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Wearable Technology for Smart Manufacturing in Industry 5.0
Abstract
The innovation of wearable Internet of Things devices has fuelled the transition from Industry 4.0 to Industry 5.0. Increasing resource efficiency, safety, and economic efficiency are some of the main goals of Industry 5.0. Herein, wearable Internet of Things devices is parallel to humans to optimize human tasks and meet a new Industry’s requirements. Integrating artificial intelligence algorithms and IoT into wearable technologies and the progress of sensors has created significant innovations in many fields, such as manufacturing, health, sports, etc.. However, wearable technologies have faced challenges and difficulties such as security, privacy, accuracy, latency, and connectivity. More specifically, the increasingly massive and complex data volume has dramatically influenced the improvement of the limits. However, these challenges have created a new solution: the federated Learning algorithm. In recent years, federated learning has been implemented with deep learning and AI to enhance powerful computing with big data, stable accuracy, and ensure the security of edge devices. In this chapter, the first objective is to survey the applications of wearable Internet of Things devices in industrial sectors, particularly in manufacturing. Second, the challenges of wearable Internet of Things devices are discussed. Finally, this chapter provides case studies applying machine learning, deep learning, and federated learning in fall and fatigue classification. These cases are the two most concerning work efficiency and safety topics in Smart Manufacturing 5.0.KeywordsWearable technologySmart ManufacturingIndustry 5.0
... Activities that involve machine operations can make use of a wide range of technologies. This includes the use of collaborative robots to perform more tedious tasks [102], the implementation of digital models to streamline operations and avoid delays in decisionmaking [103], and the use of wearables to track work activity [104]. In material movement, the use of electronic beacons allows for real-time data collection [104]. ...
... This includes the use of collaborative robots to perform more tedious tasks [102], the implementation of digital models to streamline operations and avoid delays in decisionmaking [103], and the use of wearables to track work activity [104]. In material movement, the use of electronic beacons allows for real-time data collection [104]. These data, along with technologies like cloud computing and big data, can be used to improve working conditions [105]. ...
This paper delves into the concept of social metabolism as a foundation for the development of sociotechnical systems in Industry 5.0. The study conducts an analysis of the existing methods and approaches for designing sociotechnical systems, and reviews publications that utilize such systems to incorporate Industry 4.0 technologies into manufacturing processes. Additionally, it examines the three key factors of Industry 5.0 and the enabling framework of Industry 4.0 technologies. Based on these investigations, a theoretical model is proposed for manufacturing system design, employing sociotechnical systems to integrate Industry 4.0 enabling technologies, while considering the essential aspects of Industry 5.0. The model emphasizes the early consideration of sociotechnical systems to design manufacturing systems that prioritize human-centricity, sustainability, and resilience. By embracing this comprehensive approach, the proposed model contributes to the realization of a production environment aligned with societal needs, fostering a more conscious and adaptable industry.
... This evaluation was conducted to assess the model's performance in accurately interpreting EMG signals and translating them into effective mechanical actions for enhanced mobility. The experiments were designed to rigorously test the model's classification accuracy, precision, recall, and F1-score across different movement types (Palacio et al., 2024;Omarov et al., 2016;Nguyen et al., 2023;Tursynova et al., 2022). Additionally, visual representations such as confusion matrices, spectrograms, and training-validation accuracy and loss curves are provided to offer a comprehensive understanding of the system's capabilities and its practical applicability in real-world scenarios. ...
This research paper investigates the application of deep learning techniques for enhancing the control of lower limb exoskeletons through the classification of electromyography (EMG) signals. Utilizing convolutional neural networks (CNNs) and recurrent neural networks (RNNs), this study aims to improve the precision and adaptability of exoskeletons used in rehabilitation, particularly in orthopedics and sports medicine. The methodology involves collecting EMG data from various leg movements, which are then processed using advanced signal preprocessing techniques to enhance classification accuracy. The deep learning models are trained and validated with this data, demonstrating significant improvements in movement detection and device responsiveness. Results from the study indicate that the integration of deep learning models not only offers enhanced control over exoskeletons but also ensures more natural and efficient user interactions. This research highlights the potential of integrating sophisticated computational models into rehabilitative devices, paving the way for future advancements that could significantly improve therapeutic outcomes and quality of life for individuals with mobility impairments. The findings underscore the importance of continued innovation in the field of assistive technology, suggesting pathways for further research in multi-sensor integration and adaptive control systems. Keywords: deep Learning, electromyography (EMG), sports rehabilitation, lower limb exoskeletons, movement classification, neural networks, assistive robotics.
... The results of the comprehensive analysis of MoCap technology in construction are crucial within the framework of Industry 4.0 and 5.0 [121,122]. Integrating digital transformation and human-centered innovation improves efficiency and safety in the construction workplace [123][124][125]. The study emphasizes the importance of the use of MoCap systems to assess and enhance ergonomic safety and worker well-being as part of the transition to Industry 5.0, aiming to prioritize human-centric practices and align human skills with technical advancements. ...
This research explores the integration of human-centered edge artificial intelligence (AI) and wearable technology to enhance workplace health and safety within Industry 5.0. It highlights the importance of real-time monitoring and analysis facilitated by wearable devices equipped with sensors to measure physiological and environmental parameters, which help prevent hazards and accidents. Leveraging the Industrial Internet of Things (IIoT), these wearable technologies continuously track worker health and environmental conditions, promoting proactive hazard prevention and improving workplace efficiency. The role of edge AI is emphasized for its ability to enable immediate decision-making and reduce latency by processing data closer to its source, thereby enhancing worker safety and productivity while addressing ethical concerns related to privacy and security. The potential of human-centered edge AI and wearable technology to foster a collaborative and sustainable industrial environment is significant, though challenges such as limited computational resources and battery life constraints require ongoing research and development. This comprehensive analysis underscores the transformative impact of these technologies on workplace health and safety and the necessity for innovative solutions to overcome current limitations, aiming to improve worker well-being and boost industrial efficiency and productivity.
