Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks.

Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data (i.e.

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms.

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch.

Real-Time Human Activity Recognition with IMU and Encoder Sensors in Wearable Exoskeleton Robot via Deep Learning Networks.

Wearable exoskeleton robots have become a promising technology for supporting human motions in multiple tasks. Activity recognition in real-time provides useful information to enhance the robot's control assistance for daily tasks. This work implements a real-time activity recognition system based on the activity signals of an inertial measurement unit (IMU) and a pair of rotary encoders integrated into the exoskeleton robot.

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification.

Blood cells carry important information that can be used to represent a person's current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet.

Accelerating 3D Convolutional Neural Network with Channel Bottleneck Module for EEG-Based Emotion Recognition.

Deep learning-based emotion recognition using EEG has received increasing attention in recent years. The existing studies on emotion recognition show great variability in their employed methods including the choice of deep learning approaches and the type of input features. Although deep learning models for EEG-based emotion recognition can deliver superior accuracy, it comes at the cost of high computational complexity.

Development of a Cs-free negative hydrogen ion source system using multi-pulsed plasma sources.

The Korea Atomic Energy Research Institute has recently proposed and developed a novel cesium-free negative hydrogen/deuterium ion source system based on two pulsed plasma sources for fusion and particle accelerator applications. The main feature of this ion source system is the use of both magnetic filters and plasma pulsing (also called the temporal filter). The system operates with two alternate pulsing sequences related to the respective plasma sources, thereby switching the plasmas in the after-glow state in an alternating manner.

Intramedullary nailing of subtrochanteric fractures in elderly patients: Comparative study of helical blade cephalomedullary nail versus reconstruction nail.

Introduction: Intramedullary nailing (IMN), which is a common method for treating subtrochanteric fractures, is conducted as cephalomedullary (CMN) or reconstruction (RCN) nailing. Numerous studies have reported the effectiveness of CMN, which requires a shorter surgery time and provides stronger fixation strength with blade-type devices. However, the radiographic and clinical outcomes of the use of CMN and RCN in elderly patients aged ≥65 years have not been compared yet.

Object Manipulation with an Anthropomorphic Robotic Hand via Deep Reinforcement Learning with a Synergy Space of Natural Hand Poses.

Anthropomorphic robotic hands are designed to attain dexterous movements and flexibility much like human hands. Achieving human-like object manipulation remains a challenge especially due to the control complexity of the anthropomorphic robotic hand with a high degree of freedom. In this work, we propose a deep reinforcement learning (DRL) to train a policy using a synergy space for generating natural grasping and relocation of variously shaped objects using an anthropomorphic robotic hand.

Staged Management of Severely Displaced Calcaneal Fractures With Transarticular Pinning: A Damage Control Strategy.

Background: Severely displaced calcaneal fractures can result in considerable morphology derangement and may be accompanied by soft tissue compromise. Delayed operative restoration of the calcaneal morphology may result in acute retensioning of the damaged soft tissue with associated wound-related complications. In this study, we describe a staged treatment of displaced intra-articular calcaneal fractures that uses temporary transarticular Kirschner wire (K-wire) fixation and staged conversion to definite fixation.

Hand Gesture Recognition Using Single Patchable Six-Axis Inertial Measurement Unit via Recurrent Neural Networks.

Recording human gestures from a wearable sensor produces valuable information to implement control gestures or in healthcare services. The wearable sensor is required to be small and easily worn. Advances in miniaturized sensor and materials research produces patchable inertial measurement units (IMUs).

Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms.

Background And Objective: Deep learning detection and classification from medical imagery are key components for computer-aided diagnosis (CAD) systems to efficiently support physicians leading to an accurate diagnosis of breast lesions.

Methods: In this study, an integrated CAD system of deep learning detection and classification is proposed aiming to improve the diagnostic performance of breast lesions. First, a deep learning YOLO detector is adopted and evaluated for breast lesion detection from entire mammograms.

Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram.

For computer-aided diagnosis (CAD), detection, segmentation, and classification from medical imagery are three key components to efficiently assist physicians for accurate diagnosis. In this chapter, a completely integrated CAD system based on deep learning is presented to diagnose breast lesions from digital X-ray mammograms involving detection, segmentation, and classification. To automatically detect breast lesions from mammograms, a regional deep learning approach called You-Only-Look-Once (YOLO) is used.

Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification.

Background And Objective: Computer automated diagnosis of various skin lesions through medical dermoscopy images remains a challenging task.

Methods: In this work, we propose an integrated diagnostic framework that combines a skin lesion boundary segmentation stage and a multiple skin lesions classification stage. Firstly, we segment the skin lesion boundaries from the entire dermoscopy images using deep learning full resolution convolutional network (FrCN).

