A Deep Learning Based Wearable Healthcare Iot Device for AI-Enabled Hearing Assistance Automation

With the recent booming of artificial intelligence (AI), particularly deep learning techniques, digital healthcare is one of the prevalent areas that could gain benefits from AI-enabled functionality. This research presents a novel AI-enabled Internet of Things (IoT) device operating from the ESP-8266 platform capable of assisting those who suffer from impairment of hearing or deafness to communicate with others in conversations. In the proposed solution, a server application is created that leverages Google's online speech recognition service to convert the received conversations into texts, then deployed to a micro-display attached to the glasses to display the conversation contents to deaf people, to enable and assist conversation as normal with the general population. Furthermore, in order to raise alert of traffic or dangerous scenarios, an 'urban-emergency' classifier is developed using a deep learning model, Inception-v4, with transfer learning to detect/recognize alerting/alarming sounds, such as a horn sound or a fire alarm, with texts generated to alert the prospective user. The training of Inception-v4 was carried out on a consumer desktop PC and then implemented into the AI-based IoT application. The empirical results indicate that the developed prototype system achieves an accuracy rate of 92% for sound recognition and classification with real-time performance

An overview of machine learning and 5G for people with disabilities

Currently, over a billion people, including children (or about 15% of the world’s population), are estimated to be living with disability, and this figure is going to increase to beyond two billion by 2050. People with disabilities generally experience poorer levels of health, fewer achievements in education, fewer economic opportunities, and higher rates of poverty. Artificial intelligence and 5G can make major contributions towards the assistance of people with disabilities, so they can achieve a good quality of life. In this paper, an overview of machine learning and 5G for people with disabilities is provided. For this purpose, the proposed 5G network slicing architecture for disabled people is introduced. Different application scenarios and their main benefits are considered to illustrate the interaction of machine learning and 5G. Critical challenges have been identified and addressed.This work has been supported by the Agencia Estatal de Investigación of Ministerio de Ciencia e Innovación of Spain under project PID2019-108713RB-C51 MCIN/ AEI /10.13039/501100011033.Postprint (published version

INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life

The Role of the Internet of Things in Health Care: A Systematic and Comprehensive Study

The Internet of Things (IoT) is becoming an emerging trend and has significant potential to replace other technologies, where researchers consider it as the future of the internet. It has given tremendous support and become the building blocks in the development of important cyber-physical systems and it is being severed in a variety of application domains, including healthcare. A methodological evolution of the Internet of Things, enabled it to extend to the physical world beyond the electronic world by connecting miscellaneous devices through the internet, thus making everything is connected. In recent years it has gained higher attention for its potential to alleviate the strain on the healthcare sector caused by the rising and aging population along with the increase in chronic diseases and global pandemics. This paper surveys about various usages of IoT healthcare technologies and reviews the state of the art services and applications, recent trends in IoT based healthcare solutions, and various challenges posed including security and privacy issues, which researchers, service providers and end users need to pay higher attention. Further, this paper discusses how innovative IoT enabled technologies like cloud computing, fog computing, blockchain, and big data can be used to leverage modern healthcare facilities and mitigate the burden on healthcare resources

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by Internet of Things (IoT) devices and applies Artificial Intelligence models, specifically, machine learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims to identify the machine-learning models used across different research on Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, the IoT devices used, and gaps and opportunities for further development. The survey results show that 50% of the analyzed research address visual impairment, and, for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constitute the majority of IoT devices. Deep neural networks represent 81% of the machine-learning models applied in the reviewed research.N/

Connected healthcare: Improving patient care using digital health technologies

Now more than ever, traditional healthcare models are being overhauled with digital technologies of Healthcare 4.0 being increasingly adopted. Worldwide, digital devices are improving every stage of the patient care pathway. For one, sensors are being used to monitor patient metrics 24/7, permitting swift diagnosis and interventions. At the treatment stage, 3D printers are currently being investigated for the concept of personalised medicine by allowing patients access to on-demand, customisable therapeutics. Robots are also being explored for treatment, by empowering precision surgery or targeted drug delivery. Within medical logistics, drones are being leveraged to deliver critical treatments to remote areas, collect samples, and even provide emergency aid. To enable seamless integration within healthcare, the Internet of Things technology is being exploited to form closed-loop systems that remotely communicate with one another. This review outlines the most promising healthcare technologies and devices, their strengths, drawbacks, and scopes for clinical adoption