The integration of Human-Centered Edge Artificial Intelligence (HCE-AI) in smart factories within the framework of Industry 5.0. Industry 5.0 emphasizes collaboration between humans and advanced technologies to create sustainable, people-centered production environments. The vast amount of data generated by factory sensors presents opportunities for machine learning models to enhance predictive maintenance. However, challenges such as latency, cybersecurity risks, and the lack of transparency in AI decision-making persist. This chapter highlights research perspectives like Explainable AI (XAI), Federated Learning, Self-supervised Learning, 1-bit machine, and advanced quantization techniques to address these issues. With its ability to process data locally, Edge AI reduces latency and enhances real-time response capabilities. This article also discusses the role of Digital Twins in optimizing performance and predictive maintenance. By leveraging these technologies, smart factories can achieve higher efficiency, safety, and sustainability, thus aligning with the human-centric in the 5.0 revolution.
Flexible wearable sensors, characterized by their stretchability, flexibility, lightweight, high sensitivity, and the ability to dynamically and continuously measure multiple parameters, have been widely applied in areas such as physiological health monitoring, human-computer interaction control, real-time motion monitoring, and personalized medical assistance. With the development of artificial intelligence technology, the integration of flexible wearable sensors with artificial intelligence technologies such as machine learning has become increasingly profound. This integration has facilitated the emergence and development of intelligent sensing systems with human-like perception capabilities, garnering growing attention towards flexible wearable intelligent sensing technology. This paper focuses on introducing various sensor mechanisms, intelligent sensing algorithms, and practical applications of flexible wearable intelligent sensing technology. The challenges and possible development directions of flexible wearable smart sensing technology, encompassing aspects such as power supply, multimodal sensing capability, preparation materials, processing and fabrication techniques, and smart sensing algorithms, are also discussed and prospected. Such a review can provide a reference for research in the field of flexible wearable smart sensing technology.
Malicious assaults and information leakage have grown in importance in practically every area of information and communication technology (ICT) in recent years. Enterprise ledger organization, preservation, security, and protection are all greatly aided by information security (IS). To maintain their positions in the market, industries must safeguard their data and other vital assets. The main goals of this paper’s systematic review, which covers the entire process of privacy protection and security, are to introduce cryptographic IS for Industry 5.0 (the Fifth Industrial Revolution) as a comprehensive solution that provides regulatory compliance and IS policies in addition to the industrial goals. The review covers the four main areas: (i) recognizing the need for frameworks to be developed for a secure Industry 5.0 lifecycle; (ii) emphasizing security guidelines, procedures, and countermeasures for Industry 5.0; (iii) applying control, access, and availability in real-time; and (iv) proposing a futuristic architecture for industrial information security. Furthermore, the analysis of previously published state-of-the-art techniques is presented in this paper. These techniques survive with various limitations and challenges, including intercommunication exchange, fine-grained control access, and interconnectivity-related issues, which affect the adoption of an IS in industrial programmes. At the end of this systematic review, we investigated a few open research problems and mentioned those that are involved in the design of cryptographic re-encryption-enabled IS for future Industry 5.0 developments.
The Internet of Things (IoT) has modified our daily lives. This study examines the relationship between behavioural intention towards adoption of IoT wearable devices (WIoT) in the context of social norms, privacy, security concerns, and trust. The current study consists of a review of the literature for constructing the theoretical framework of the study and subsequently, survey was conducted from 200 respondents from Saudi Arabia was conducted between 2020 and 2020. The statistical results illustrate that the proposed model fits well for the current study and conclude that perceived privacy risks and health awareness have a significant impact on the intention to adopt wearable devices, whereas the social norms have a negative effect on the privacy and security concerns towards the use of WIoT devices. The study also helps to improve the predictability of user intention to use IoT devices
Purpose: We examined the movement, physiological and muscle function responses to running with and without (i.e. linear) multiple directional changes to understand which measures of external demands better reflected changes in the internal response. Methods: Twelve team sport athletes completed a linear and multidirectional running trial during which movement characteristics, oxygen consumption (V˙O2), blood lactate (B[La]) and heart rate (HR) were measured. Isometric peak torque of knee extensors and flexors was also assessed before and after each trial. Results: High speed running distance was higher during the linear trial (p < 0.001), whereas time at high metabolic power (p = 0.046), number of accelerations (p < 0.001), summated HR (p = 0.003) and B[La] (p = 0.002) were higher during the multidirectional trial. Integrated external to internal ratios of high-speed running: summated HR and high-speed running: total V˙O2 were different between multidirectional and linear trials (p ≤ 0.001). Conversely, high metabolic power: summated HR and high metabolic power: total V˙O2 were similar (p ≥ 0.246). Small decrements in knee flexor (p = 0.003) and extensor torque (p = 0.004) were observed after both trials. Conclusion: Time at high metabolic power better reflects the increased internal response during running with more directional changes than high speed running.
The last decades have witnessed the rapid growth of advanced technologies and their application which has a significant influence on industrial manufacturing, leading to smart manufacturing (SM). The recent development of information and communication technologies has engendered the concept of the smart factory that adds intelligence into the manufacturing process to drive continuous improvement, knowledge transfer, and data-based decision-making. The Industrial Internet of Things (IIoT) is one of the main technologies used to enable smart factories, which is specific to industrial applications. IIoT is about connecting all the industrial assets, including machines and control systems, with the information systems and the business processes. A huge volume of data collected can feed real-time analytic solutions provided by Artificial Intelligence (AI), Big Data Analytics, and Decision Support Systems (DSS), which can lead to optimal industrial operations. In SM, based on modern technologies of the IIoT, the process of collecting, transforming, and storing data from all stages of the production process becomes easier and more efficient, promoting the era of Big Data in manufacturing. AI algorithms provide powerful tools for exploiting the wealth of data generated in the IIoT. By extracting useful information and features from Big Data, the AI algorithms allow complex tasks such as predicting, maintaining, monitoring, and optimizing the production process to be performed smartly and efficiently. To combine human knowledge with these above results, DSS is integrated to help manufacturers manage data, analytics, modeling, and planning effectively. This chapter aims to provide a survey of the key techniques that enable SM, including IIoT, Big Data, DSS, and AI. Several important perspectives for the decentralized techniques in SM will be discussed. There are two illustrative examples demonstrated, the source code can be found https://github.com/tungnhitran/Anomaly-Detection-in-Smart-Manufacturing.
Over the past years, cars' computing, sensing, and networking capabilities have rapidly increased, and the automotive development aims for autonomous driving. However, the driver is still the focal point for decision making. It has to be alert at all times to avoid traffic accidents due to human factors like tiredness, inattentiveness, and intoxication. Therefore, there is a need for a system that monitors the driver and intervenes before human failure can have a negative impact on traffic. A variety of commercially available wearable IoT devices, such as smartwatches, bracelets, and rings, are capable of monitoring human health conditions. However, those devices come with technological differences and manufacturer-specific implementations. This paper proposes a prototype for a human-sensing and health monitoring system based on wearable sensor devices. The aim is to find a solution that ignores the technological heterogeneity of IoT devices and generalises their implementation into the automotive system. Consequently, the data should be available to be analysed together with the data collected from the vehicular sensors. Our solution is compatible with open-source platforms Eclipse Hono and Kuksa.
Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases’ diagnosis, and it can play a great role in elderly care and patient’s health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.
Out-of-office blood pressure (BP) measurement is considered an integral component of the diagnostic algorithm and management of hypertension. In the era of digitalization, a great deal of wearable BP measuring devices has been developed. These digital blood pressure monitors allow frequent BP measurements with minimal annoyance to the patient while they do promise radical changes regarding the diagnostic accuracy, as the importance of making an accurate diagnosis of hypertension has become evident. By increasing the number of BP measurements in different conditions, these monitors allow accurate identification of different clinical phenotypes, such as masked hypertension and pathological BP variability, that seem to have a negative impact on cardiovascular prognosis. Frequent measurements of BP and the incorporation of new features in BP variability, both enable well-rounded interpretation of BP data in the context of real-life settings. This article is a review of all different technologies and wearable BP monitoring devices.
With the widespread use of intelligent teaching, data containing student performance information continues to emerge, and artificial intelligence technology based on big data has made a qualitative leap. At present, the prediction of college students’ sports performance is only based on the past performance, and it does not reflect the student’s training effect very well. In order to solve these problems, this paper puts forward the analysis and simulation of college sports performance fusion with adaptive federated deep learning algorithm, aiming to study the influencing factors of student sports performance and suggestions for improvement. This paper uses an adaptive federated learning method and a personalized federated learning algorithm based on deep learning and then proposes a student performance prediction method. These methods integrate the quantitative methods of motor skill assessment and establish standards for college students, which are good standards for evaluating college students’ sports skills. This paper adopts the performance prediction framework and then establishes the sports performance prediction model. Through the analysis of sports performance analysis examples, it is concluded that the model proposed in this paper can accurately predict the student’s sports performance, and the average accuracy rate of each sports item has reached 91.7%.
The term human digital twin has recently been applied in many domains, including medical and manufacturing. This term extends the digital twin concept, which has been illustrated to provide enhanced system performance as it combines system models and analyses with real-time measurements for an individual system to improve system maintenance. Human digital twins have the potential to change the practice of human system integration as these systems employ real-time sensing and feedback to tightly couple measurements of human performance, behavior, and environmental influences throughout a product’s life cycle to human models to improve system design and performance. However, as this concept is relatively new, the literature lacks inclusive and precise definitions of this concept. The current research reviews the literature on human digital twins to provide a generalized structure of these systems, provide definitions of a human digital twin and human digital twin system, and review the potential applications of these systems within product design, development, and sustainment. This review of the existing literature suggests that components of human models sufficient to provide robust human digital twins are likely to be derived across multiple fields of study. Thus, development of these systems would benefit an open multi-disciplinary research effort.
Every day, vehicle accidents occur and many of them might be avoided if the drivers demonstrated excellent driving without mistakes. This paper presents a novel prototype applied in a real transportation system, particularly for buses, to avoid accidents, which may involve numerous victims, and even occasionally cause death. This system consists of a wearable device and embedded system with several sensors connected via Bluetooth, similar to the Internet of Things (IoT). Wearable devices are made to monitor the driver's heart rate and alert the driver if they are in a state of sleep deprivation to prevent any potential accidents. The embedded system includes a Global Positioning System (GPS), accelerometers, and gyroscopes attached to a Smart Box mounted on the bus. The embedded system alert function will be triggered if an accident occurs and automatically sends the geolocation of the accident to the registered phone number through a message using a mobile phone. The results for all scenarios were significant when measured by an automatic accident trigger via the smart box if the value of measured values in each axis exceeded 583. In conclusion, the implementation of this innovative solution at the system-level was shown to be satisfactory in terms of the safety mechanism used by the nominated drivers.
Low back pain (LBP) is a leading contributor to musculoskeletal injury worldwide and carries a high economic cost. The healthcare industry is the most burdened, with nurses, in particular, being highly prone to LBP. Wearable technologies have the potential to address the challenges of monitoring postures that contribute to LBP and increase self-awareness of workplace postures and movements. We aimed to gain insight into workers’ perceptions of LBP and whether they would consider using wearable monitoring technologies to reduce injury risks. We conducted a cross-sectional survey to gather information from a selected population of nurses. Sixty-four participants completed the survey, and data were analyzed with the support of Machine Learning techniques. Findings from this study indicate that the surveyed population (64 nurses) is interested in these new approaches to monitor movement and posture in the workplace. This technology can potentially change the way ergonomic guidelines are implemented in this population.
Mixed reality as an emerging technology can improve users’ experience. Using this technology, people can interact between virtual objects and the real world. Mixed reality has enormous potential for enhancing the human cyber—physical system for different manufacturing functions: planning, designing, production, monitoring, quality control, training, and maintenance. This study aims to understand the existing development of mixed reality technology in manufacturing by analysing patent publications from the InnovationQ-Plus database. Evaluations of trends in this developing technology have focused on qualitative literature reviews and insufficiently on patent technology analytics. Patents connected to mixed reality will be mapped to give technology experts a better grasp of present progress and insights for future technological development in the industry. Thus 709 patent publications are systematically identified and analysed to discover the technological trends. In addition, we map existing patent publications with manufacturing functions, and illustrate this with a technology function matrix. Finally, we identify future research in human cyber—physical system development by enhancing different human senses to give users more sensations while interacting with virtual objects. We provide insight into this human cyber—physical system development in three industries: automotive, food and beverage, and textiles.
Diabetes and its complications have become a worldwide concern that influences human health negatively and even leads to death. The real-time and convenient glucose detection in biofluids is urgently needed. Traditional glucose testing is detecting glucose in blood and is invasive, which cannot be continuous and results in discomfort for the users. Consequently, wearable glucose sensors toward continuous point-of-care glucose testing in biofluids have attracted great attention, and the trend of glucose testing is from invasive to non-invasive. In this review, the wearable point-of-care glucose sensors for the detection of different biofluids including blood, sweat, saliva, tears, and interstitial fluid are discussed, and the future trend of development is prospected.
