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Sustainable smart city initiatives significantly improve the living standards of citizens and bring significant changes in economic, environmental, and social well-being. Sustainable and remote medical services are vital attributes of modern smart health infrastructure. Smart remote health infrastructure uses interconnected devices to gather and exchange health data. In smart health infrastructure, massive quantities of health data are gathered as Personal Health Records (PHR). Clinical images form a major part of the accumulated PHR. As medical images are confidential and sensitive, secure storage and transmission are significant. Hence, different cryptographic techniques are used to enhance medical image security. The conventional cryptographic schemes are complex and generate a single cipher image corresponding to a plain image. The cipher images are stored in a single database. Such storage makes the cryptosystem weak against single-point attacks. This paper discusses a simple, keyless, and distributed secure storage mechanism for medical images called SecMISS. SecMISS uses Random Grid-based Visual Secret Sharing (RGVSS) along with super-resolution for secure encryption with distributed storage and better reconstruction. The security parameters such as SSIM (≈ 0), correlation of adjacent pixels (≈ 0), PSNR (≤ 8 dB), and entropy (= 1) between share and initial image show that SecMISS provides highest level of security. Similarly, different Human Visual System (HVS) parameters between the initial and reconstructed images show an improvement in the reconstruction. Experimental results show that SecMISS outperforms the existing technique in terms of security and reconstruction. Thereby SecMISS can be efficiently used for securing medical image in sustainable smart hospitals and health infrastructures. [ABSTRACT FROM AUTHOR]

Smartwatch health sensor data are increasingly utilized in smart health applications and patient monitoring, including stress detection. However, such medical data often comprise sensitive personal information and are resource-intensive to acquire for research purposes. In response to this challenge, we introduce the privacy-aware synthetization of multi-sensor smartwatch health readings related to moments of stress, employing Generative Adversarial Networks (GANs) and Differential Privacy (DP) safeguards. Our method not only protects patient information but also enhances data availability for research. To ensure its usefulness, we test synthetic data from multiple GANs and employ different data enhancement strategies on an actual stress detection task. Our GAN-based augmentation methods demonstrate significant improvements in model performance, with private DP training scenarios observing an 11.90–15.48% increase in F1-score, while non-private training scenarios still see a 0.45% boost. These results underline the potential of differentially private synthetic data in optimizing utility–privacy trade-offs, especially with the limited availability of real training samples. Through rigorous quality assessments, we confirm the integrity and plausibility of our synthetic data, which, however, are significantly impacted when increasing privacy requirements. [ABSTRACT FROM AUTHOR]

Critical Infrastructures (CIs), such as healthcare facilities, power grids, transportation systems, and financial institutions, are vital components of a functioning society, with the economy and safety being dependent on them. Nevertheless, they have become increasingly vulnerable to cyber threats and attacks in recent years. The main reason is their inability to quickly adapt to technological changes, employ updated cryptographic frameworks, and implement a thoroughly secure architecture based on their characteristics. In this study, the unique complexities of these systems are highlighted. Various verified cyberattacks that were executed against CIs in recent years are analyzed. Moreover, the general framework of CIs is demonstrated together with the employed technologies and cryptographic primitives. A thorough architecture of said technologies is developed to better understand the targeted components and easily identify potentially hidden threats. Afterwards, threat, adversary, and attack models that target critical systems and services are designed. The purpose is a better comprehension of the systems' vulnerabilities, attack structures, motives, and targets for assisting CIs' designers in creating secure frameworks and mechanisms, with the ability to mitigate such threats. Lastly, security controls and cryptography frameworks are demonstrated together with efficient mitigation architectures and implementations from the research community. [ABSTRACT FROM AUTHOR]

Cancer is the second major cause of disease-related death worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient's life. Cancer, as a complex and heterogeneous disorder, results from the disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, the histopathology of a biopsy, and imaging (MRI, CT, PET, and US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient's response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient's big data, and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical–clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research. [ABSTRACT FROM AUTHOR]

TThe term “Smart hospital” is a highly comprehensive concept and it has not received the attention it deserves among researchers, as it is more than using a smart tool in a hospital. This study was designed to conduct a review of smart hospitals. In this systematic review, 808 studies were identified using keywords through searches on PubMed, Science Direct, Embase, Scopus and IEEE databases. After applying the inclusion and exclusion criteria (705 based on abstracts and titles and 35 after reading full texts) and removing duplicates (43), 25 studies were included in this review. Geographically, of the majority of the articles were from Asia (60%). The highest number of publications was observed in 2012 and 2020. A multidisciplinary team was involved in 72% of the research, and 68% were conducted in more than one research center. Most articles have been published in Q1 quality journals (48%), and high-income countries accounted for the largest percentage (56). Notably, 32% of these studies focused on patient care. RFID technology was the most used technology, featured in 66.67% of the studies, which primarily centered on the implementation of smart hospitals. It should be noted that development or implementation of a smart device in a hospital should not be equated with the implementation of a smart hospital and it is far beyond that. [ABSTRACT FROM AUTHOR]

This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals. [ABSTRACT FROM AUTHOR]

Ensuring the secure sharing of privacy-sensitive healthcare data is attracting considerable interest from researchers. Recently, Ogundoyin et al. designed a lightweight privacy-preserving authentication scheme named PAASH for smart health applications. Benil et al. proposed a public verification and auditing scheme named ECACS for securing e-health systems. Ogundoyin et al. and Benil et al. proposed an efficient certificateless aggregate signature (CLAS) scheme as their respective foundation signature schemes. They declared that their constructions were provably secure under the hardness assumption of cryptographic problems. In this work, we disprove their claim by analyzing the correctness and security of their underlying CLAS schemes. We first show that the batch verification process of n signatures for the CLAS scheme in PAASH is incorrect, and any public-key replacement attacker can easily break the scheme. We analyze the reasons for our attack and propose an improved scheme, named PAASH + . We then show that the CLAS scheme in ECACS fails to achieve correctness, an essential property that a cryptographic scheme should provide. As a result, it is impractical to deploy the designed PAASH and ECACS constructions in any real smart health applications. [ABSTRACT FROM AUTHOR]

