Kun Qian 교수

An aging population is increasingly prevalent in both developed and developing countries, raising a series of social challenges and economic burdens. In particular, more elderly people are staying alone at home than are living with people who can take care of them. Therefore, assisted living (AL) and health-care monitoring (HM) can be critical issues in this era of human-centered artificial intelligence (AI). In this context, we aim to provide an encompassing review summarizing the state-of-the-

This paper describes a novel approach for the machine-based multifeature classification of the excitation location of snore sounds in the upper airway.

Synthetic aperture radar tomography (TomoSAR) has been extensively employed in 3-D reconstruction in dense urban areas using high-resolution SAR acquisitions. Compressive sensing (CS)-based algorithms are generally considered as the state-of-the art in super-resolving TomoSAR, in particular in the single look case. This superior performance comes at the cost of extra computational burdens, because of the sparse reconstruction, which cannot be solved analytically, and we need to employ computatio

Cardiovascular diseases are the leading cause of death and severely threaten human health in daily life. There have been dramatically increasing demands from both the clinical practice and the smart home application for monitoring the heart status of individuals suffering from chronic cardiovascular diseases. However, experienced physicians who can perform efficient auscultation are still lacking in terms of number. Automatic heart sound classification leveraging the power of advanced signal pro

In recent years, research fields, including ecology, bioacoustics, signal processing, and machine learning, have made bird sound recognition a part of their focus. This has led to significant advancements within the field of ornithology, such as improved understanding of evolution, local biodiversity, mating rituals, and even the implications and realities associated to climate change. The volume of unlabeled bird sound data is now overwhelming, and comparatively little exploration is being made

OPINION article Front. Digit. Health, 26 June 2020 | https://doi.org/10.3389/fdgth.2020.00005

In the past three decades, snoring (affecting more than 30 % adults of the UK population) has been increasingly studied in the transdisciplinary research community involving medicine and engineering. Early work demonstrated that, the snore sound can carry important information about the status of the upper airway, which facilitates the development of non-invasive acoustic based approaches for diagnosing and screening of obstructive sleep apnoea and other sleep disorders. Nonetheless, there are m

Snore related signals (SRS) have been found to carry important information about the snore source and obstruction site in the upper airway of an Obstructive Sleep Apnea/Hypopnea Syndrome (OSAHS) patient. An overnight audio recording of an individual subject is the preliminary and essential material for further study and diagnosis. Automatic detection, segmentation and classification of SRS from overnight audio recordings are significant in establishing a personal health database and in researchi

Leveraging the power of artificial intelligence to facilitate an automatic analysis and monitoring of heart sounds has increasingly attracted tremendous efforts in the past decade. Nevertheless, lacking on standard open-access database made it difficult to maintain a sustainable and comparable research before the first release of the PhysioNet CinC Challenge Dataset. However, inconsistent standards on data collection, annotation, and partition are still restraining a fair and efficient compariso

Location and form of the upper airway obstruction is essential for a targeted therapy of obstructive sleep apnea (OSA). Utilizing snore sounds (SnS) to reveal the pathological characters of OSA patients has been the subject of scientific research for several decades. Fewer studies exist on the evaluation of SnS to identify the corresponding obstruction types in the upper airway. In this study, we propose a novel feature set based on wavelet transform with a support vector machine classifier to d

Computer audition (CA) has experienced a fast development in the past decades by leveraging advanced signal processing and machine learning techniques. In particular, for its noninvasive and ubiquitous character by nature, CA-based applications in healthcare have increasingly attracted attention in recent years. During the tough time of the global crisis caused by the coronavirus disease 2019 (COVID-19), scientists and engineers in data science have collaborated to think of novel ways in prevent

Drowsiness detection is a crucial step for safe driving. A plethora of efforts has been invested on using pervasive sensor data (e.g., video, physiology) empowered by machine learning to build an automatic drowsiness detection system. Nevertheless, most of the existing methods are based on complicated wearables (e.g., electroencephalogram) or computer vision algorithms (e.g., eye state analysis), which makes the relevant systems hardly applicable in the wild. Furthermore, data based on these met

This paper proposes an improved bubble entropy algorithm called Improved Hierarchical Refined Composite Multiscale Multichannel Bubble Entropy (IHRCMMCBE) to characterize the fault characteristics of rotating machinery. By introducing the refined composite multiscale analysis algorithm, the improved hierarchical decomposition algorithm, and the multi-channel data analysis method, the bubble entropy algorithm can more fully characterize the fault characteristics. Then, this method is combined wit