Doppler radar-based non-contact health monitoring for obstructive sleep apnea diagnosis: A comprehensive review

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage

Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage.</jats:p

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Privacy-Protecting Techniques for Behavioral Data: A Survey

Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Driver lane change intention inference using machine learning methods.

Lane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways. This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ. Finally, discussions and conclusions are made in Part Ⅵ. A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour.PhD in Transpor

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available