Background: Internet-of-things technologies are reshaping healthcare applications. We take a special interest in long-term, out-of-clinic, electrocardiogram (ECG)-based heart health management and propose a machine learning framework to extract crucial patterns from noisy mobile ECG signals. Methods: A three-stage hybrid machine learning framework is proposed for estimating heart-disease-related ECG QRS duration. First, raw heartbeats are recognized from the mobile ECG using a support vector machine (SVM). Then, the QRS boundaries are located using a novel pattern recognition approach, multiview dynamic time warping (MV-DTW). To enhance robustness with motion artifacts in the signal, the MV-DTW path distance is also used to quantize heartbeat-specific distortion conditions. Finally, a regression model is trained to transform the mobile ECG QRS duration into the commonly used standard chest ECG QRS durations. Results: With the proposed framework, the performance of ECG QRS duration estimation is very encouraging, and the correlation coefficient, mean error/standard deviation, mean absolute error, and root mean absolute error are 91.2%, 0.4 ± 2.6, 1.7, and 2.6 ms, respectively, compared with the traditional chest ECG-based measurements. Conclusions: Promising experimental results are demonstrated to indicate the effectiveness of the framework. This study will greatly advance machine-learning-enabled ECG data mining towards smart medical decision support. [ABSTRACT FROM AUTHOR]

Cities have undergone numerous permanent transformations at times of severe disruption. The Lisbon earthquake of 1755, for example, sparked the development of seismic construction rules. In 1848, when cholera spread through London, the first health law in the United Kingdom was passed. The Chicago fire of 1871 led to stricter building rules, which led to taller skyscrapers that were less likely to catch fire. Along similar lines, the COVID-19 epidemic may have a lasting effect, having pushed the global shift towards greener, more digital, and more inclusive cities. The pandemic highlighted the significance of smart/remote healthcare. Specifically, the elderly delayed seeking medical help for fear of contracting the infection. As a result, remote medical services were seen as a key way to keep healthcare services running smoothly. When it comes to both human and environmental health, cities play a critical role. By concentrating people and resources in a single location, the urban environment generates both health risks and opportunities to improve health. In this manuscript, we have identified the most common mental disorders and their prevalence rates in cities. We have also identified the factors that contribute to the development of mental health issues in urban spaces. Through careful analysis, we have found that multimodal feature fusion is the best method for measuring and analysing multiple signal types in real time. However, when utilizing multimodal signals, the most important issue is how we might combine them; this is an area of burgeoning research interest. To this end, we have highlighted ways to combine multimodal features for detecting and predicting mental issues such as anxiety, mood state recognition, suicidal tendencies, and substance abuse. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this technology has been significantly hampered by concerns about data privacy, confidentiality, and sensitivity, particularly in healthcare and finance. The "data hunger" of ML describes how additional data can increase performance and accuracy, which is why this question arises. Federated learning (FL) has emerged as a technology that helps solve the privacy problem by eliminating the need to send data to a primary server and collect it where it is processed and the model is trained. To maintain privacy and improve model performance, FL shares parameters rather than data during training, in contrast to the typical ML practice of sending user data during model development. Although FL is still in its infancy, there are already applications in various industries such as healthcare, finance, transportation, and others. In addition, 32% of companies have implemented or plan to implement federated learning in the next 12–24 months, according to the latest figures from KPMG, which forecasts an increase in investment in this area from USD 107 million in 2020 to USD 538 million in 2025. In this context, this article reviews federated learning, describes it technically, differentiates it from other technologies, and discusses current FL aggregation algorithms. It also discusses the use of FL in the diagnosis of cardiovascular disease, diabetes, and cancer. Finally, the problems hindering progress in this area and future strategies to overcome these limitations are discussed in detail. [ABSTRACT FROM AUTHOR]

The present spreading out in healthcare initiated with the appreciation of e-healthcare by interconnecting the medical sensors. IoT played a dynamic part in the healthcare industry, which founded the realization of the Industrial Internet of Things (IIoT). The IIoT is becoming a recognized measure of healthcare universally and is shifting the aptitude to treat patients. IIoT-enabled smart health can easily authenticate the patients and expedites efficient tracking. It supports critical information sharing for effective diagnosis and treatments. IIoT-enabled health systems are the combination of sensors, cameras, devices, and objects that produce a giant quantity of data called big data. The spreading out of Big Data founded the realization of Machine Learning (ML) to predict diseases for better treatment. The major goal of IIoT-enabled smart health systems is to investigate big data and provide valued insights for improved QoS. This research suggests an IIoT-enabled system for healthcare to process the Big Data generated by IIoT sensors using machine learning. The proposed scheme is the optimized parallel and distributed framework for real-time efficient processing. The proposed framework is comprised of three modules that are big data preparation, optimized model building, and big data computation. The data preparation is performed for accurate prediction and to get faster real-time big data processing. The current proposals lack effective prediction and resourceful parallel data processing. The proposed framework is simulated through an experiment with the parallel and distributed framework. The efficiency of the proposed scheme has been verified using authentic datasets with experimentations and simulations. [ABSTRACT FROM AUTHOR]

Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use. [ABSTRACT FROM AUTHOR]

The advent of new technologies such as Machine Learning has highly influenced the health sector's activities; with this, there is an ease in diagnosis and decision-making processes in the sector. Hence, this study aims to analyze the application of Machine Learning in Smart Health research. This study uses 192 records from the Scopus database based on a well-crafted search term to identify nations with the highest publication output, the principal research subject areas, the top funding sponsors, and research keywords in this subject matter. The result shows that the first document on machine learning in smart health was published in 2011. The research output on this subject has dramatically increased, with India now being the top nation where research in this area is conducted. It was also discovered that the journal IEEE Access has the highest number of publications in this area. This analysis will help researchers, policy developers, and professionals in the health sector to better understand the development of Machine Learning in Smart Health research. Machine Learning in Smart Health portends Growth in the future. [ABSTRACT FROM AUTHOR]

