (PLS-SEM) to evaluate theoretical associations between the study constructs. The fuzzy set qualitative comparative analysis (fsQCA) was employed as an asymmetrical approach. As a result, it was found that the ease of use, lifestyle compatibility, and trust significantly impacted the intention to adopt smart wearable payment devices. However, social influence and facilitating conditions did not support the intention of adopting smart wearable payment devices. Adopting these devices requires policy and infrastructure development to harness the adoption of smart wearable payment devices. This paper is concluded with study limitations and future research suggestions.

Human activity recognition is an important direction in pattern recognition that learns from low-level raw signals acquired from smartphones and commercially available and customizable wearable devices to acquire high-level knowledge. HAR plays an essential role in providing smart healthcare to physically impaired older adults, with potential applications for elderly care, fall detection physical rehabilitation, clinical assessment and surveillance. Numerous researchers and scholars have conducted HAR based on conventional pattern recognition (PR) approaches and deep models. Conventional PR methods rely on the heuristic hand-crafted feature, which needs to pre-process the raw signals. Deep learning models can automatically learn features end-to-end, compared with the conventional PR approaches have achieved promising performance. Therefore, this paper reviews the progress of activity recognition based on wearable sensor devices, and discussed the potential application areas of human motion recognition technology. Finally, this paper discussed the related problems that can be further studied in the field of activity recognition.

In the field of health care management, smart wearable devices and its supporting technologies have tremendously made a name all around the globe. Smart watches and other sensor trackers are practically being used by various people and its usage has shown to be accompanied with lots of benefits. This technology was envisaged to play a vital role in the healthcare needs of people; especially with applications in the healthcare sector. The objective of this study, therefore, is to evaluate the technological impact of wearable sensors in human health and fitness (HHF). A web based survey was used for data collection for the period of one month. Emails were sent to registered members of a particular gym who uses any of the smart wearable sensors in keeping fit. The study findings indicate that among the smart wearable devices examined, smart wristwatches (45.6%) appears to be the most commonly used wearable sensor device followed by smart wrist bands (34.7%), smart textiles (10.7%) and smart rings (9.1%). This signifies that a large number of people can effortlessly use SWSs and devices and are optimistic about its support in their daily healthcare/fitness needs. Users are positive on the technological prospects of SWSs and devices; although there is a gap between personal motivation to use wearable devices and trust in the confidentiality and privacy of data generated.
 Keywords: Devices, Health, Fitness, Wearable, Sensors

Application of transfer learning and ensemble learning in image-level classification for breast histopathology

Introduction:Rehabilitation after anterior cruciate ligament (ACL) reconstruction is critical to patient outcome. Despite its importance; however, hospital-based rehabilitation is limited, with barriers, including distance and cost. With recent technological advancements, wearable devices have actively been used to address these barriers. In this study, we propose a randomized controlled trial protocol investigating the efficacy and feasibility of home-based rehabilitation after ACL reconstruction using a smart wearable device providing electrical stimulation that allows knee exercise.Methods and analysis:This is a protocol proposal for a prospective, single-center, randomized, controlled study. We plan to recruit adults discharged after ACL reconstruction; the recruited subjects will be randomly allocated to 1 of 2 groups, using a computer-generated randomization method: the intervention (n = 20) or control group (n = 20). The intervention group will receive a 6-week home-based rehabilitation program using smart wearable device. The control group will undergo a 6-week self-exercise program as normal. The following outcomes will be assessed at baseline, 2 weeks, and 6 weeks post the 6-week intervention program: quadriceps strength of the affect side as measured by a dynamometer (primary outcome); range of motion; root mean square of quadriceps muscle using surface electromyography; knee function using questionnaire; quality of life; subject's satisfaction score using questionnaire; frequency and duration of exercise; and knee pain. An intention-to-treat analysis will be conducted for the primary outcome.Discussion:This study is a prospective, single-center, randomized, controlled study. This study aims to research the feasibility and efficacy of a 6-week, structured home-based rehabilitation program for patients after ACL reconstruction using a smart wearable device. The findings of this study will help to establish a home-based rehabilitation program to better recovery in patients with ACL reconstruction.Trial registration number:This protocol was registered in ClinicalTrials.gov, under the number NCT04079205.

This chapter discusses the use of strain sensors in wearable devices. Strain sensors are used to monitor deformation under applied load. Various techniques for the fabrication of strain sensors are discussed and some example applications are presented. Special focus is placed on textile based and inkjet-printed strain sensors. Textile based strain sensors open new frontiers for wearable systems by integrating sensors into garments which can be used for extended periods of time. Inkjet printing along with conductive inkjet inks provides low cost, efficient, and rapid prototyping solution for implementation of strain sensors in wearable devices. Applications in the area of remote monitoring of physiological signals, vital signs, and human activity are presented.

Background and Aim. The growing integration of wearable smart technologies in sports has transformed the way individuals monitor and enhance their physical activity and performance. Devices such as smartwatches and fitness trackers enable users to track physiological and performance-related data, which may influence motivation and training behaviours. Despite their increasing popularity, understanding the factors influencing consumer adoption and acceptance of these technologies in sports remains important. Therefore, the objective of this study was to investigate consumer attitudes towards wearable smart technologies in sports and to examine how perceived training effectiveness, device design satisfaction, and enhanced sports experience influence users’ intention to adopt and continue using these technologies. Methods. A quantitative cross-sectional survey was conducted among 601 participants using a structured questionnaire assessing perceptions of usability, functionality, and motivational impact of wearable smart devices in sports. Data were analysed using IBM SPSS Statistics. Descriptive statistics were used to summarise demographic characteristics, while Pearson correlation analysis was performed to examine relationships among variables. Results. Among the participants, 49.4% were female, 24.6% were male, and 26.0% preferred not to disclose their gender. Correlation analysis revealed several statistically significant but weak relationships between device characteristics and user perceptions. A positive correlation was observed between perceived training effectiveness and satisfaction with device design (r = 0.107, p < 0.01). Additionally, enhanced sports experience was positively associated with the intention to continue using wearable technologies (r = 0.097, p < 0.05). Conclusions. The findings indicate that consumers generally have positive perceptions of wearable smart technologies in sports, with usability, design, and perceived performance benefits influencing their acceptance and continued use. Keywords: Wearable technology; smart wearable devices; technology adoption; consumer acceptance; sports technology

BackgroundWith the rise of mobile medicine, the development of new technologies such as smart sensing, and the popularization of personalized health concepts, the field of smart wearable devices has developed rapidly in recent years. Among them, medical wearable devices have become one of the most promising fields. These intelligent devices not only assist people in pursuing a healthier lifestyle but also provide a constant stream of health care data for disease diagnosis and treatment by actively recording physiological parameters and tracking metabolic status. Therefore, wearable medical devices have the potential to become a mainstay of the future mobile medical market.ObjectiveAlthough previous reviews have discussed consumer trends in wearable electronics and the application of wearable technology in recreational and sporting activities, data on broad clinical usefulness are lacking. We aimed to review the current application of wearable devices in health care while highlighting shortcomings for further research. In addition to daily health and safety monitoring, the focus of our work was mainly on the use of wearable devices in clinical practice.MethodsWe conducted a narrative review of the use of wearable devices in health care settings by searching papers in PubMed, EMBASE, Scopus, and the Cochrane Library published since October 2015. Potentially relevant papers were then compared to determine their relevance and reviewed independently for inclusion.ResultsA total of 82 relevant papers drawn from 960 papers on the subject of wearable devices in health care settings were qualitatively analyzed, and the information was synthesized. Our review shows that the wearable medical devices developed so far have been designed for use on all parts of the human body, including the head, limbs, and torso. These devices can be classified into 4 application areas: (1) health and safety monitoring, (2) chronic disease management, (3) disease diagnosis and treatment, and (4) rehabilitation. However, the wearable medical device industry currently faces several important limitations that prevent further use of wearable technology in medical practice, such as difficulties in achieving user-friendly solutions, security and privacy concerns, the lack of industry standards, and various technical bottlenecks.ConclusionsWe predict that with the development of science and technology and the popularization of personalized health concepts, wearable devices will play a greater role in the field of health care and become better integrated into people’s daily lives. However, more research is needed to explore further applications of wearable devices in the medical field. We hope that this review can provide a useful reference for the development of wearable medical devices.