EYECOM: an innovative approach for computer interaction

The world is innovating rapidly, and there is a need for continuous interaction with the technology. Sadly, there do not exist promising options for paralyzed people to interact with the machines i.e., laptops, smartphones, and tabs. A few commercial solutions such as Google Glasses are costly and cannot be afforded by every paralyzed person for such interaction. Towards this end, the thesis proposes a retina-controlled device called EYECOM. The proposed device is constructed from off-the-shelf cost-effective yet robust IoT devices (i.e., Arduino microcontrollers, Xbee wireless sensors, IR diodes, and accelerometer). The device can easily be mounted on to the glasses; the paralyzed person using this device can interact with the machine using simple head movement and eye blinks. The IR detector is located in front of the eye to illuminate the eye region. As a result of illumination, the eye reflects IR light which includes electrical signals and as the eyelids close, the reflected light over eye surface is disrupted, and such change in reflected value is recorded. Further to enable cursor movement onto the computer screen for the paralyzed person a device named accelerometer is used. The accelerometer is a small device, with the size of phalanges, a human thumb bone. The device operates on the principle of axis-based motion sensing and it can be worn as a ring by a paralyzed person. A microcontroller processes the inputs from the IR sensors, accelerometer and transmits them wirelessly via Xbee wireless sensor (i.e., a radio) to another microcontroller attached to the computer. With the help of a proposed algorithm, the microcontroller attached to the computer, on receiving the signals moves cursor onto the computer screen and facilitate performing actions, as simple as opening a document to operating a word-to-speech software. EYECOM has features which can help paralyzed persons to continue their contributions towards the technological world and become an active part of the society. Resultantly, they will be able to perform number of tasks without depending upon others from as simple as reading a newspaper on the computer to activate word-to-voice software

Addressing training data sparsity and interpretability challenges in AI based cellular networks

To meet the diverse and stringent communication requirements for emerging networks use cases, zero-touch arti cial intelligence (AI) based deep automation in cellular networks is envisioned. However, the full potential of AI in cellular networks remains hindered by two key challenges: (i) training data is not as freely available in cellular networks as in other fields where AI has made a profound impact and (ii) current AI models tend to have black box behavior making operators reluctant to entrust the operation of multibillion mission critical networks to a black box AI engine, which allow little insights and discovery of relationships between the configuration and optimization parameters and key performance indicators. This dissertation systematically addresses and proposes solutions to these two key problems faced by emerging networks. A framework towards addressing the training data sparsity challenge in cellular networks is developed, that can assist network operators and researchers in choosing the optimal data enrichment technique for different network scenarios, based on the available information. The framework encompasses classical interpolation techniques, like inverse distance weighted and kriging to more advanced ML-based methods, like transfer learning and generative adversarial networks, several new techniques, such as matrix completion theory and leveraging different types of network geometries, and simulators and testbeds, among others. The proposed framework will lead to more accurate ML models, that rely on sufficient amount of representative training data. Moreover, solutions are proposed to address the data sparsity challenge specifically in Minimization of drive test (MDT) based automation approaches. MDT allows coverage to be estimated at the base station by exploiting measurement reports gathered by the user equipment without the need for drive tests. Thus, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in current and emerging cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. For the first time, this dissertation reveals the existence of an optimal bin width for coverage estimation in the presence of inaccurate user positioning, scarcity of user reports and quantization error. The presented framework can enable network operators to configure the bin size for given positioning accuracy and user density that results in the most accurate MDT based coverage estimation. The lack of interpretability in AI-enabled networks is addressed by proposing a first of its kind novel neural network architecture leveraging analytical modeling, domain knowledge, big data and machine learning to turn black box machine learning models into more interpretable models. The proposed approach combines analytical modeling and domain knowledge to custom design machine learning models with the aim of moving towards interpretable machine learning models, that not only require a lesser training time, but can also deal with issues such as sparsity of training data and determination of model hyperparameters. The approach is tested using both simulated data and real data and results show that the proposed approach outperforms existing mathematical models, while also remaining interpretable when compared with black-box ML models. Thus, the proposed approach can be used to derive better mathematical models of complex systems. The findings from this dissertation can help solve the challenges in emerging AI-based cellular networks and thus aid in their design, operation and optimization

Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review

Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential for maternal and infant health, since maternal and infant health is vital for a healthy society. Over the last few years, researchers have delved into sensing and artificially intelligent healthcare systems for maternal and infant health. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. Since these healthcare systems deal with large amounts of data, significant development is also noted in the computing platforms. The relevant literature reports the potential impact of ICT-enabled systems for improving maternal and infant health. This article reviews wearable sensors and AI algorithms based on existing systems designed to predict the risk factors during and after pregnancy for both mothers and infants. This review covers sensors and AI algorithms used in these systems and analyzes each approach with its features, outcomes, and novel aspects in chronological order. It also includes discussion on datasets used and extends challenges as well as future work directions for researchers

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio