UMAP for Dimensionality Reduction in Sleep Stage Classification Using EEG Data.

Sleep is vital for wellness, and electroencephalography (EEG) serves as an instrumental tool in the study of sleep. Sleep is classified into four stages: stages N1-N3, and rapid eye movement (REM). To acquire effective and robust EEG features for sleep detection and analysis, we explore the dimensionality reduction effects of Uniform Manifold Approximation and Projection (UMAP) on various features of the EEG signals.

Dining Bowl Modeling and Optimization for Single-Image-Based Dietary Assessment.

In dietary assessment using a single-view food image, an object of known size, such as a checkerboard, is often placed manually in the camera's view as a scale reference to estimate food volume. This traditional scale reference is inconvenient to use because of the manual placement requirement. Consequently, utensils, such as plates and bowls, have been suggested as alternative references.

Editorial for the Special Issue "Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare".

The integration of sensor technology in healthcare has become crucial for disease diagnosis and treatment [...

Estimating Amount of Food in a Circular Dining Bowl from a Single Image.

Unhealthy diet is a top risk factor causing obesity and numerous chronic diseases. To help the public adopt healthy diet, nutrition scientists need user-friendly tools to conduct Dietary Assessment (DA). In recent years, new DA tools have been developed using a smartphone or a wearable device which acquires images during a meal.

Conflict resolution in the case of convective weather cell circumvention.

This research analyzes the area required for the conflict resolution between aircraft in two flows impacted by a convective weather cell (CWC). The CWC is introduced as a constrained area, forbidden to flight through, which affects the air traffic flows. Prior the conflict resolution, two flows and their intersection are relocated away from the CWC area (thus enabling circumvention of the CWC), which is followed by a tuning of the relocated flows intersection angle in order to create the minimal size of the conflict zone (CZ-a circular area centered at the intersection of two flows, which provides aircraft enough space to completely resolve the conflict within).

Improved Wearable Devices for Dietary Assessment Using a New Camera System.

An unhealthy diet is strongly linked to obesity and numerous chronic diseases. Currently, over two-thirds of American adults are overweight or obese. Although dietary assessment helps people improve nutrition and lifestyle, traditional methods for dietary assessment depend on self-report, which is inaccurate and often biased.

A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation.

Knowing the amounts of energy and nutrients in an individual's diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable device. One of the challenges in this approach is to computationally measure the volume of food in a bowl from an image.

Biomechanical influence of thread form on stress distribution over short implants (≤6 mm) using finite element analysis.

The macro and micro design is essential to the biomechanical performance of a short implant. In this study, the implant thread parameters of short implants used in edentulous maxillae will be discussed. The aim of the study is to analyse biomechanical distinctions in different thread parameters over short implants by applying the vertical or oblique load of 130 N on dental prosthesis.

Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System.

It is well known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed to allow an assessment of dietary intake accurately and easily in real-world settings so that effective intervention to manage being overweight, obesity, and related chronic diseases can be developed.

Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features.

Malnutrition, including both undernutrition and obesity, is a significant problem in low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa.

Estimating Dining Plate Size From an Egocentric Image Sequence Without a Fiducial Marker.

Despite the extreme importance of food intake in human health, it is currently difficult to conduct an objective dietary assessment without individuals' self-report. In recent years, a passive method utilizing a wearable electronic device has emerged. This device acquires food images automatically during the eating process.

Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks.

This paper reports on the use of machine learning to delineate data harnessed by fiber-optic distributed acoustic sensors (DAS) using fiber with enhanced Rayleigh backscattering to recognize vibration events induced by human locomotion. The DAS used in this work is based on homodyne phase-sensitive optical time-domain reflectometry (φ-OTDR). The signal-to-noise ratio (SNR) of the DAS was enhanced using femtosecond laser-induced artificial Rayleigh scattering centers in single-mode fiber cores.

Distributed fiber sensor and machine learning data analytics for pipeline protection against extrinsic intrusions and intrinsic corrosions.

This paper presents an integrated technical framework to protect pipelines against both malicious intrusions and piping degradation using a distributed fiber sensing technology and artificial intelligence. A distributed acoustic sensing (DAS) system based on phase-sensitive optical time-domain reflectometry (φ-OTDR) was used to detect acoustic wave propagation and scattering along pipeline structures consisting of straight piping and sharp bend elbow. Signal to noise ratio of the DAS system was enhanced by femtosecond induced artificial Rayleigh scattering centers.