Mobile device-based healthcare has emerged as a game-changing method of telerehabilitation. Society, including the scientific community, is paying more attention to smart health rehabilitation during the COVID-19 era. This study provides a review of smart health rehabilitation in palliative care. Alongside monitoring, some systems provide additional patient-caregiver communication and progress management. The findings of this study may aid researchers and clinicians in the development of smartphone-based rehabilitation systems' functionalities and interactions, as well as in the selection of technology in palliative care during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The majority of current smart health applications are deployed on a smartphone paired with a smartwatch. The phone is used as the computation platform or the gateway for connecting to the cloud while the watch is used mainly as the data sensing device. In the case of fall detection applications for older adults, this kind of setup is not very practical since it requires users to always keep their phones in proximity while doing the daily chores. When a person falls, in a moment of panic, it might be difficult to locate the phone in order to interact with the Fall Detection App for the purpose of indicating whether they are fine or need help. This paper demonstrates the feasibility of running a real-time personalized deep-learning-based fall detection system on a smartwatch device using a collaborative edge-cloud framework. In particular, we present the software architecture we used for the collaborative framework, demonstrate how we automate the fall detection pipeline, design an appropriate UI on the small screen of the watch, and implement strategies for the continuous data collection and automation of the personalization process with the limited computational and storage resources of a smartwatch. We also present the usability of such a system with nine real-world older adult participants. [ABSTRACT FROM AUTHOR]

Today's world is changing dramatically due to the influence of various factors. Whether due to the rapid development of technological tools, advances in telecommunication methods, global economic and social events, or other reasons, almost everything is changing. As a result, the concepts of a "job" or work have changed as well, with new work shifts being introduced and the office no longer being the only place where work is done. In addition, our non-stop active society has increased the stress and pressure at work, causing fatigue to spread worldwide and becoming a global problem. Moreover, it is medically proven that persistent fatigue is a cause of serious diseases and health problems. Therefore, monitoring and detecting fatigue in the workplace is essential to improve worker safety in the long term. In this paper, we provide an overview of the use of smart wearable devices to monitor and detect occupational physical fatigue. In addition, we present and discuss the challenges that hinder this field and highlight what can be done to advance the use of smart wearables in workplace fatigue detection. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) security is a fundamental challenge for the enterprises working behind the buildup of the IoT, and it is related applications. In today's ara, with IoT, another buzzword is a blockchain-based cryptocurrency and other blockchain applications. Blockchain technology has proven itself one of the most secure existing technology in trust and privacy. This paper discusses IoT and blockchain technology fundamentals, followed by a discussion regarding numerous consensus models. We also discuss mathematical foundations used by blockchain technology, and it is followed by the proposed privacy-preserving technique for IoT devices with better device identity management using blockchain technology. This paper presents a novel solution for privacy preservation and identity allocation using secure identity management through public key-based cryptography-based blockchain. Though we consider smart home management and smart health management's ecosystem for applicability discussion of the proposed scheme, the proposed system can be applied to any IoT application for better privacy preservation. Finally, we conclude with the discussion regarding the futuristic scope of the proposed system.// [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) consists of heterogeneous devices such as wearables and monitoring devices that collect data to provide autonomous decision-making and smart applications. IoT technologies, such as the Internet of Medical Things (IoMT), have become gradually popular for medical purposes, combining IoT and medical devices to achieve good health and well-being. However, IoMT devices are often tight and have resource constraints, which leads to limited local data processing in the device. Edge computing provides access to additional computation and storage resources for IoMT devices, bringing intelligent processing closer to the data sources. This technology opens up great possibilities for IoMT applications, especially when combined with Artificial Intelligence (AI). Edge AI runs AI computations close to the IoT devices and users instead of centralized services such as cloud servers. This paper investigates the potential of Edge AI and IoMT. In this sense, this survey is the first work to further detail Edge AI and Machine Learning Operations in IoMT domains and wearable technology, thus contributing to the literature by comprehensively exploring the potential of ML strategies and operations at the network's edge and intelligence distribution. This study also presents a case study on heart anomaly detection. [ABSTRACT FROM AUTHOR]

(1) Background: In the last few years, technological developments in the surgical field have been rapid and are continuously evolving. One of the most revolutionizing breakthroughs was the introduction of the IoT concept within surgical practice. Our systematic review aims to summarize the most important studies evaluating the IoT concept within surgical practice, focusing on Telesurgery and surgical Telementoring. (2) Methods: We conducted a systematic review of the current literature, focusing on the Internet of Surgical Things in Telesurgery and Telementoring. Forty-eight (48) studies were included in this review. As secondary research questions, we also included brief overviews of the use of IoT in image-guided surgery, and patient Telemonitoring, by systematically analyzing fourteen (14) and nineteen (19) studies, respectively. (3) Results: Data from 219 patients and 757 healthcare professionals were quantitively analyzed. Study designs were primarily observational or based on model development. Palpable advantages from the IoT incorporation mainly include less surgical hours, accessibility to high quality treatment, and safer and more effective surgical education. Despite the described technological advances, and proposed benefits of the systems presented, there are still identifiable gaps in the literature that need to be further explored in a systematic manner. (4) Conclusions: The use of the IoT concept within the surgery domain is a widely incorporated but less investigated concept. Advantages have become palpable over the past decade, yet further research is warranted. [ABSTRACT FROM AUTHOR]

