Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors

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

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

A Review of Deep Learning Methods for Photoplethysmography Data

Photoplethysmography (PPG) is a highly promising device due to its advantages in portability, user-friendly operation, and non-invasive capabilities to measure a wide range of physiological information. Recent advancements in deep learning have demonstrated remarkable outcomes by leveraging PPG signals for tasks related to personal health management and other multifaceted applications. In this review, we systematically reviewed papers that applied deep learning models to process PPG data between January 1st of 2017 and July 31st of 2023 from Google Scholar, PubMed and Dimensions. Each paper is analyzed from three key perspectives: tasks, models, and data. We finally extracted 193 papers where different deep learning frameworks were used to process PPG signals. Based on the tasks addressed in these papers, we categorized them into two major groups: medical-related, and non-medical-related. The medical-related tasks were further divided into seven subgroups, including blood pressure analysis, cardiovascular monitoring and diagnosis, sleep health, mental health, respiratory monitoring and analysis, blood glucose analysis, as well as others. The non-medical-related tasks were divided into four subgroups, which encompass signal processing, biometric identification, electrocardiogram reconstruction, and human activity recognition. In conclusion, significant progress has been made in the field of using deep learning methods to process PPG data recently. This allows for a more thorough exploration and utilization of the information contained in PPG signals. However, challenges remain, such as limited quantity and quality of publicly available databases, a lack of effective validation in real-world scenarios, and concerns about the interpretability, scalability, and complexity of deep learning models. Moreover, there are still emerging research areas that require further investigation

Design and Development of a Modular, Multichannel Photoplethysmography System

In this paper, we present the design, development, and validation of a `modular photoplethysmography (PPG) system called ZenPPG. This portable, dual-channel system has the capability to produce ``raw'' PPG signals at two different wavelengths using commercial and/or custom-made PPG sensors. The system consists of five modules, each consisting of circuitry required to perform specific tasks, and are all interconnected by a system bus. The ZenPPG system also facilitates the acquisition of other physiological signals on-demand including electrocardiogram (ECG), respiration, and temperature signals. This report describes the technical details and the evaluation of the ZenPPG along with results from a pilot in vivo study on healthy volunteers. The results from the technical evaluations demonstrate the superiority and flexibility of the system. Also, the systems' compatibility with commercial pulse oximetry sensors such as the Masimo reusable sensors was demonstrated, where good quality raw PPG signals were recorded with the signal-to-noise ratio (SNR) of 50.65 dB. The estimated arterial oxygen saturation (SpO & #x2082;) values from the system were also in close agreement with commercial pulse oximeters, although the accuracy of the reported SpO & #x2082; value is dependent on the calibration function used. Future work is targeted toward the development of variations of each module, including the laser driver and fiber optic module, onboard data acquisition and signal processing modules. The availability of this system will help researchers from a wide range of disciplines to customize and integrate the ZenPPG system to their research needs and will most definitely enhance research in related fields

H2B: Heartbeat-based Secret Key Generation Using Piezo Vibration Sensors

We present Heartbeats-2-Bits (H2B), which is a system for securely pairing wearable devices by generating a shared secret key from the skin vibrations caused by heartbeat. This work is motivated by potential power saving opportunity arising from the fact that heartbeat intervals can be detected energy-efficiently using inexpensive and power-efficient piezo sensors, which obviates the need to employ complex heartbeat monitors such as Electrocardiogram or Photoplethysmogram. Indeed, our experiments show that piezo sensors can measure heartbeat intervals on many different body locations including chest, wrist, waist, neck and ankle. Unfortunately, we also discover that the heartbeat interval signal captured by piezo vibration sensors has low Signal-to-Noise Ratio (SNR) because they are not designed as precision heartbeat monitors, which becomes the key challenge for H2B. To overcome this problem, we first apply a quantile function-based quantization method to fully extract the useful entropy from the noisy piezo measurements. We then propose a novel Compressive Sensing-based reconciliation method to correct the high bit mismatch rates between the two independently generated keys caused by low SNR. We prototype H2B using off-the-shelf piezo sensors and evaluate its performance on a dataset collected from different body positions of 23 participants. Our results show that H2B has an overwhelming pairing success rate of 95.6%. We also analyze and demonstrate H2B's robustness against three types of attacks. Finally, our power measurements show that H2B is very power-efficient

Towards an Effectve Arousal Detecton System for Virtual Reality

Immersive technologies offer the potential to drive engagement and create exciting experiences. A better understanding of the emotional state of the user within immersive experiences can assist in healthcare interventions and the evaluation of entertainment technologies. This work describes a feasibility study to explore the effect of affective video content on heart-rate recordings for Virtual Reality applications. A lowcost reflected-mode photoplethysmographic sensor and an electrocardiographic chest-belt sensor were attached on a novel non-invasive wearable interface specially designed for this study. 11 participants responses were analysed, and heart-rate metrics were used for arousal classification. The reported results demonstrate that the fusion of physiological signals yields to significant performance improvement; and hence the feasibility of our new approach

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology