Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch.

Mobile health (mHealth) utilizes mobile devices, mobile communication techniques, and the Internet of Things (IoT) to improve not only traditional telemedicine and monitoring and alerting systems, but also fitness and medical information awareness in daily life. In the last decade, human activity recognition (HAR) has been extensively studied because of the strong correlation between people's activities and their physical and mental health. HAR can also be used to care for elderly people in their daily lives.

Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review.

Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD, but the process of early-stage AD can slow down.

Novel Spatio-Temporal Continuous Sign Language Recognition Using an Attentive Multi-Feature Network.

Given video streams, we aim to correctly detect unsegmented signs related to continuous sign language recognition (CSLR). Despite the increase in proposed deep learning methods in this area, most of them mainly focus on using only an RGB feature, either the full-frame image or details of hands and face. The scarcity of information for the CSLR training process heavily constrains the capability to learn multiple features using the video input frames.

SHEL5K: An Extended Dataset and Benchmarking for Safety Helmet Detection.

Wearing a safety helmet is important in construction and manufacturing industrial activities to avoid unpleasant situations. This safety compliance can be ensured by developing an automatic helmet detection system using various computer vision and deep learning approaches. Developing a deep-learning-based helmet detection model usually requires an enormous amount of training data.

A simulated measurement for COVID-19 pandemic using the effective reproductive number on an empirical portion of population: epidemiological models.

COVID-19 as a global pandemic has had an unprecedented impact on the entire world. Projecting the future spread of the virus in relation to its characteristics for a specific suite of countries against a temporal trend can provide public health guidance to governments and organizations. Therefore, this paper presented an epidemiological comparison of the traditional SEIR model with an extended and modified version of the same model by splitting the infected compartment into asymptomatic mild and symptomatic severe.

Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN.

The recent growth of the elderly population has led to the requirement for constant home monitoring as solitary living becomes popular. This protects older people who live alone from unwanted instances such as falling or deterioration caused by some diseases. However, although wearable devices and camera-based systems can provide relatively precise information about human motion, they invade the privacy of the elderly.

Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier.

To prevent disasters and to control and supervise crowds, automated video surveillance has become indispensable. In today's complex and crowded environments, manual surveillance and monitoring systems are inefficient, labor intensive, and unwieldy. Automated video surveillance systems offer promising solutions, but challenges remain.

Using Direct Acyclic Graphs to Enhance Skeleton-Based Action Recognition with a Linear-Map Convolution Neural Network.

Research on the human activity recognition could be utilized for the monitoring of elderly people living alone to reduce the cost of home care. Video sensors can be easily deployed in the different zones of houses to achieve monitoring. The goal of this study is to employ a linear-map convolutional neural network (CNN) to perform action recognition with RGB videos.

Towards Privacy-Preserved Aging in Place: A Systematic Review.

Owing to progressive population aging, elderly people (aged 65 and above) face challenges in carrying out activities of daily living, while placement of the elderly in a care facility is expensive and mentally taxing for them. Thus, there is a need to develop their own homes into smart homes using new technologies. However, this raises concerns of privacy and data security for users since it can be handled remotely.

Robotic assistive and rehabilitation devices leading to motor recovery in upper limb: a systematic review.

Purpose: Stroke, spinal cord injury and other neuromuscular disorders lead to impairments in the human body. Upper limb impairments, especially hand impairments affect activities of daily living (ADL) and reduce the quality of life. The purpose of this review is to compare and evaluate the available robotic rehabilitation and assistive devices that can lead to motor recovery or maintain the current motor functional level.

Trends and Technologies in Rehabilitation of Foot Drop: A Systematic Review.

Introduction: Foot Drop (FD) is a condition, which is very commonly found in post-stoke patients; however it can also be seen in patients with multiple sclerosis, and cerebral palsy. It is a sign of neuromuscular damage caused by the weakness of the muscles. There are various approaches of FD's rehabilitation, such as physiotherapy, surgery, and the use of technological devices.

Implementing FDM 3D Printing Strategies Using Natural Fibers to Produce Biomass Composite.

Current environmental concerns have led to a search of more environmentally friendly manufacturing methods; thus, natural fibers have gained attention in the 3D printing industry to be used as bio-filters along with thermoplastics. The utilization of natural fibers is very convenient as they are easily available, cost-effective, eco-friendly, and biodegradable. Using natural fibers rather than synthetic fibers in the production of the 3D printing filaments will reduce gas emissions associated with the production of the synthetic fibers that would add to the current pollution problem.

Ultrasonic assisted functionalization of MWCNT and synergistic electrocatalytic effect of nano-hydroxyapatite incorporated MWCNT-chitosan scaffolds for sensing of nitrofurantoin.

In the present work, we report the fabrication of stable composite of chitosan hydrogels (CHI) on multiwalled carbon nanotubes (MWCNT) using a simple ultrasonic-assisted method. Also, rod-like hydroxyapatite nanoparticles (HA NPs) were synthesised using a hydrothermal route and were incorporated into the highly conductive MWCNT-CHI scaffolds using an ultrasonication method. The functionalization of MWCNT and preparation of HA NPs on MWCNT-CHI nanocomposite were done using the sonication over the frequency of 37 kHz with the ultrasonic power capable of 150 W (Elmasonic Easy 60H bath sonicator).

Unobtrusive Activity Recognition of Elderly People Living Alone Using Anonymous Binary Sensors and DCNN.

Elderly population (over the age of 60) is predicted to be 1.2 billion by 2025. Most of the elderly people would like to stay alone in their own house due to the high eldercare cost and privacy invasion.

Ubiquitous Emergency Medical Service System Based on Wireless Biosensors, Traffic Information, and Wireless Communication Technologies: Development and Evaluation.

This study presents a new ubiquitous emergency medical service system (UEMS) that consists of a ubiquitous tele-diagnosis interface and a traffic guiding subsystem. The UEMS addresses unresolved issues of emergency medical services by managing the sensor wires for eliminating inconvenience for both patients and paramedics in an ambulance, providing ubiquitous accessibility of patients' biosignals in remote areas where the ambulance cannot arrive directly, and offering availability of real-time traffic information which can make the ambulance reach the destination within the shortest time. In the proposed system, patient's biosignals and real-time video, acquired by wireless biosensors and a webcam, can be simultaneously transmitted to an emergency room for pre-hospital treatment via WiMax/3.