An automatic electronic instrument for accurate measurements of food volume and density.

Objective: Accurate measurements of food volume and density are often required as 'gold standards' for calibration of image-based dietary assessment and food database development. Currently, there is no specialised laboratory instrument for these measurements. We present the design of a new volume of density (VD) meter to bridge this technological gap.

Small Object Augmentation of Urban Scenes for Real-Time Semantic Segmentation.

Semantic segmentation is a key step in scene understanding for autonomous driving. Although deep learning has significantly improved the segmentation accuracy, current highquality models such as PSPNet and DeepLabV3 are inefficient given their complex architectures and reliance on multi-scale inputs. Thus, it is difficult to apply them to real-time or practical applications.

Sub-optimally Solving Actuator Redundancy in a Hybrid Neuroprosthetic System with a Multi-layer Neural Network Structure.

Functional electrical stimulation (FES) has recently been proposed as a supplementary torque assist in lower-limb powered exoskeletons for persons with paraplegia. In the combined system, also known as a hybrid neuroprosthesis, both FES-assist and the exoskeleton act to generate lower-limb torques to achieve standing and walking functions. Due to this actuator redundancy, we are motivated to optimally allocate FES-assist and exoskeleton torque based on a performance index that penalizes FES overuse to minimize muscle fatigue while also minimizing regulation or tracking errors.

Multilabel Feature Extraction Algorithm via Maximizing Approximated and Symmetrized Normalized Cross-Covariance Operator.

Multilabel feature extraction (FE) is an effective preprocessing step to cope with some possible irrelevant, redundant, and noisy features, to reduce computational costs and even improve classification performance. Original normalized cross-covariance operator represents a kernel-based nonlinear dependence measure between features and labels, whose empirical estimator is formulated as a trace operation including two inverse matrices of feature and label kernels with a regularization constant. Due to such a complicated expression, it is impossible to derive an eigenvalue problem for linear FE directly.

Deep Learning for Classification of Normal Swallows in Adults.

Cervical auscultation is a method for assessing swallowing performance. However, its ability to serve as a classification tool for a practical clinical assessment method is not fully understood. In this study, we utilized neural network classification methods in the form of Deep Belief networks in order to classify swallows.

Automatic food detection in egocentric images using artificial intelligence technology.

Objective: To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment.

Design: To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects.

Simultaneous Wireless Power Transfer and Data Communication Using Synchronous Pulse-Controlled Load Modulation.

Wireless Power Transfer (WPT) and wireless data communication are both important problems of research with various applications, especially in medicine. However, these two problems are usually studied separately. In this work, we present a joint study of both problems.

Brain-computer interface combining eye saccade two-electrode EEG signals and voice cues to improve the maneuverability of wheelchair.

Brain-computer interfaces (BCIs) largely augment human capabilities by translating brain wave signals into feasible commands to operate external devices. However, many issues face the development of BCIs such as the low classification accuracy of brain signals and the tedious human-learning procedures. To solve these problems, we propose to use signals associated with eye saccades and blinks to control a BCI interface.

Dictionary learning for sparse representation and classification of neural spikes.

Spike sorting is the problem of identifying and clustering neurons spiking activity from recorded extracellular electro-physiological data. This is important for experimental neuroscience. Existing approaches to solve this problem consist of three steps: spike detection, feature extraction, and clustering.

Quantification of neural reflex and muscular intrinsic contributions to parkinsonian rigidity.

Parkinson's disease (PD) is a progressive neurodegenerative disease characterized by rigidity, bradykinesia, resting tremor, and postural instability. Rigidity, defined as an increased resistance to passive movement of a joint, progresses faster than other motor signs in PD. Rigidity is attributable to both exaggerated neural reflex and altered muscle mechanical properties.

Landmark-Based Indoor Positioning for Visually Impaired Individuals.

Position localization is essential for visually impaired individuals to live independently. Comparing with outdoor environment in which the global positioning system (GPS) can be utilized, indoor positioning is more difficult due to the absence of the GPS signal and complex or unfamiliar building structure. In this paper, a novel landmark-based assistive system is presented for indoor positioning.