Activity and Fall detection have been a topic of keen interest in the field of ambient assisted living system research. Such systems make use of different sensing mechanisms to monitor human motion and aim to ascertain the activity being performed for health monitoring and other purposes. Towards this end, in addition to activity recognition, fall detection is an especially important task as falls can lead to injuries and sometimes even death. This work presents a fall detection and activity recognition system that not only considers various activities of daily living but also considers detection of falls while taking into consideration the direction and severity. Inertial Measurement Unit (accelerometer and gyroscope) data from the SisFall dataset is first windowed into non-overlapping segments of duration 3 s. After suitable data augmentation, it is then passed on to a Convolutional Neural Network (CNN) for feature extraction with an eXtreme Gradient Boosting (XGB) last stage for classification into the various output classes. The experiments show that the gradient boosted CNN performs better than other comparable techniques, achieving an unweighted average recall of 88%. [ABSTRACT FROM AUTHOR]

This research puts forward novel deep hybrid frameworks for assessment of mental health indicators viz. depression and sleep quality using IoT-based motor activity recordings. The study employs long short-term memory (LSTM) to extract high-level features from sequential motor activity data, which are then combined with statistical features of the raw data to form a hybrid feature extraction model. The combined feature vector obtained via the hybrid feature extractor is fed into four regression models, namely linear regression (LR), sequential minimal optimization regression (SMOR), random forest regression (RFR) and adaptive neuro-fuzzy inference system (ANFIS) forming four deep hybrid regression models, namely LSTM-LR, LSTM-SMOR, LSTM-RFR, and LSTM-ANFIS for prediction of depression and sleep quality. The proposed deep hybrid frameworks are validated on benchmark datasets, namely the Depresjon dataset and the MESA actigraphy dataset, and the best performance is observed by LSTM-RFR with the adjusted R2 value of 74.19% on the MESA dataset. On validating the significance of statistical features, it has been observed that all the models show significant improvement in performance on combining the statistical features with high-level features with highest reduction in mean absolute error as 4.0436 and highest increase in R2 statistic as 0.1651. Model building time of the four regression methods is also compared for which LR gave the best results as only 0.01 s for training and testing. [ABSTRACT FROM AUTHOR]

Healthcare cognitive assistants (HCAs) are intelligent systems or agents that interact with users in a context-aware and adaptive manner to improve their health outcomes by augmenting their cognitive abilities or complementing a cognitive impairment. They assist a wide variety of users ranging from patients to their healthcare providers (e.g., general practitioner, specialist, surgeon) in several situations (e.g., remote patient monitoring, emergency response, robotic surgery). While HCAs are critical to ensure personalized, scalable, and efficient healthcare, there exists a knowledge gap in finding the emerging trends, key challenges, design guidelines, and state-of-the-art technologies suitable for developing HCAs. This survey aims to bridge this gap for researchers from multiple domains, including but not limited to cyber-physical systems, artificial intelligence, human-computer interaction, robotics, and smart health. It provides a comprehensive definition of HCAs and outlines a novel, practical categorization of existing HCAs according to their target user role and the underlying application goals. This survey summarizes and assorts existing HCAs based on their characteristic features (i.e., interactive, context-aware, and adaptive) and enabling technological aspects (i.e., sensing, actuation, control, and computation). Finally, it identifies critical research questions and design recommendations to accelerate the development of the next generation of cognitive assistants for healthcare. [ABSTRACT FROM AUTHOR]

With the progress of artificial intelligence, big data and functional neuroimaging technologies, brain computing has rapidly advanced our understanding of brain intelligence and brain disorders. We argue that existing data analytical methods have become insufficient for brain computing when dealing with multiple brain big data sources, because such methods mainly focus on flattening strategies and fail to work well for systematic understanding of the constituent elements of cognition, emotion and disease, as well as the intra- and inter-relations within and among themselves. To address this problem, we present in this paper a novel multi-source brain computing platform by Data-Brain driven systematic fusion. First, we formalize a series of behaviors surrounding the Brain Informatics-based investigation process, and present a conceptual model to systematically represent content and context of functional neuroimaging data. Then, we propose the systematic brain computing framework with multi-aspect fusion and inference to understand brain specificity and give uncertainty quantification, as well as its inspiration and applications for translational studies on brain health. In particular, a graph matching-based task search algorithm is introduced to help systematic experimental design and data sampling with multiple cognitive tasks. The study increases the interpretability and transparency of brain computing findings by inferring and testing multiple hypotheses taking into consideration the effect of evidence combination. Finally, multiple sources of knowledge (K), information (I) and data (D) are driven by a KID loop as the thinking space to inspire never-ending learning and multi-dimensional interactions in the connected social–cyber–physical spaces. Experimental results have demonstrated the efficacy of the proposed brain computing method with systematic fusion. [Display omitted] • Brain informatics-guided systematic fusion and brain investigation. • Brain big data integration and analytics based on the extensible Data-Brain. • Multi-source fusion of data, information and knowledge for brain computing. • Fusion and inference to interpret multiple uncertainties of brain computing findings. • Predictive model to build a bridge between brain computing and smart health services. [ABSTRACT FROM AUTHOR]

The rapid developments in Artificial Intelligence present an opportunity for the research community to provide and advance Smart Health for the well-being of our society. By considering the availability of multi-source information and heterogeneous data in the era of Big Data, this Special Issue explores the theories, methodologies and possible breakthroughs that have designed and adopted information fusion for Smart Health powered by recent Artificial Intelligence advances. Specifically, this Special Issue focuses on three questions; How to achieve and realize human-level intelligence in Smart Health, How to achieve and benefit Smart Health from a multi-disciplinary balance , and How to utilize the power of Big Data for Smart Health. The Special Issue is a great success, with a small number of quality studies carefully selected from an overwhelming amount of contributions. • AI presents an opportunity for the researchers to provide and advance Smart Health. • Multi-source information has designed and adopted information fusion for Smart Health. • To achieve and realize human-level intelligence in Smart Health. • To achieve and benefit Smart Health from a multi-disciplinary balance. • To utilize the power of Big Data for Smart Health. [ABSTRACT FROM AUTHOR]

• ECC-based certificateless signature scheme with compact aggregation. • Fine-grained access control using attribute-based encryption and signature scheme. • Proof of security in the random oracle model (ROM). • Efficient, secure, and privacy-preserving for practical deployment. Very recently, a number of intelligent applications have evolved in smart cities such as smart grid, smart parking, smart waste management, smart manufacturing, smart home, and smart health, making our cities more smarter. In the context of smart health, smart devices are employed to collect and transmit biomedical information to a medical server, allowing medical practitioners to give better and improved healthcare services. However, considering the sensitive nature of the medical data, any illegal access may have devastating effects on the patient's health condition. A lightweight privacy-preserving authentication and fine-grained access control scheme for smart health known as PAASH is proposed. In PAASH, a new ultraefficient certificateless signature scheme with compact aggregation is developed to achieve source authentication and data integrity. To achieve efficient user authorization and data confidentiality, a privacy-preserving access control technique using the developed aggregate signature and ciphertext-policy attribute-based encryption (CP-ABE) is implemented. An analysis of PAASH reveals that it can provide desirable privacy and security measures in smart health. Moreover, the formal security demonstrated in the random oracle model (ROM) shows that PAASH is semantically secure under the intractability of the Discrete Logarithm Problem (DLP). The performance evaluation shows that PAASH is par excellence practically suitable for smart health. [ABSTRACT FROM AUTHOR]

Social Internet of Things (SIoT) is one of the emerging research areas which integrates the Internet of Things (IoT) with social networking concepts. It envisages better information sharing, by overcoming the scalability issues in IoT. It is supposed to overhaul the smart health industry, as it is a flexible compared to conventional IoT based solutions. It however poses many challenges with data authentication and privacy, being a couple of such issues that need to be addressed. In this paper, we present an efficient watermarking method for tamper detection, localization, and self-recovery of medical images for SIoT-based smart health networks. The cover image is initially divided into four equal parts, with each part progressively divided into 4 × 4 non-overlapping blocks. A watermark is prepared from a group of randomly chosen four, 4 × 4 blocks, determined by chaotic mapping. The information is embedded into 4 sub-blocks mapped utilizing chaotic sequence such that the restoration process can be carried out even if three sub-blocks out of four containing the information, get tampered with. The proposed technique is found to restore almost 1/3rd of the tampered image and produces a high-quality image with the average Peak Signal-to-Noise Ratio value of above 33 dB after the restoration process. The performance of the proposed method for various collage attacks shows its efficacy over other state-of-the-art techniques making it a potential candidate for SIoT driven smart health systems. [ABSTRACT FROM AUTHOR]

The terrible cost of injuries and sudden illnesses does have fatal consequences that exposes the limitations of the current prehospital processes in terms of time for emergency staff to arrive on scene and lack of first aid skills among the available incident witnesses. In this paper we aim at developing a smart pervasive chatbot for emergency case assistance based on cloud computing called SPeCECA that assists victims or incident witnesses to help avoiding deterioration of the subject's condition and maintaining his/her physical integrity until the aid arrives, which could dramatically increase the victim's survivability chances. Therefore, even a person with no first aid skills, can help the victim to survive by performing first aid support as suggested by the virtual assistant. Furthermore, thanks to its connectivity with the emergency medical service, trusted person(s), and the access to social media, SPeCECA has its own way of alarming the emergency case, in parallel, after having released the degree of the emergency situation's severity. The proposed method is a mobile pervasive healthcare service in the form of a connected mobile application as a virtual assistant for the benefit of anyone facing an emergency case. The proposed chatbot allows an online human-bot interaction that supports different scenarios for every single emergency case. The design of the system is introduced by its six interdependent components: information pre-processing component (IPPC), natural language processing component (NLPC), context component (CC), information post-processing component (IPoPC), response generator component (RGC), and alert message constructor component (AMCC). [ABSTRACT FROM AUTHOR]

Smart health applications are raising a growing interest around the world thanks to its potential to act proactively and solve health related problems with smart technologies. Smart health technologies can provide effective healthcare services such as personalization of treatments through big data, robotics in cure and care, artificial intelligence support to doctors, etc. The mixed structure of the evaluation of smart health technologies involves various contradictory criteria. However, when information is of uncertain nature, it is difficult to decide on how to treat. A hesitant fuzzy linguistic term set (HFLTS) approach is applied to overcome such uncertainties related to this multi-criteria decision-making (MCDM) problem. This approach can be used to facilitate experts' decision-making processes in complex and uncertain situations. In this study, an integrated hesitant fuzzy linguistic (HFL) MCDM approach is proposed to evaluate smart health technologies. The criteria are weighted with HFL Analytic Hierarchy Process (AHP), and then, smart health technologies are evaluated with the HFL Combinative Distance-based Assessment (CODAS) method. A comparative analysis with HFL COPRAS and HFL TOPSIS is applied. Lastly, the potential of this approach is presented through a case study. [ABSTRACT FROM AUTHOR]

These days, the Internet of Things (IoT) gets an incredible amount of thought from analysts as it transforms into significant innovation that guarantees a person's life, brings together things, machines and everything. By allowing the exchange of IoT, we talk about frameworks that incorporate objects into reality and sensors are connected or integrated with those components, which are associated to the Internet through wired and external system architecture. IoT sensors can use a variety of organizations, such as RFID, Wi-Fi, Bluetooth Bluetooth and ZigBee, despite allowing a wide range of networks that use multiple fields, GPRS, GSM, LTE and 3G. The IoT powerful object will share data on the location of the objects and the location of individuals, programming frameworks and different machines. In this paper, we take a look at some IoT applications [ABSTRACT FROM AUTHOR]

Health, such as the industry, university or city, is immersed in a process of digital transformation generated by the possibility and technological convergence of the Internet of Things (IoT), Big Data and Artificial Intelligence on the one hand and their consequences on the other: hyperconnectivity, datafication and algorithmization. This is a process of transformation towards what has come to be called Smart Health, Health 4.0 or mHealth. However, despite the enormous potential that underlies the digitization of the healthcare sector, this process can have very negative effects both for the sector and its organizations and for the system’s user. One of the most worrying questions at this moment is the aspect and the force that bioethification is taking; that is, the process by which mass data on the opinions, preferences and behaviors of the hyperconnected users of the cyber-physical assistance system are stored, crossed and processed, on the basis of an utilitarian criterion, to deter mine through mathematical models what is morally valid. The aim of the present study is to analyze the present and future consequences of bioethification in the health field, as well as to propose guidelines to undertake a socially responsible and morally valid digitalization. [ABSTRACT FROM AUTHOR]

Today's medical industry can be represented by a human-centered u-healthcare paradigm that is available and accessible anywhere, where high-tech IT can serve as the basis at any time and any place. In addition, in the medical industry, studies of many of the developments and applications are actively conducted based on the development of information communication technology. The aim of medical-information systems is the construction of an advanced IT and integrated u-healthcare system that evolves in the direction of integrated medical-based IT-convergence systems. Accordingly, to resolve the problems of telemedicine, in terms of the remote access to medical data, some public and private initiatives have been proposed, ranging from patient-mobility approaches to medical data. In addition, regarding GENICloud, which provides links with the existing future Internet testbeds and the Eucalyptus Cloud, two out of the seven GENIAM APIs have been announced as the common APIs of the future Internet testbeds in GENI, and they have been implemented and are provided. In the present GENI Cloud system, due to the provision of limited APIs, restrictions may occur in the future Internet testbeds and the Eucalyptus Cloud system management. Therefore, this study proposes a cloud-based mobile health service for the enhancement of the quality of service (QoS) including factors such as reliability and response time to resolve the problems of the broadband-communication infrastructure in the existing mobile health service and the delay problem on the wireless body area network. In this paper, we propose the cloud-based u-healthcare network with a QoS-guaranteed mobile health service. For this method, the TMO-distribution object model that was used in the existing research to implement a reliable and efficient cloud system for users was not used, and instead, a cloud-platform environment was built up through the construction of a distributed system based on a cluster-based mobile object. For this purpose, this study considered the characteristics of the wireless-communication environments between the terminals and the cloud servers in the mobile cloud environment and the proposed cloud mobility services and the specialized mobile cloud-control software. Later, for linkages with cloud computing environments and testbeds was proposed. In addition, this study carried out a cloud mobility-control design to provide a service in the mobile cloud environment that is based on the actual future Internet testbeds. Lastly, based on the structured cloud-platform environment, this study designed access interfaces to provide a mobile healthcare service in consideration of the user convenience. For the mobile-service access interfaces, since the same service interfaces can be used to access the characteristics and functions of all of the applications from browsers and device clients, the model-view-controller structure of the platform was designed, including the components for the further improvement of the requirements, reuse, and maintenance of the codes in medium and large distributed systems. [ABSTRACT FROM AUTHOR]

With the emergence of mobile and pervasive computing, digital gadgets and products have become a pervasive and of our lives. Everyday, they influence us explicitly or implicitly and are changing our way of life as well as our behaviors, either intentionally or unintentionally. Throughout the past decade, research has been conducted to make persuasion interactive rather than unilateral. The outcome is known today as persuasive computing technology. There have been considerable efforts to apply persuasive technology in many domains, especially healthcare. Although these efforts to are gradually coming to fruition, several problems and challenges remain. Most currently available persuasive systems were not based on explicit theoretical foundations of persuasion theories and models when they were developed. There are two major reasons: (1) a general lack of theoretical frameworks and practical methodologies for building persuasive systems or applications, and (2) it is difficult for computer scientists and developers to understand and harness persuasion theories and models. In response, we have developed an Action-based Behavior Model (ABM) that can be utilized by computer scientists and developers. In this paper, we validate ABM through a scenario-based case, describing how it can be utilized in participatory telehealth to enable persuasion. [ABSTRACT FROM AUTHOR]

Highlights • In this work, we coated nylon filaments with reduced graphene oxide (rGO) uniformly by inkjet printing and chemical reduction. The rGO was utilized as a uniquely conformal material with negative temperature coefficient (NTC). • We tested and proved that moderate bending (<34°) and stretching (<4% strain) did not appreciably and irreversibly damage the structure of rGO film, and therefore the stable NTC behavior could be sustained. • We designed and fabricated the textile-infused sensor array for spatiotemporal mapping of skin temperatures. The rGO-coated nylon filaments were stitched along with Ag conductive threads into a polyester fabric to create the array, consisting of individually addressable 6 × 6 NTC sensing elements. • The temperature-mapping attribute of the sensor array was evaluated in comparison to infrared imaging. The accuracy of the sensor array was proved to be comparable to those of infrared cameras. Abstract We report a textile-infused sensor array, utilizing reduced graphene oxide (rGO) as a uniquely conformal negative temperature coefficient (NTC) material, for spatiotemporal mapping of skin temperatures. Nylon filaments were coated with rGO and stitched along with Ag conductive threads into a polyester fabric to create the array of individually addressable 6 × 6 NTC sensing elements. The temperature-mapping attribute of the sensor array was evaluated in comparison to infrared imaging. The rGO film remained mechanically and electrically stable upon stretching (<4% strain) and bending (<34°) of the filaments, demonstrating its conformal nature. These results suggest the intriguing possibility of thermally mapping topographically complex skin surfaces in a non-invasive, wearable, and cost effective manner. [ABSTRACT FROM AUTHOR]

Smart city–based IoT devices enable collection of vast amounts of data, which can be used to provide more efficient public and private services. Among these, healthcare is especially relevant, and smart health (s-health) models are already being deployed. The authors propose attribute-based credentials (ABCs) to cope with s-health privacy issues and to set the stage for the further adoption in other privacy-aware IoT-based smart cities’ services. [ABSTRACT FROM AUTHOR]

