Design and Implementation of a Multifunction Wearable Device to Monitor Sleep Physiological Signals

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

ERS technical standards for using type III devices (limited channel studies) in the diagnosis of sleep disordered breathing in adults and children

For more than three decades, type III devices have been used in the diagnosis of sleep disordered breathing in supervised as well as unsupervised settings. They have satisfactory positive and negative predictive values for detecting obstructive and central sleep apnoea in populations with moderately high pre-test probability of symptoms associated with these events. However, standardisation of commercially available type III devices has never been undertaken and the technical specifications can vary widely. None have been subjected to the same rigorous processes as most other diagnostic modalities in the medical field. Although type III devices do not include acquisition of electroencephalographic signals overnight, the minimum number of physical sensors required to allow for respiratory event scoring using standards outlined by the American Academy of Sleep Medicine remains debatable. This technical standard summarises data on type III studies published since 2007 from multiple perspectives in both adult and paediatric sleep practice. Most importantly, it aims to provide a framework for considering current type III device limitations in the diagnosis of sleep disordered breathing while raising research- and practice-related questions aimed at improving our use of these devices in the present and future.

Applications of Smart Material Sensors and Soft Electronics in Healthcare Wearables for Better User Compliance

The need for innovation in the healthcare sector is essential to meet the demand of a rapidly growing population and the advent of progressive chronic ailments. Over the last decade, real-time monitoring of health conditions has been prioritized for accurate clinical diagnosis and access to accelerated treatment options. Therefore, the demand for wearable biosensing modules for preventive and monitoring purposes has been increasing over the last decade. Application of machine learning, big data analysis, neural networks, and artificial intelligence for precision and various power-saving approaches are used to increase the reliability and acceptance of smart wearables. However, user compliance and ergonomics are key areas that need focus to make the wearables mainstream. Much can be achieved through the incorporation of smart materials and soft electronics. Though skin-friendly wearable devices have been highlighted recently for their multifunctional abilities, a detailed discussion on the integration of smart materials for higher user compliance is still missing. In this review, we have discussed the principles and applications of sustainable smart material sensors and soft electronics for better ergonomics and increased user compliance in various healthcare devices. Moreover, the importance of nanomaterials and nanotechnology is discussed in the development of smart wearables.

Cardiodiagnostics Based on Photoplethysmographic Signals

The article presents a methodology to support the process of correct cardiodiagnostics based on cardio signals recorded with modern optical photoplethysmographic (PPG) sensor devices. An algorithm for preprocessing registered PPG signals and the formation of a time series for the analysis of heart rate variability is presented, which is an important information indicator in the diagnosis of cardiovascular diseases. In order to validate the proposed algorithm, an experimental scheme for synchronous recordings of PPG and electrocardiographic (ECG) signals and the study of the accuracy of the registered signals was created. The obtained results show high accuracy of the studied signals in terms of the following parameters: number of QRS complexes/pulse waves and mean RR intervals/PP intervals and the finding that the proposed algorithm is suitable for preprocessing PPG signals, as well as the possibility of interchangeable use of PPG and ECG. The results of the mathematical analysis of heart rate variability by applying linear methods (Time-Domain and Frequency-Domain) to two groups of people are presented: healthy controls and patients with cardiovascular disease (syncope). After determining the values of the parameters of the methods used, in order to distinguish healthy subjects from sick ones, statistical analysis was applied using t-test and Receiver Operating Characteristics (ROC) analysis. The obtained results show that the linear methods used are suitable for analysing the dynamics of PP interval series and for distinguishing healthy subjects from those with pathological diseases. The presented research and analyses can find applications in guaranteeing correctness and accuracy of conducting cardiodiagnostics in clinical practice.

An IoT-Based Smart System with an MQTT Broker for Individual Patient Vital Sign Monitoring in Potential Emergency or Prehospital Applications

