Deep Learning Systems for Advanced Driving Assistance

Next generation cars embed intelligent assessment of car driving safety through innovative solutions often based on usage of artificial intelligence. The safety driving monitoring can be carried out using several methodologies widely treated in scientific literature. In this context, the author proposes an innovative approach that uses ad-hoc bio-sensing system suitable to reconstruct the physio-based attentional status of the car driver. To reconstruct the car driver physiological status, the author proposed the use of a bio-sensing probe consisting of a coupled LEDs at Near infrared (NiR) spectrum with a photodetector. This probe placed over the monitored subject allows to detect a physiological signal called PhotoPlethysmoGraphy (PPG). The PPG signal formation is regulated by the change in oxygenated and non-oxygenated hemoglobin concentration in the monitored subject bloodstream which will be directly connected to cardiac activity in turn regulated by the Autonomic Nervous System (ANS) that characterizes the subject's attention level. This so designed car driver drowsiness monitoring will be combined with further driving safety assessment based on correlated intelligent driving scenario understanding

Visual Saliency Detection in Advanced Driver Assistance Systems

Visual Saliency refers to the innate human mechanism of focusing on and extracting important features from the observed environment. Recently, there has been a notable surge of interest in the field of automotive research regarding the estimation of visual saliency. While operating a vehicle, drivers naturally direct their attention towards specific objects, employing brain-driven saliency mechanisms that prioritize certain elements over others. In this investigation, we present an intelligent system that combines a drowsiness detection system for drivers with a scene comprehension pipeline based on saliency. To achieve this, we have implemented a specialized 3D deep network for semantic segmentation, which has been pretrained and tailored for processing the frames captured by an automotive-grade external camera. The proposed pipeline was hosted on an embedded platform utilizing the STA1295 core, featuring ARM A7 dual-cores, and embeds an hardware accelerator. Additionally, we employ an innovative biosensor embedded on the car steering wheel to monitor the driver drowsiness, gathering the PhotoPlethysmoGraphy (PPG) signal of the driver. A dedicated 1D temporal deep convolutional network has been devised to classify the collected PPG time-series, enabling us to assess the driver level of attentiveness. Ultimately, we compare the determined attention level of the driver with the corresponding saliency-based scene classification to evaluate the overall safety level. The efficacy of the proposed pipeline has been validated through extensive experimental results

MODELOWANIE I ANALIZA SKURCZOWEGO I ROZKURCZOWEGO CIŚNIENIA KRWI Z WYKORZYSTANIEM SYGNAŁÓW EKG I PPG

Taking into account the peculiarities of using the MAX86150 evaluation system for measuring ECG and PPG signals, mathematical models were developed for indirect determination of systolic and diastolic pressure using fingers on the hand, which were tested in the MATLAB environment. Received ECG and PPG signals. Based on the proposed mathematical models, ECG and PPG signals were processed in the MATLAB package and the results of indirect measurement of blood pressure were presented.Biorąc pod uwagę specyfikę wykorzystania systemu oceny MAX86150 do pomiaru sygnałów EKG i PPG, opracowano modele matematyczne do pośredniego określania ciśnienia skurczowego i rozkurczowego używając palców dłoni, które zostały przetestowane w środowisku MATLAB. Otrzymano sygnały EKG i PPG. W oparciu o zaproponowane modele matematyczne, sygnały EKG i PPG zostały przetworzone w pakiecie MATLAB oraz przedstawiono wyniki pośredniego pomiaru ciśnienia krwi

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

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

MODELING AND ANALYSIS OF SYSTOLIC AND DIASTOLIC BLOOD PRESSURE USING ECG AND PPG SIGNALS

Taking into account the peculiarities of using the MAX86150 evaluation system for measuring ECG and PPG signals, mathematical models were developed for indirect determination of systolic and diastolic pressure using fingers on the hand, which were tested in the MATLAB environment. Received ECG and PPG signals. Based on the proposed mathematical models, ECG and PPG signals were processed in the MATLAB package and the results of indirect measurement of blood pressure were presented

Recent Trends in Computational Research on Diseases

Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields. Statistical methodologies based on high-performance computing and big data analysis are now indispensable for the qualitative and quantitative understanding of experimental results. In fact, the last few decades have witnessed drastic improvements in high-throughput experiments in health science, for example, mass spectrometry, DNA microarray, next generation sequencing, etc. Those methods have been providing massive data involving four major branches of omics (genomics, transcriptomics, proteomics, and metabolomics). Information about amino acid sequences, protein structures, and molecular structures are fundamental data for the prediction of bioactivity of chemical compounds when screening drugs. On the other hand, cell imaging, clinical imaging, and personal healthcare devices are also providing important data concerning the human body and disease. In parallel, various methods of mathematical modelling such as machine learning have developed rapidly. All of these types of data can be utilized in computational approaches to understand disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, and much more. In this Special Issue, we have published 8 excellent papers dedicated to a variety of computational problems in the biomedical field from the genomic level to the whole-person physiological level

Signal Processing for Early Warning Arrhythmia Detection and Survival Prediction for Clinical Decision

According to the British Heart Foundation, UK, there is a population of around 7 million living in the UK with heart and circulatory diseases; about 25% of all the deaths in the UK are caused by cardiovascular diseases and more than 30,000 people a year suffer cardiac arrest out-of-hospital. As people all over the world, continue to live busy and stressful lives, a vast majority of people start showing cardiac arrhythmia-related symptoms which, if not treated in time may lead to a serious heart condition or even sudden cardiac death. To identify the early-warning signs in cardiac arrhythmia, methods to identify the precursors to fatal arrhythmia were developed in this research study, using a wearable kit. To enable accurate classification between arrhythmic beats, novel feature extraction algorithms using spectral components were developed. Often a fatal cardiac arrhythmia, or a serious injury, may lead to trauma and in such situations, it becomes imperative that the critical care teams have adequate information about the patient’s health status at remote location following an ambulatory response. A real-time trauma scoring algorithm was developed, and correlation and regression analyses were performed to arrive at these scores using the physiological parameters and vital signs. It was found that with appropriate feature extraction algorithms, supervised learning classifiers could identify the precursors to arrhythmia in real time and on a resource-constrained device, regardless of time and location. The trauma scoring algorithm, implemented using ICU patients’ dataset, produced values that agreed with the patients’ status and events could be logged to electronic health records using standard clinical coding systems. It could, therefore, be concluded that regardless of situation and location of an individual, fatal arrhythmia and trauma events could be identified ahead of time before reaching a state of emergency

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Wearable Wireless Devices

No abstract available