In the context of hospital management and operation, Intensive Care Units (ICU) are one of the most challenging in terms of time responsiveness and criticality, in which adequate resource management and signal processing play a key role in overall system performance. In this work, a context aware Intensive Care Unit is implemented and analyzed to provide scalable signal acquisition capabilities, as well as to provide tracking and access control. Wireless channel analysis is performed by means of hybrid optimized 3D Ray Launching deterministic simulation to assess potential interference impact as well as to provide required coverage/capacity thresholds for employed transceivers. Wireless system operation within the ICU scenario, considering conventional transceiver operation, is feasible in terms of quality of service for the complete scenario. Extensive measurements of overall interference levels have also been carried out, enabling subsequent adequate coverage/capacity estimations, for a set of Zigbee based nodes. Real system operation has been tested, with ad-hoc designed Zigbee wireless motes, employing lightweight communication protocols to minimize energy and bandwidth usage. An ICU information gathering application and software architecture for Visitor Access Control has been implemented, providing monitoring of the Boxes external doors and the identification of visitors via a RFID system. The results enable a solution to provide ICU access control and tracking capabilities previously not exploited, providing a step forward in the implementation of a Smart Health framework. [ABSTRACT FROM AUTHOR]

Nowadays, pervasive computing technologies are paving a promising way for advanced smart health applications. However, a key impediment faced by wide deployment of these assistive smart devices, is the increasing privacy and security issue, such as how to protect access to sensitive patient data in the health record. Focusing on this challenge, biometrics are attracting intense attention in terms of effective user identification to enable confidential health applications. In this paper, we take special interest in two bio-potential-based biometric modalities, electrocardiogram (ECG) and electroencephalogram (EEG), considering that they are both unique to individuals, and more reliable than token (identity card) and knowledge-based (username/password) methods. After extracting effective features in multiple domains from ECG/EEG signals, several advanced machine learning algorithms are introduced to perform the user identification task, including Neural Network, K-nearest Neighbor, Bagging, Random Forest and AdaBoost. Experimental results on two public ECG and EEG datasets show that ECG is a more robust biometric modality compared to EEG, leveraging a higher signal to noise ratio and also more distinguishable morphological patterns. Among different machine learning classifiers, the random forest greatly outperforms the others and owns an identification rate as high as 98%. This study is expected to demonstrate that properly selected biometric empowered by an effective machine learner owns a great potential, to enable confidential biomedicine applications in the era of smart digital health. [ABSTRACT FROM AUTHOR]

Smart Health, with its flexibility and efficiency, has been widely deployed, especially during the COVID-19 pandemic. However, privacy protection mechanisms for Smart Health are not yet well established and still present a number of security issues. Ciphertext-Policy Attribute-Based Encryption (CP-ABE), is identified as the furthest potential approach for constructing privacy-preserving Smart Health. However, traditional CP-ABE is facing some new challenges. On the one hand, access policy is not encrypted, and the identity information of the user could be exposed. On the other hand, Smart Health Records (SHRs) are outsourced to the Cloud Service Providers (CSP) and may be at risk of being tampered with. In this article, we have built a CP-ABE solution (PHCA) that supports policy-hiding and cloud auditing to ensure privacy security for smart health, in which the decryption cost is constant. To ensure data integrity, we securely introduce an effective third-party auditor. In addition, we design and implement safe and effective outsourcing decryption algorithms, which significantly low the decryption costs for users. Performance comparisons and security analysis demonstrate that our solutions function effectively. • We propose a CP-ABE scheme (PHCA) that supports policy-hiding and cloud auditing. • Our scheme ensures data integrity and privacy. • Our scheme is proven secure under dual-system encryption technique. [ABSTRACT FROM AUTHOR]

As a stressful and sensitive task, driving can be disturbed by various factors from the health condition of the driver to the environmental variables of the vehicle. Continuous monitoring of driving hazards and providing the most appropriate business services to meet actual needs can guarantee safe driving and make great use of the existing information resources and business services. However, there is no in-depth research on the perception of a driver’s health status or the provision of customized business services in case of various hazardous situations. In order to constantly monitor the health status of the drivers and react to abnormal situations, this paper proposes a context-aware service system providing a configurable architecture for the design and implementation of the smart health service system for safe driving, which can perceive a driver’s health status and provide helpful services to the driver. With the context-aware technology to construct a smart health services system for safe driving, this is the first time that such a service system has been implemented in practice. Additionally, an assessment model is proposed to mitigate the impact of the acceptable abnormal status and, thus, reduce the unnecessary invocation of the services. With regard to different assessed situations, the business services can be invoked for the driver to adapt to hazardous situations according to the services configuration model, which can take full advantage of the existing information resources and business services. The evaluation results indicate that the alteration of the observed status in a valid time range T can be tolerated and the frequency of the service invocation can be reduced. [ABSTRACT FROM AUTHOR]

Longitudinal individual and crowdsourced health data from wearable sensors can be leveraged to increase the quality and length of life, giving rise to a new health data economy. [ABSTRACT FROM AUTHOR]

Portable medical devices generate volumes of data that could be useful in identifying health risks. The proposed method filters patients' electrocardiograms (ECGs) and applies machine-learning classifiers to identify cardiac health risks and estimate severity. The authors present the results of applying their method in a case study. [ABSTRACT FROM AUTHOR]

Healthcare delivery is being transformed by technology that personalizes, tracks, and manages patient information across devices. Smart and connected health will develop safer and more effective, efficient, equitable, and user-centered services through pervasive computing innovations. [ABSTRACT FROM AUTHOR]

Smart homes offer an unprecedented opportunity to unobtrusively monitor human behavior in everyday environments and to determine whether relationships exist between behavior and health changes. Behavior change detection (BCD) can be used to identify changes that accompany health events, which can potentially save lives. [ABSTRACT FROM AUTHOR]

Health applications involve many data sources, individuals, and services that work against guarantees that an individual's personal data will not be used without consent. The proposed privacy-centered architecture integrates data security and semantic descriptions into a trust-query framework, enabling the provision of user consent as a service. [ABSTRACT FROM PUBLISHER]

Future smart cities are the key to fulfilling the ever-growing demands of citizens. Information and communication advancements will empower better administration of accessible resources. The eventual fate of the world's betterment lies in its urban environment advancement. The fast influx of individuals creates possibility, yet it additionally causes challenges. Creating sustainable, reasonable space in the world's steadily extending cities is tested confronting governments worldwide. The model of the smart cities rise, where the rights and well-being of the smart city citizens are assured, the industry is in action, and the assessment of urban planning from an environmental point of view. This paper presents a survey on analyzing future technologies and requirements for future smart cities. We provide extensive research to identify and inspect the latest technology advancements, the foundation of the upcoming robust era. Such technologies include deep learning (DL), machine learning (ML), internet of things (IoT), mobile computing, big data, blockchain, sixth-generation (6G) networks, WiFi-7, industry 5.0, robotic systems, heating ventilation, and air conditioning (HVAC), digital forensic, industrial control systems, connected and automated vehicles (CAVs), electric vehicles, product recycling, flying Cars, pantry backup, calamity backup and vital integration of cybersecurity to keep the user concerns secured. We provide a detailed review of the existing future smart cities application frameworks. Furthermore, we discuss various technological challenges of future smart cities. Finally, we identify the future dimensions of smart cities to develop smart cities with the precedence of smart living. • Analyze future smart cities' characteristics based on state-of-the-art technologies. • Identifies and inspects the latest advancements in technology. • Analyze the most feasible frameworks and their proficiency for FSC. • Provide technology challenges identified in the literature. • Provide future directions are identified to develop smart cities. [ABSTRACT FROM AUTHOR]

To introduce smart health in Wuhan, and provide some references for other cities. As the largest mega-city in central China, Wuhan is investing large amounts of resources to push forward the development of Smart Wuhan and Health Wuhan, and it has unique features. It is one of the centerpieces of China's New Healthcare Reform, and great hope is put on it to help solve the conflict between limited healthcare resources and the large population of patients. How to plan and design smart health is important. The construction of Wuhan Smart Health includes some aspects as follows, like requirement analysis, the establishment of objectives and blueprint, the architecture design of regional health information platform, evaluation and implementation, problems and solutions, and so on. Wuhan Smart Health has obtained some achievements in health network, information systems, resident's health records, information standard, and the first phase of municipal health information platform. The focus of this article is the whole construction process of smart health in Wuhan. Although there are some difficulties during this period, some smart health services and management have been reflected. Compared with other cities or countries, Wuhan Smart Health has its own advantages and disadvantages. This study aims to provide a reference for other cities. Because smart health of Wuhan is characteristic in construction mode. Though still in the initial stage, it has great potentials in the future. [ABSTRACT FROM AUTHOR]

This paper proposes a new indoor localization system, based on RFID technology and a hierarchical structure of classifiers. This system has been specifically designed to work in unfriendly scenarios, where transmissions could be disturbed by other electronic devices or shielded walls. The infrastructure has been deployed and evaluated in the emergency unit of a large Italian hospital (48 rooms covering about 4000 m 2 ) to detect the room where lost or forgotten patients lie. Extensive experiments show the potential of such technology for indoor localization applications in terms of accuracy, precision, complexity, robustness and scalability. In 98% of cases the system localizes the correct room (83%) or one of its adjacency (15%). [ABSTRACT FROM AUTHOR]

Advances in medicine and clinical care are increasingly tied to computing technologies. This special issue explores emerging trends in smart health and the benefits they bring to individual patients and society as a whole. [ABSTRACT FROM AUTHOR]

Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood that physical fitness is seriously influenced. Jarring motions and improper posture during workouts can lead to temporary or permanent disability. With the advent of technological advances, activity acknowledgment dependent on wearable sensors has pulled in countless studies. Still, a fully portable smart fitness suite is not industrialized, which is the central need of today's time, especially in the Covid-19 pandemic. Considering the effectiveness of this issue, we proposed a fully portable smart fitness suite for the household to carry on their routine exercises without any physical gym trainer and gym environment. The proposed system considers two exercises, i.e., T-bar and bicep curl with the assistance of the virtual real-time android application, acting as a gym trainer overall. The proposed fitness suite is embedded with a gyroscope and EMG sensory modules for performing the above two exercises. It provided alerts on unhealthy, wrong posture movements over an android app and is guided to the best possible posture based on sensor values. The KNN classification model is used for prediction and guidance for the user while performing a particular exercise with the help of an android application-based virtual gym trainer through a text-to-speech module. The proposed system attained 89% accuracy, which is quite effective with portability and a virtually assisted gym trainer feature. [ABSTRACT FROM AUTHOR]

Human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users' movement and are also relatively simple to implement compared to other approaches. In this paper, we present a hierarchical classification framework based on wavelets and adaptive pooling for activity recognition and fall detection predicting fall direction and severity. To accomplish this, windowed segments were extracted from each recording of inertial measurements from the SisFall dataset. A combination of wavelet based feature extraction and adaptive pooling was used before a classification framework was applied to determine the output class. Furthermore, tests were performed to determine the best observation window size and the sensor modality to use. Based on the experiments the best window size was found to be 3 s and the best sensor modality was found to be a combination of accelerometer and gyroscope measurements. These were used to perform activity recognition and fall detection with a resulting weighted F1 score of 94.67%. This framework is novel in terms of the approach to the human activity recognition and fall detection problem as it provides a scheme that is computationally less intensive while providing promising results and therefore can contribute to edge deployment of such systems. [ABSTRACT FROM AUTHOR]