Emergency care is a critical area of medicine whose outcomes are influenced by the time, availability, and accuracy of contextual information. The success of critical or emergency care is determined by the quality and accuracy of the information received during the emergency call and the data collected during emergency transportation. The Internet of Things (IoT) consists of many smart devices and components that communicate via their connection to the Internet, which is used to collect data with sensors that obtain personal health parameters. In the past, most health measurement systems were based on a single dedicated orientation, and few systems had multiple devices on the same platform. In addition to traditional health measurement technologies, most such systems use centralized data transmission, which means that health measurement data have become the exclusive intellectual asset of the system developer. Therefore, this study develops an IoT-based message-broker system that is deployed and demonstrated for five health devices: blood oxygen, blood pressure, forehead temperature, body temperature, and body weight sensors. A central controller accessed by radio-frequency identification (RFID) collects clients' health profiles on the cloud platform. All collected data can be quickly shared, analyzed, and visualized, and the health devices can be changed, added to, and removed reliably when the requirements change. Additionally, following the message queuing telemetry transport (MQTT) protocol, all devices can communicate with each other and be integrated into a higher-level health measurement standard (such as blood pressure plus weight or body temperature plus blood oxygen). We implement a smart healthcare monitoring system (SHMS) and verify its reliability. We use MQTT to establish an open communication format that other organizations can follow to perform individual patient vital sign monitoring in potential applications. The robustness and flexibility of this research can be verified through the addition of other systems. Through this structure, more large-scale health detection devices can be integrated into the method proposed in this research in the future. Personal RFID or health insurance cards can be used for personal services or in medical institutions, and the data can easily be shared through the mechanism of this research. Such information sharing will enable the utilization of medical resources to be maximized.

Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation

In the assistive research area, human-computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.

A Low-Cost Visual Grasp Aid for Neuropathy Patients Using Flexible Three-Dimensional Printed Tactile Sensors

Neuropathy is a nerve-damaging disease that causes those affected to lose feeling in their otherwise functional limbs. It can cause permanent numbing to the peripheral limb of a patient such as a hand or foot. In this report, we present a real-time visualization aid for grasp realization that can be used by patients experiencing numbness of the limb. This wearable electronic device was developed on an open-source microcontroller-based platform. This is a very simple and inexpensive solution. It is referred to as the NeuroGlove, and it provides patients with a visual light scale to allow them to understand the strength of the grasp that they have on any object. A soft tactile sensor was additively manufactured by utilizing a multimaterial direct-print system. The sensor consists of an ionic liquid (IL)-based pressure-sensitive membrane, stretchable electrodes, and insulation membranes. The printed flexible polymeric sensor was evaluated under varying forces. Next, the fabricated sensor was integrated with a microcontroller board where it was programmed to respond in a light scale according to the applied force on the sensor. Finally, the sensor-microcontroller system was installed on a glove to demonstrate a wearable visual aid for neuropathy patients. Additive manufacturing offers the ability for customization in a design, material, and geometry that could potentially lead to printing sensors on prosthetic or robotic hands.

Recent advances in wearable sensors and portable electronics for sleep monitoring

Despite the increasing awareness of the importance of sleep, the number of people suffering from insufficient sleep has increased every year. The gold-standard sleep assessment uses polysomnography (PSG) with various sensors to identify sleep patterns and disorders. However, due to the high cost of PSG and limited availability, many people with sleep disorders are left undiagnosed. Recent wearable sensors and electronics enable portable, continuous monitoring of sleep at home, overcoming the limitations of PSG. This report reviews the advances in wearable sensors, miniaturized electronics, and system packaging for home sleep monitoring. New devices available in the market and systems are collectively summarized based on their overall structure, form factor, materials, and sleep assessment method. It is expected that this review provides a comprehensive view of newly developed technologies and broad insights on wearable sensors and portable electronics toward advanced sleep monitoring as well as at-home sleep assessment.

Voice Assistant Application for Avoiding Sedentarism in Elderly People Based on IoT Technologies

The rise in the use of virtual assistants such as Siri, Google Assistant, or Alexa among different sectors of society is facilitating access to information and services that were previously inconceivable due to the existing digital divide due to age. This situation allows especially the elderly to perform tasks much more easily and to access applications and services that could be a challenge for them with other digital user interfaces. With this in mind, the EMERITI project aims to improve the lives of the elderly through the use of virtual assistants in different case studies. In this sense, virtual voice assistants along with the use of Internet of Things (IoT) technologies can contribute to avoid sedentarism in the elderly; however, it is necessary to address the problem of proactivity presented by the virtual assistants available in the market. This article presents a solution that, through the use of activity monitoring smart bracelets, IoT devices and virtual voice assistants allow the elderly to monitor their daily physical activity simply by using their voice and therefore prevent them from sedentary patterns. Finally, this study presents the technical results obtained after the deployment of the proposed system and discusses the main advantages and the current challenges of the use of virtual assistants in applications to prevent sedentary lifestyles in the elderly.