Deep learning plays a vital role in classifying different arrhythmias using electrocardiography (ECG) data. Nevertheless, training deep learning models normally requires a large amount of data and can lead to privacy concerns. Unfortunately, a large amount of healthcare data cannot be easily collected from a single silo. Additionally, deep learning models are like black-box, with no explainability of the predicted results, which is often required in clinical healthcare. This limits the application of deep learning in real-world health systems.
In this paper, to address the above-mentioned challenges, we design a novel end-to-end framework in a federated setting for ECG-based healthcare using explainable artificial intelligence (XAI) and deep convolutional neural networks (CNN). The federated setting is used to solve challenges such as data availability and privacy concerns. Furthermore, the proposed framework effectively classifies different arrhythmias using an autoencoder and a classifier, both based on a CNN. Additionally, we propose an XAI-based module on top of the proposed classifier for interpretability of the classification results, which helps clinical practitioners to interpret the predictions of the classifier and to make quick and reliable decisions. The proposed framework was trained and tested using the baseline Massachusetts Institute of Technology - Boston’s Beth Israel Hospital (MIT-BIH) Arrhythmia database. The trained classifier outperformed existing work by achieving accuracy up to 94.5% and 98.9% for arrhythmia detection using noisy and clean data, respectively, with five-fold cross-validation. We also propose a new communication cost reduction method to reduce the communication costs and to enhance the privacy of users’ data in the federated setting. While the proposed framework was tested and validated for ECG classification, it is general enough to be extended to many other healthcare applications.
Multiple and diverse factory digital twins have been proposed in the literature. However, despite the recognized growing importance of workers in smart and autonomous industrial settings, such models still lack or oversimplify human representation. Human digital twins must include human monitoring and behavioural data and models based on psychophysical status, knowledge, skills, and personal needs to manage production systems that aim, at the same time, to achieve process performance and workers’ wellbeing. This paper proposes a metamodel based on data, events, and connectors that supports the modular composition of tailored human digital twins. This work also addresses an industrial application of the metamodel for preliminary validation.
Wearable smart devices are widely used to determine various physico-mechanical parameters at chosen intervals. The proliferation of such devices has been driven by the acceptance of enhanced technology in society. Despite the exponential growth of wearable sensors, there are limitations in terms of the broader aspects of their commercialized uses, which need improvement to further enhance the field of wearable electronics. The Internet of Things (IoT) is responsible for connecting smart objects around the world and has led to the proliferation of these smart objects over the last two decades. This Special Issue aims to present the issues and challenges faced by the currently proposed IoT-based systems in addition to state-of-the-art research on the com-mercialization of the current systems. This editorial summarizes the published papers in this Special Issue on Wearable Sensors and Systems on the Internet of Things theme of applications. The global market for Internet of things (IoT) technology reached USD 100 billion for the first time in 2017, with a forecast to further grow to around USD 1.6 trillion by 2025, according to [1]. The global internet of things in the healthcare market was valued at USD 113.75 billion in 2019 and is expected to reach USD 332.67 billion by 2027, registering a compound annual growth-rate of 13.20% from 2020 to 2027 according to [2]. Human-activity monitoring is a vibrant area of research with the potential for commercial development, which is discussed by Mukhopadhyay [3]. It is expected that many more lightweight, robust, and efficient wearable devices will be available for monitoring a wide range of activities. The development of lightweight physiological sensors will lead to the formation of comfortable wearable systems to monitor different ranges of inhabitant-related activities. Formal and informal surveys predict an increase in the interest in, and consequently usage of, wearable devices in the near future, where the cost of such devices is expected to fall with an increased use. Wearable devices have been a state-of-the-art technology and associated with IoT for quite some time. The full range of new capabilities of these IoT-based wearable sensors has been realized every day for their pervasive connectivity. Traditional remote healthcare information systems involve data transfer, signal processing mechanisms , and naive machine learning models deployed on remote servers to process the medical data of patients. Some of the shortcomings of this technique include an un-suitability for resource-constrained wearable IoT devices. Resources such as processing , memory, energy, and networking capability are limited in wearable IoT (WIoT) devices. Citation: Mukhopadhyay, S.C.; Suryadevara, N.K.; Nag, A. Wearable Sensors and Systems in the IoT. Sensors 2021, 21, 7880.
Background
Falls are a common and serious health issue facing the global population, causing an estimated 646,000 deaths per year globally. Wearable devices typically combine accelerometers, gyroscopes and even barometers; using the data collected and inputting this into an algorithm that decides whether a fall has occurred. The purpose of this umbrella review was to provide a comprehensive overview of the systematic reviews on the effectiveness of wearable electronic devices for falls detection in adults.
Methods
MEDLINE, Embase, Cochrane Database of Systematic Reviews (CDSR), and CINAHL, were searched from their inceptions until April 2019 for systematic reviews that assessed the accuracy of wearable technology in the detection of falls.
Results
Seven systematic reviews were included in this review. Due to heterogeneity between the included systematic reviews in their methods and their reporting of results, a meta-analysis could not be performed. Most devices tested used accelerometers, often in combination with gyroscopes. Three systematic reviews reported an average sensitivity of 93.1% or greater and an average specificity of 86.4% or greater for the detection of falls. Placing sensors on the trunk, foot or leg appears to provide the highest accuracy for falls detection, with multiple sensors increasing the accuracy, specificity, and sensitivity of these devices.
Conclusions
This review demonstrated that wearable device technology offers a low-cost and accurate way to effectively detect falls and summon for help. There are significant differences in the effectiveness of these devices depending on the type of device and its placement. Further high-quality research is needed to confirm the accuracy of these devices in frail older people in real-world settings.
Wearable and Internet of Things (IoT) technologies in sports open a new era in athlete’s training, not only for performance monitoring and evaluation but also for fitness assessment. These technologies rely on sensor systems that collect, process and transmit relevant data, such as biomarkers and/or other performance indicators that are crucial to evaluate the evolution of the athlete’s condition, and therefore potentiate their performance. This work aims to identify and summarize recent studies that have used wearables and IoT technologies and discuss its applicability for fitness assessment. A systematic review of electronic databases (WOS, CCC, DIIDW, KJD, MEDLINE, RSCI, SCIELO, IEEEXplore, PubMed, SPORTDiscus, Cochrane and Web of Science) was undertaken according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 280 studies initially identified, 20 were fully examined in terms of hardware and software and their applicability for fitness assessment. Results have shown that wearable and IoT technologies have been used in sports not only for fitness assessment but also for monitoring the athlete’s internal and external workloads, employing physiological status monitoring and activity recognition and tracking techniques. However, the maturity level of such technologies is still low, particularly with the need for the acquisition of more—and more effective—biomarkers regarding the athlete’s internal workload, which limits its wider adoption by the sports community.
Ecological environments research helps to assess the impacts on forests and managing forests. The usage of novel software and hardware technologies enforces the solution of tasks related to this problem. In addition, the lack of connectivity for large data throughput raises the demand for edge-computing-based solutions towards this goal. Therefore, in this work, we evaluate the opportunity of using a Wearable edge AI concept in a forest environment. For this matter, we propose a new approach to the hardware/software co-design process. We also address the possibility of creating wearable edge AI, where the wireless personal and body area networks are platforms for building applications using edge AI. Finally, we evaluate a case study to test the possibility of performing an edge AI task in a wearable-based environment. Thus, in this work, we evaluate the system to achieve the desired task, the hardware resource and performance, and the network latency associated with each part of the process. Through this work, we validated both the design pattern review and case study. In the case study, the developed algorithms could classify diseased leaves with a circa 90% accuracy with the proposed technique in the field. This results can be reviewed in the laboratory with more modern models that reached up to 96% global accuracy. The system could also perform the desired tasks with a quality factor of 0.95, considering the usage of three devices. Finally, it detected a disease epicenter with an offset of circa 0.5 m in a 6 m × 6 m × 12 m space. These results enforce the usage of the proposed methods in the targeted environment and the proposed changes in the co-design pattern.
Objective
To evaluate muscle activity in the arm of adult stroke survivors with limited or no arm movement during acute care.
Design
Prospective observational study.
Setting
Acute Care Regional Stroke Center
Participants
We recruited adults who had a stroke within the last five days who were admitted to a level one trauma hospital and had an NIH Stroke Scale score at the time of recruitment greater than one for arm function. Twenty-one (21) adults (13 male/8 female) with an average age of 60±15 years were recruited an average of 3±1 days after their stroke. Eleven (7 male/4 female, age 56±11 years) had no observable or palpable arm muscle activity (Manual Muscle Test, MMT=0) and ten (6 male/4 female, age 64±1 years) had detectable activity (MMT>0).
Interventions
Dual mode sensors (electromyography and accelerometry) were placed on the anterior deltoid, biceps, triceps, wrist extensors, and wrist flexors of the impaired arm.
Main Outcome Measure(s)
The number of muscle contractions, as well as average duration, amplitude, and co-contraction patterns were evaluated for each participant.
Results
Muscle contractions were observed in all five muscles for all participants using EMG recordings. Contractions were easily identified from 30-minutes of monitoring for participants with MMT>0, but up to three hours of monitoring was required for participants with MMT=0 to detect contractions in all five muscles during standard care. Only the wrist extensors demonstrated significantly larger amplitude contractions for participants with MMT>0 than MMT=0. Co-contraction was rare, involving less than 10% of contractions. Co-contraction of two muscles most commonly aligned with the flexor synergy pattern commonly observed after stroke. For participants with MMT=0, number of contractions and maximum amplitude were moderately correlated with MMT scores at follow-up.
Conclusion
Muscle activity was detected with surface EMG recordings during standard acute care, even for individuals with no observable activity by clinical exam. Wearable sensors may be useful for monitoring early muscle activity and movement after stroke.
Today, ensuring work safety is considered to be one of the top priorities for various industries. Workplace injuries, illnesses, and deaths often entail substantial production and financial losses, governmental checks, series of dismissals, and loss of reputation. Wearable devices are one of the technologies that flourished with the fourth industrial revolution or Industry 4.0, allowing employers to monitor and maintain safety at workplaces. The purpose of this article is to systematize knowledge in the field of industrial wearables’ safety to assess the relevance of their use in enterprises as the technology maintaining occupational safety, to correlate the benefits and costs of their implementation, and, by identifying research gaps, to outline promising directions for future work in this area. We categorize industrial wearable functions into four classes (monitoring, supporting, training, and tracking) and provide a classification of the metrics collected by wearables to better understand the potential role of wearable technology in preserving workplace safety. Furthermore, we discuss key communication technologies and localization techniques utilized in wearable-based work safety solutions. Finally, we analyze the main challenges that need to be addressed to further enable and support the use of wearable devices for industrial work safety.
Wearable devices have found numerous applications in healthcare ranging from physiological diseases, such as cardiovascular diseases, hypertension and muscle disorders to neurocognitive disorders, such as Parkinson’s disease, Alzheimer’s disease and other psychological diseases. Different types of wearables are used for this purpose, for example, skin-based wearables including tattoo-based wearables, textile-based wearables, and biofluidic-based wearables. Recently, wearables have also shown encouraging improvements as a drug delivery system; therefore, enhancing its utility towards personalized healthcare. These wearables contain inherent challenges, which need to be addressed before their commercialization as a fully personalized healthcare system. This paper reviews different types of wearable devices currently being used in the healthcare field. It also highlights their efficacy in monitoring different diseases and applications of healthcare wearable devices (HWDs) for diagnostic and treatment purposes. Additionally, current challenges and limitations of these wearables in the field of healthcare along with their future perspectives are also reviewed.
Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected “things” and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions.
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies, and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, “big data.” Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges, and privacy issues in federated learning, and point out the implications and potentials in healthcare.
This study empirically examined the effects of internal and external factors on actual use behavior, health improvement expectancy, and continuous use intention of healthcare wearable devices. The study proposed a research model with its associated hypotheses that were tested using structural equation modeling. We also performed a comparative analysis of the two sample groups (medical personnel and general public), based on data collected from 288 healthcare wearable devices/apps users. The findings of the study indicated that internal and external factors have positive effects on actual use behavior, and health improvement expectancy and continuous use intention of healthcare wearable devices can be promoted through actual use behavior. The comparative analysis of the two groups showed that medical personnel had higher relationships among the study factors than general public. The study results shed theoretical and practical implications regarding how healthcare wearable devices or apps can be effectively used for disease prevention and health management for the users.