Wearable healthcare technologies enable continuous monitoring of wearers’ health status and the implementation of preventive measures that significantly improve their health. In recent years, the popularity of wearable fitness devices has skyrocketed. Privacy concerns are a significant impediment to more people using these devices. This study aims to understand the moderating role of privacy concerns on users’ intentions to use wearable fitness technologies. The theoretical model for this study integrates the UTAUT2 framework and privacy concerns as a second-order model. The study follows a quantitative research approach, using a questionnaire to collect data. The PLS-SEM model was used to test the theoretical model. The integrated model with the indirect effect of privacy concerns explained higher variance in predicting the behavioral intention to use wearable fitness devices. Price value, performance expectancy, habit, and facilitating conditions all had a significant influence on users’ decisions to use wearable fitness devices, while privacy concerns moderated the relationship between UTAUT2 constructs for behavioral intention to use these devices. This study confirmed that the UTAUT2 model could be extended to explain the initial adoption of smart wearable fitness devices. Prior studies investigated the intention to use smart wearable devices, but few addressed privacy concerns associated with wearable fitness devices. This study addressed this gap by investigating the moderating role of privacy concerns on the intention to use wearable fitness devices. The integrated theoretical model developed uncovers users’ privacy concerns related to these systems.

An internal etching for three‐dimensional structures (ETS) antenna capable of operating in the 810–960, 1370–1450 and 1710–2630 MHz bands, respectively, for long‐term evolution (LTE)/wideband code division multiple access (WCDMA)/2.4 GHz industrial, scientific and medical (ISM) band operations suitable for being embedded within a smart wrist wearable wireless mobile communication device is proposed. The proposed antenna radiation efficiency can achieve 37.8–55.3% from 810–960 MHz, 53.1–92.63% from 1370–1450 MHz and 52.9–93.1% from 1710–1630 MHz for LTE/WCDMA bands and 87.4% for ISM band without the hand phantom in the free space condition. The antenna radiation efficiency of the LTE/WCDMA and ISM bands is reduced by half because of the hand phantom with lossy material properties. Furthermore, the health risk related to exposure to electromagnetic radiation, the specific absorption rates of the device are evaluated and the results were far below the limits required by the Federal Communications Commission (FCC) organisation. The embedded three‐dimensional (3D) multi‐band antenna can be adapted for the smart wrist wearable wireless mobile communication device. This study breaks the pattern that wearable devices must work with mobile phones. The wearable device proposed herein can directly communicate with the carrier base station independently.

As we know if anyone falls from a narrow height it is very critical especially for the people who are under 10 years or above 60 years of age. The age group under 10 years is very sensitive, if any child who falls can face injuries which will affect them in terms of mental and physical disability or fatal injuries can also be caused in some cases. Similar for the age group of above 60 is also crucial if they got any kind of injury due to falling and by the time if they didn’t get any medical treatment so it will cause a decisive loss for that person. In the present era we are living in the age of 4G and 3D technology which means in this era everyone is connected with each other by means of smart phone and internet, also the smart wearable devices, which will be connected with human beings and send a kind of information signal through the medium of internet which is commonly known as Internet of Things (IoT). According to this kind of technology we are able to rectify the injury causing by collapsing on the ground. In this paper, we proposed an energy-efficient smart wearable IoT device which is able to detect the motion of human body and based on that it will detect the collapsing motion and immediately take action by sending data to the cloud server via internet which will immediately send an SMS to the concerned person. Apart from that we also introduce one more feature which is panic alert, so if anyone who need any emergency support, this wearable device will send an SMS to the concerned person. Here we used ESP8266-01 for the communication channel, for the calculation of body motion we use ADXL335 and for the reduction of power we used ATTINY85 which will reduce the ample of energy and utilize the power intelligently which results in the long life of 220 mAh battery. Its battery back-up is approximately up to 3000 h which is extremely far better than all the previous existing approaches.

Application of video behavior fast detection based on wearable motion sensor devices in sports training