Automatic hip geometric feature extraction in DXA imaging using regional random forest.

Background: Hip fracture is considered one of the salient disability factors across the global population. People with hip fractures are prone to become permanently disabled or die from complications. Although currently the premier determiner, bone mineral density has some notable limitations in terms of hip fracture risk assessment.

Femur segmentation in DXA imaging using a machine learning decision tree.

Background: Accurate measurement of bone mineral density (BMD) in dual-energy X-ray absorptiometry (DXA) is essential for proper diagnosis of osteoporosis. Calculation of BMD requires precise bone segmentation and subtraction of soft tissue absorption. Femur segmentation remains a challenge as many existing methods fail to correctly distinguish femur from soft tissue.

A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.

A computer-aided diagnosis (CAD) system requires detection, segmentation, and classification in one framework to assist radiologists efficiently in an accurate diagnosis. In this paper, a completely integrated CAD system is proposed to screen digital X-ray mammograms involving detection, segmentation, and classification of breast masses via deep learning methodologies. In this work, to detect breast mass from entire mammograms, You-Only-Look-Once (YOLO), a regional deep learning approach, is used.

Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks.

Background And Objective: Automatic segmentation of skin lesions in dermoscopy images is still a challenging task due to the large shape variations and indistinct boundaries of the lesions. Accurate segmentation of skin lesions is a key prerequisite step for any computer-aided diagnostic system to recognize skin melanoma.

Methods: In this paper, we propose a novel segmentation methodology via full resolution convolutional networks (FrCN).

Treatment of infra-isthmal femoral fracture with an intramedullary nail: Is retrograde nailing a better option than antegrade nailing?

Introduction: Antegrade intramedullary (IM) nailing is ideal for femoral shaft fractures, but fixing the fracture distal to the isthmal level may be difficult because of medullary canal widening and the proximity of fracture location from the distal femoral joint line. This study aimed to compare treatment results between antegrade and retrograde nailing for infra-isthmal femoral shaft fracture, and to identify influencing factors of nonunion and malalignment.

Materials And Methods: Sixty patients with infra-isthmal femoral shaft fractures treated with IM nailing and followed-up for > 1 year were enrolled in this retrospective study, 38 in the antegrade nailing group, and 22 in the retrograde nailing group.

Denoising images of dual energy X-ray absorptiometry using non-local means filters.

Background: In general, the image quality of high and low energy images of dual energy X-ray absorptiometry (DXA) suffers from noise due to the use of a small amount of X-rays. Denoising of DXA images could be a key process to improve a bone mineral density map, which is derived from a pair of high and low energy images. This could further improve the accuracy of diagnosis of bone fractures and osteoporosis.

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.

Background And Objective: Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Although most previous studies only deal with classification of masses, our proposed YOLO-based CAD system can handle detection and classification simultaneously in one framework.

Wearable Internet of Things - from human activity tracking to clinical integration.

Wearable devices for human activity tracking have been emerging rapidly. Most of them are capable of sending health statistics to smartphones, smartwatches or smart bands. However, they only provide the data for individual analysis and their data is not integrated into clinical practice.

Natural radon reduction rate of the community groundwater system in South Korea.

Tank waters from 13 Community Groundwater Systems (CGS) showed average radon reduction rate of 26.5% (varying from -17.9% to 63.

Prolonged stimulation with low-intensity ultrasound induces delayed increases in spontaneous hippocampal culture spiking activity.

Ultrasound is a promising neural stimulation modality, but an incomplete understanding of its range and mechanism of effect limits its therapeutic application. We investigated the modulation of spontaneous hippocampal spike activity by ultrasound at a lower acoustic intensity and longer time scale than has been previously attempted, hypothesizing that spiking would change conditionally upon the availability of glutamate receptors. Using a 60-channel multielectrode array (MEA), we measured spontaneous spiking across organotypic rat hippocampal slice cultures (N = 28) for 3 min each before, during, and after stimulation with low-intensity unfocused pulsed or sham ultrasound (spatial-peak pulse average intensity 780 μW/cm ) preperfused with artificial cerebrospinal fluid, 300 μM kynurenic acid (KA), or 0.

Synergistic combination of near-infrared irradiation and targeted gold nanoheaters for enhanced photothermal neural stimulation.

Despite a potential of infrared neural stimulation (INS) for modulating neural activities, INS suffers from limited light confinement and bulk tissue heating. Here, a novel methodology for an advanced optical stimulation is proposed by combining near-infrared (NIR) stimulation with gold nanorods (GNRs) targeted to neuronal cell membrane. We confirmed experimentally that in vitro and in vivo neural activation is associated with a local heat generation based on NIR stimulation and GNRs.