Personal mobile devices such as smartwatches, smart jewelry, and smart clothes have launched a new trend in the Internet of Things (IoT) era, namely the Internet of Wearable Things (IoWT). These wearables are small IoT devices capable of sensing, storing, processing, and exchanging data to assist users by improving their everyday life tasks through various applications. However, the IoWT has also brought new challenges for the research community to address such as increasing demand for enhanced computational power, better communication capabilities, improved security and privacy features, reduced form factor, minimal weight, and better comfort. Most wearables are battery-powered devices that need to be recharged-therefore, the limited battery life remains the bottleneck leading to the need to enhance the energy efficiency of wearables, thus, becoming an active research area. This paper presents a survey of energy-efficient solutions proposed for diverse IoWT applications by following the systematic literature review method. The available techniques published from 2010 to 2020 are scrutinized, and the taxonomy of the available solutions is presented based on the targeted application area. Moreover, a comprehensive qualitative analysis compares the proposed studies in each application area in terms of their advantages, disadvantages, and main contributions. Furthermore, a list of the most significant performance parameters is provided. A more in-depth discussion of the main techniques to enhance wearables' energy efficiency is presented by highlighting the trade-offs involved. Finally, some potential future research directions are highlighted. INDEX TERMS Wearables, Internet of Wearable Things, energy consumption, wearable applications, energy efficiency, computing, systematic literature review.
Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson's disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging wearable devices and machine-learning methods to develop and evaluate automated detection of FOG and quantification of its severity. Seventy-one subjects with FOG completed a FOG-provoking test while wearing three wearable sensors (lower back and each ankle). Subjects were videotaped before (OFF state) and after (ON state) they took their antiparkinsonian medications. Annotations of the videos provided the “ground-truth” for FOG detection. A leave-one-patient-out validation process with a training set of 57 subjects resulted in 84.1% sensitivity, 83.4% specificity, and 85.0% accuracy for FOG detection. Similar results were seen in an independent test set (data from 14 other subjects). Two derived outcomes, percent time frozen and number of FOG episodes, were associated with self-report of FOG. Bother derived-metrics were higher in the OFF state than in the ON state and in the most challenging level of the FOG-provoking test, compared to the least challenging level. These results suggest that this automated machine-learning approach can objectively assess FOG and that its outcomes are responsive to therapeutic interventions.
Background:
In the face of an aging society, an immediate and preventive medical system urgently needs to be established, and the application of wearable devices is essential. However, the application of smart medical care in Taiwan is still not widespread, and few studies have explored the related issues of wearable medical device usage. Thus, determining the success of a wearable medical device mainly depends on the degree of user adoption and use.
Objective:
The purpose of this study was to examine the factors that influence the intention to use wearable medical devices.
Methods:
This study applied the unified theory of acceptance and use of technology (UTAUT) to build a comprehensive model that explains intentions to use wearable medical devices.
Results:
The research findings showed that health consciousness and trust were the strongest predictors of intentions to use wearable medical devices.
Conclusions:
The results reveal the magnitudes of the impacts of the variables in a well-accepted revised UTAUT model in the context of the medical industry, particularly in the setting of wearable medical devices. Several important implications for academics and industry decision-makers can be formulated from these results.
The detection of activities of daily living (ADL) and the detection of falls is of utmost importance for addressing the issue of serious injuries and death as a consequence of elderly people falling. Wearable sensors can provide a viable solution for monitoring people in danger of falls with minimal external involvement from health or care home workers. In this work, we recorded accelerometer data from 35 healthy individuals performing various ADLs, as well as falls. Spatial and frequency domain features were extracted and used for the training of machine learning models with the aim of distinguishing between fall and no fall events, as well as between falls and other ADLs. Supervised classification experiments demonstrated the efficiency of the proposed approach, achieving an F1-score of 98.41% for distinguishing between fall and no fall events, and an F1-score of 88.11% for distinguishing between various ADLs, including falls. Furthermore, the created dataset, named “ShimFall&ADL” will be publicly released to facilitate further research on the field.
Lean Management focusses on the elimination of wasteful activities in production. Whilst numerous methods such as value stream analysis or spaghetti diagrams exist to identify transport, inventory, defects, overproduction or waiting, the waste of human motion is difficult to detect. Activity recognition attempts to categorize human activities using sensor data. Human activity recognition (HAR) is already used in the consumer domain to detect human activities such as walking, climbing stairs or running. This paper presents an approach to transfer the human activity recognition methods to production in order to detect wasteful motion in production processes and to evaluate workplaces. Using sensor data from ordinary smartphones, long-term short-term memory networks (LSTM) are used to classify human activities. Additional to the LSTM-network, the paper contributes a labeled data set for supervised learning. The paper demonstrates how activity recognition can be included in learning factory training starting from the generation of training data to the analysis of the results.
Recent advances in Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient's vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted for a long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors' limited computational capacity but also enabling the use of local closed‐loop energy optimization algorithms to increase the battery life. Furthermore, the patient's contextual information – including health and activity status – can be exploited to guide energy optimization algorithms more effectively. By incorporating the patient's contextual information, a desired quality of experience can be achieved by creating a dynamic balance between energy‐efficiency and measurement accuracy. We present a runtime distributed control‐based solution to find the most energy‐efficient system state for a given context while keeping the accuracy of the decision‐making process over a certain threshold. Our optimization algorithm resides in the fog layer to avoid imposing computational overheads to the sensor layer. Our solution can be extended to reduce the probability of errors in the data collection process to ensure the accuracy of the results. The implementation of our fog‐assisted control solution on a remote monitoring system shows a significant improvement in energy efficiency with a bounded loss in accuracy.
Containing outbreaks of infectious disease requires rapid identification of transmission hotspots, as the COVID-19 pandemic demonstrates. Focusing limited public health resources on transmission hotspots can contain spread, thus reducing morbidity and mortality, but rapid data on community-level disease dynamics is often unavailable. Here, we demonstrate an approach to identify anomalously elevated levels of influenza-like illness (ILI) in real-time, at the scale of US counties. Leveraging data from a geospatial network of thermometers encompassing more than one million users across the US, we identify anomalies by generating accurate, county-specific forecasts of seasonal ILI from a point prior to a potential outbreak and comparing real-time data to these expectations. Anomalies are strongly correlated with COVID-19 case counts and may provide an early-warning system to locate outbreak epicenters.
One Sentence Summary
Distributed networks of smart thermometers track COVID-19 transmission epicenters in real-time.
