Development of artificial neural network-based object detection algorithms for low-cost hardware devices

Finally, the fourth work was published in the “WCCI” conference in 2020 and consisted of an individuals' position estimation algorithm based on a novel neural network model for environments with forbidden regions, named “Forbidden Regions Growing Neural Gas”.The human brain is the most complex, powerful and versatile learning machine ever known. Consequently, many scientists of various disciplines are fascinated by its structures and information processing methods. Due to the quality and quantity of the information extracted from the sense of sight, image is one of the main information channels used by humans. However, the massive amount of video footage generated nowadays makes it difficult to process those data fast enough manually. Thus, computer vision systems represent a fundamental tool in the extraction of information from digital images, as well as a major challenge for scientists and engineers. This thesis' primary objective is automatic foreground object detection and classification through digital image analysis, using artificial neural network-based techniques, specifically designed and optimised to be deployed in low-cost hardware devices. This objective will be complemented by developing individuals' movement estimation methods by using unsupervised learning and artificial neural network-based models. The cited objectives have been addressed through a research work illustrated in four publications supporting this thesis. The first one was published in the “ICAE” journal in 2018 and consists of a neural network-based movement detection system for Pan-Tilt-Zoom (PTZ) cameras deployed in a Raspberry Pi board. The second one was published in the “WCCI” conference in 2018 and consists of a deep learning-based automatic video surveillance system for PTZ cameras deployed in low-cost hardware. The third one was published in the “ICAE” journal in 2020 and consists of an anomalous foreground object detection and classification system for panoramic cameras, based on deep learning and supported by low-cost hardware

Multispectral Video Fusion for Non-contact Monitoring of Respiratory Rate and Apnea

Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications

Review of recent advances in frequency-domain near-infrared spectroscopy technologies [Invited]

Over the past several decades, near-infrared spectroscopy (NIRS) has become a popular research and clinical tool for non-invasively measuring the oxygenation of biological tissues, with particular emphasis on applications to the human brain. In most cases, NIRS studies are performed using continuous-wave NIRS (CW-NIRS), which can only provide information on relative changes in chromophore concentrations, such as oxygenated and deoxygenated hemoglobin, as well as estimates of tissue oxygen saturation. Another type of NIRS known as frequency-domain NIRS (FD-NIRS) has significant advantages: it can directly measure optical pathlength and thus quantify the scattering and absorption coefficients of sampled tissues and provide direct measurements of absolute chromophore concentrations. This review describes the current status of FD-NIRS technologies, their performance, their advantages, and their limitations as compared to other NIRS methods. Significant landmarks of technological progress include the development of both benchtop and portable/wearable FD-NIRS technologies, sensitive front-end photonic components, and high-frequency phase measurements. Clinical applications of FD-NIRS technologies are discussed to provide context on current applications and needed areas of improvement. The review concludes by providing a roadmap toward the next generation of fully wearable, low-cost FD-NIRS systems

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

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Wearable brain computer interfaces with near infrared spectroscopy

Brain computer interfaces (BCIs) are devices capable of relaying information directly from the brain to a digital device. BCIs have been proposed for a diverse range of clinical and commercial applications; for example, to allow paralyzed subjects to communicate, or to improve machine human interactions. At their core, BCIs need to predict the current state of the brain from variables measuring functional physiology. Functional near infrared spectroscopy (fNIRS) is a non-invasive optical technology able to measure hemodynamic changes in the brain. Along with electroencephalography (EEG), fNIRS is the only technique that allows non-invasive and portable sensing of brain signals. Portability and wearability are very desirable characteristics for BCIs, as they allow them to be used in contexts beyond the laboratory, extending their usability for clinical and commercial applications, as well as for ecologically valid research. Unfortunately, due to limited access to the brain, non-invasive BCIs tend to suffer from low accuracy in their estimation of the brain state. It has been suggested that feedback could increase BCI accuracy as the brain normally relies on sensory feedback to adjust its strategies. Despite this, presenting relevant and accurate feedback in a timely manner can be challenging when processing fNIRS signals, as they tend to be contaminated by physiological and motion artifacts. In this dissertation, I present the hardware and software solutions we proposed and developed to deal with these challenges. First, I will talk about ninjaNIRS, the wearable open source fNIRS device we developed in our laboratory, which could help fNIRS neuroscience and BCIs to become more accessible. Next, I will present an adaptive filter strategy to recover the neural responses from fNIRS signals in real-time, which could be used for feedback and classification in a BCI paradigm. We showed that our wearable fNIRS device can operate autonomously for up to three hours and can be easily carried in a backpack, while offering noise equivalent power comparable to commercial devices. Our adaptive multimodal Kalman filter strategy provided a six-fold increase in contrast to noise ratio of the brain signals compared to standard filtering while being able to process at least 24 channels at 400 samples per second using a standard computer. This filtering strategy, along with visual feedback during a left vs right motion imagery task, showed a relative increase of accuracy of 37.5% compared to not using feedback. With this, we show that it is possible to present relevant feedback for fNIRS BCI in real-time. The findings on this dissertation might help improve the design of future fNIRS BCIs, and thus increase the usability and reliability of this technology

Can air quality feedback be an effective tool to encourage parents and caregivers to “take smoking right outside”?

Second-hand tobacco smoke (SHS) is a serious cause of ill-health, particularly for children. Smoking indoors leads to high concentrations of SHS and behaviour change interventions have been developed to promote smoke-free homes for children’s benefit. Air-quality feedback – giving parents and caregivers personalised information on the effect of smoking on air pollution at home – has been used in several trials with positive results. A qualitative study was conducted comparing attitudes to SHS and outdoor air pollution. Focus group participants and internet commenters viewed outdoor pollution as a serious health risk, suggesting that comparing SHS to outdoor air pollution could be a promising avenue for increasing awareness about the risks from SHS and promoting behaviour change. An air-quality feedback intervention using a low-cost particle counter was developed and piloted, with lessons from this feasibility study used to develop an innovative intervention using mHealth techniques and remote monitoring for use in a larger trial in four centres around Europe. This study of 68 homes resulted in a statistically significant decline of 17% in measured SHS over the intervention period, but resulted in only eight participants making their homes fully smoke-free. An algorithm was developed to detect smoking in homes using low-cost particulate matter sensors. When tested with data from 144 homes in Scotland, 135 were correctly classified (113 smoking homes, 22 non-smoking homes). Similar predictive rates were achieved in a study of 16 homes in Israel demonstrating that it could be used in different environmental conditions. The algorithm did not enable detection of the periods when smoking occurred in homes. Air-quality feedback can play a role in changing smoking behaviour but may require careful targeting at those with the capability and opportunity to make the change. Future research could use these techniques more widely as part of an “endgame” approach to tobacco control