Increasing in elderly population put extra pressure on healthcare systems globally in terms of operational costs and resources. To minimize this pressure and provide efficient healthcare services, the application of the Internet of Things (IoT) and wearable technology could be promising. These technologies have the potential to improve the quality of life of the elderly population while reducing strain on healthcare systems and minimizing their operational cost. Although IoT and wearable applications for elderly healthcare purposes were reviewed previously, there is a further need to summarize their current applications in this fast-developing area. This paper provides a comprehensive overview of IoT and wearable technologies’ applications including the types of data collected and the types of devices for elderly healthcare. This paper provides insights into existing areas of IoT/wearable applications while presenting new research opportunities in emerging areas of applications, such as robotic technology and integrated applications. The analysis in this paper could be useful to healthcare solution designers and developers in defining technology supported futuristic healthcare strategies to serve elderly people and increasing their quality of life.
Smart wearables collect and analyze data, and in some scenarios make a smart decision and provide a response to the user and are finding more and more applications in our daily life. In this paper, we comprehensively survey the most recent and important research works conducted in the area of wearable Internet of Things (IoT) and classify the wearables into four major clusters: (i) health, (ii) sports and daily activity, (iii) tracking and localization, and (iv) safety. The fundamental differences of the algorithms associated within each cluster are grouped and analyzed and the research challenges and open issues in each cluster are discussed. This survey reveals that although Cellular IoT (CIoT) has many advantages and can bring enormous applications to IoT wearables, it has been rarely studied by the researchers. This article also addresses the opportunities and challenges related to implementing CIoT-enabled wearables.
Recent advances in technology have empowered the widespread application of cyber–physical systems in manufacturing and fostered the Industry 4.0 paradigm. In the factories of the future, it is possible that all items, including operators, will be equipped with integrated communication and data processing capabilities. Operators can become part of the smart manufacturing systems, and this fosters a paradigm shift from independent automated and human activities to human–cyber–physical systems (HCPSs). In this context, a Healthy Operator 4.0 (HO4.0) concept was proposed, based on a systemic view of the Industrial Internet of Things (IIoT) and wearable technology. For the implementation of this relatively new concept, we constructed a unified architecture to support the integration of different enabling technologies. We designed an implementation model to facilitate the practical application of this concept in industry. The main enabling technologies of the model are introduced afterward. In addition, a prototype system was developed, and relevant experiments were conducted to demonstrate the feasibility of the proposed system architecture and the implementation framework, as well as some of the derived benefits.
Existing ergonomic risk assessment tools require monitoring of multiple risk factors. To eliminate the direct observation, we investigated the effectiveness of an end-to-end framework that works with the data from a single wearable sensor. The framework is used to identify the performed task as the major contextual risk factor, and then estimate the task duration and number of repetitions as two main indicators of task intensity. For evaluation of the framework, we recruited 37 participants to complete 10 simulated work tasks in a laboratory setting. In testing, we achieved an average accuracy of 92% for task identification, 7.3% error in estimation of task duration, and 7.1% error for counting the number of task repetitions. Moreover, we showed the utility of the framework outputs in two ergonomic tools to estimate the risk of injury. Overall, we indicated the feasibility of using data from wearable sensors to automate the ergonomic risk assessment in workplaces.
The Internet of Things (IoT)-based intelligent medical system contains confidential and sensitive information of patients. Most of the data is related to the patient's medical information and physical information with a high degree of privacy. Therefore, the security and privacy of the data are very important. This study focuses on the privacy protection on information of data of patients during medical data sharing. First, the module structure of the IoT-based telemedicine system is analyzed. A lightweight and fine-grained retrieval and data sharing scheme is designed based on the Wireless Bodyarea Network (WBAN). The Ciphertext Policy Attribute Based Encryption (CP-ABE) scheme is improved mainly by incorporating a partial hidden encryption algorithm, and the improved one is defined as Attribute Partially Hidden Access Control (APHAC). Data users can only access the data when their attributes match the access policy formulated by the data owner. The simulation experiment factors into the storage overhead and computational overhead. The results show that when the number of attributes reaches 100, the APHAC scheme can save about 500ms compared with the traditional CP-ABE scheme. As the number of attributes increases, only this scheme is stable, so it is more suitable for mobile devices. It can be concluded that the encrypted access control strategy proposed in this study avoids the excessive expenditure of end users in computing and storage resources while enabling users to have flexible access control.
Among all the functions of the Internet of Things, healthcare applications are the most important. The internet of Things has played a vital role in the sharing of various medical resources making it an essential factor in the field of medicine. Without the applications, it may become challenging for the medical practice to achieve some objectives. Today, information is shared freely across different networks, making it easy for practitioners and institutions to work with the available resources and deliver on the medical needs of the society. IoT has made it possible to offer smart and effective healthcare services to people. The application of smart sensors is one of the factors that has contributed to effective management of healthcare needs in the society. Wearable devices can be used to track different issues within the body. Some can be embedded to monitor different functions of the body to ensure there is effectiveness in the way medical services are delivered to such patients. The information collected in such manner, can be analyzed, aggregated and mined to do the early prediction of diseases. Processing algorithms play a vital role in helping practitioners in developing personalized treatment, which makes healthcare more economical. Apart from analyzing the effectiveness of IoT in healthcare, challenges will be discussed in this study and how they impact medicine.
The emergence of sensor-based Internet of Things (IoT) monitoring technologies have paved the way for conducting large-scale naturalistic driving studies, where continuous kinematic driver-based data are generated, capturing crash/near-crash safety critical events (SCEs) and their precursors. However, it is unknown whether the SCEs risk can be predicted to inform driver decisions in the medium term (e.g., hours ahead) since the literature has focused on SCE predictions either for a given road segment or for automated breaking applications, i.e., immediately before the event. In this paper, we examine the SCE data generated from 20+ million miles-driven by 496 commercial truck drivers to address three main questions. First, whether SCEs can be predicted using disparate driving-related data sources. Second, if so, what the relative importance of the different predictors examined is. Third, whether the prediction models can be generalized to new drivers and future time periods. We show that SCEs can be predicted 30 min in advance, using machine learning techniques and dependent variables capturing the driver’s characteristics, weather conditions, and day/time categories, where an area under the curve (AUC) up to 76% can be achieved. Moreover, the predictive performance remains relatively stable when tested on new (i.e., not in the training set) drivers and a future two-month time period. Our results can inform dispatching and routing applications, and lead to the development of technological interventions to improve driver safety.
With the incoming introduction of 5G networks and the advancement in technologies such as network function virtualization and software defined networking, new and emerging networking technologies and use cases are taking shape. One such technology is the Internet of Vehicles (IoV), which describes an interconnected system of vehicles and infrastructure. Coupled with recent developments in artificial intelligence and machine learning, IoV is transformed into an intelligent transportation system (ITS). There are, however, several operational considerations that hinder the adoption of ITSs, including scalability, high availability, and data privacy. To address these challenges, federated learning, a collaborative and distributed intelligence technique, is suggested. Through an ITS case study, the ability of a federated model deployed on roadside infrastructure throughout the network to recover from faults by leveraging group intelligence while reducing recovery time and restoring acceptable system performance is highlighted. With a multitude of use cases and benefits, federated learning is a key enabler for ITS and is poised to achieve widespread implementation in 5G and beyond networks and applications.
Background:
The modern Internet of Things (IoT) makes small devices that can sense, process, interact, connect devices, and other sensors ready to understand the environment. IoT technologies and intelligent health apps have multiplied. The main challenges in the sports environment are playing without injuries and healthily.
Objective:
In this paper the Internet of Things-based Smart Wearable System (IoT-SWS) is introduced for monitoring sports person activity to improve sports person health and performance in a healthy way.
Method:
Wearable systems are commonly used to capture individual sports details on a real-time basis. Collecting data from wearable devices and IoT technologies can help organizations learn how to optimize in-game strategies, identify opponents' vulnerabilities, and make smarter draft choices and trading decisions for a sportsperson.
Results:
The experimental result shows that IoT-SWS achieve the highest accuracy of 98.22% and efficient in predicting the sports person's health to improve sports person performance reliably.
Pandemic like Coronavirus disease (COVID-19) shows that there is an urgent need for changing our traditional healthcare monitoring system which produces a lot of waste and pollutes the environment. At present patients need to visit a doctor/clinic to check-up their health. Due to COVID-19, there are risks for doctors and patients to be infected by COVID-19. There are many cases reported worldwide where doctors/nurses and low immunity power people are easily affected by Coronavirus. Many clinics, hospitals are not able to treat regular patients. Therefore, there is a need to change the system for healthcare monitoring. Different types of micro/nano, wearable sensors and devices are developed for diagnosing diseases. The advantages of these micro and wearable sensors are higher sensitivity, fast response and low power consumption. Other hand, Instead of bulky instruments these wearable microsensors can be embedded/attached with the patient and it can monitor the patient’s health remotely. Using modern computer and electronics technology like the Internet of Things (IoT) platform (which includes computer vision, Very Large Scale Integration (VLSI), big data analysis, deep learning, machine learning and artificial intelligence), it will become a real time health monitoring system. In this chapter we will initially discuss the present day’s healthcare system, followed by micro and wearable sensors used for diagnostic purposes. Further focus of the chapter will be on the electronics used for driving the sensors, devices and the collected data will be transmitted. Finally the frameworks for IoT based wearable sensors will be discussed. These IoT based wearable sensors will be a solution for sustainable healthcare systems.
Myocardial Infarction (MI) is a fatal heart disease that is a leading cause of death. The silent and recurrent nature of MI requires real-time monitoring on a daily basis through wearable devices. Real-time MI detection on wearable devices requires a fast and energy-efficient solution to enable long term monitoring. In this paper, we propose an MI detection methodology using Binary Convolutional Neural Network (BCNN) that is fast, energy-efficient and outperforms the state-of-the- art work on wearable devices. We validate the performance of our methodology on the well known PTB diagnostic ECG database from PhysioNet. Evaluation on real hardware shows that our BCNN is faster and achieves up to 12x energy efficiency compared to the state-of-the-art work.
Internet of Things (IoT) and Artificial Intelligence (AI) play a vital role in the upcoming years to improve the assistance systems. The IoT devices utilize several sensor devices that able to collect a large volume of data in different domains which is processed by AI techniques to make the decision about the assistance problems. Among several applications, in this work, IoT with AI is used to examine the healthcare sectors to improve patient assistance and patient care in the future direction. Traditional health care assistance system fails to predict the exact patient health information and needs which reduces the accuracy of patient assistance process. For these issues, an IoT sensor with AI is used to predict the exact patient details such as fitness tracker, medical reports, health activity, body mass, temperature, and other health care information which helps to choose the right assistance process. Healthcare mobile application is used to achieve this goal and collect the patient’s information. This information is shared in the cloud environment, which is accessed and processed by applying the optimized machine learning techniques. The gathered patient details are processed according to the iterative golden section optimized deep belief neural network (IGDBN). The introduced network examines the patient’s details from the previous health information which helps to predict the exact patient health condition in the future direction. The efficiency of IoT sensor with an AI-based health assistance prediction process is developed using MATLAB tool. Excellence is determined in terms of precision (99.87), loss error (0.045), simple matching coefficient (99.71%), Matthews correlation coefficient (99.10%) and accuracy (99.86%).
The concentration in the new era of healthcare is medical Internet of things according to preventive and prediction (p2Health). In large scale and general perspective, the effective parameters from behavioral, ambient, and physiological domains as the most influencing fields of interest in healthcare, must be monitored. In personalized healthcare monitoring, wearables are playing an important role in terms of data measurement and collection. We aim at creating a configurable and adaptable platform for comprehensive parameters monitoring, according to convenient mode of wearability. Hence, we develop an innovative wrist-worn prototype for ambient monitoring, and a flexible IoT-gateway. The prototype measures the most critical parameters from ambient domain. In this platform, via IoT-gateway as an intermediate hub between wearables and IoT-server, bidirectional communication between the end-user and medics is established in real-time. In addition, physician as the real-time observer of patients are given the possibility to set up the required parameters for measurement through the IoT-gateway and activate/deactivate the sensors on the wearables. Therefore, depending on the target investigation, status of patients, requirements, and demands, medics can determine the setup parameters for measurement. Thus, the application of this platform is not limited to specific groups, but widely may be applied whether in daily routine or medical research investigation. Under the flexible IoT-gateway, the new wearables can readily be integrated and synchronized into the platform. The users are not restricted to select only one specific product but there are alternatives as well. To support the solution, experimental results are provided.
Recent years have witnessed the rapid development of human activity recognition (HAR) based on wearable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in HAR. Although these methods are fast and easy for implementation, they still have some limitations due to poor performance in a number of situations. In this chapter, we propose an improved machine learning method based on the ensemble algorithm to boost the performance of these machine learning methods for HAR.