Microneedles have been using nowadays in many biomedical trends and therefore gained popularity form the last few years. In this paper, the simulation techniques for solid microneedle made up of gold has been given. Usually, a patch of many microneedles arranged in the form of an array has been used for drug deliveries and other biomedical applications but we have considered here a single microneedle to understand the effect of pressure on the tip of microneedle that how it gets deformed and distorted. Here we have performed a simulation and considered the transient structure of gold microneedle in which the effect of pressure on the tip with respect to time has been calculated. The pressure of 1000 MPa has been applied with respect to variable time.
The main goal of this paper is to present a method that allows the automatic detection of communication patterns of patients with a severe state of ASD (Autism Spectrum Disorder), through audio and video processing. Patients with ASD are have social and communicative disorders that make it difficult to analyze their expression, since do not manage natural language properly. The problem could range from a slight difficulty of expression to the total impossibility of speaking. The processing of the routines to be able to extract video and sound patterns is based on the work "ASD: ML perspective for Individual Performance Evaluation". Taking the basis that stereotyped responses can be detected in a systematic way, similar to those that specialized therapists usually describe: gaze fixation, nodding, leg movements, clapping, among others; In this work, a model for the parameterization of said movements and sounds is proposed, through routines specially oriented to detect automatic patterns by audio and video processing. In this work there are the development and first results obtained from audio analysis of a use case, and main findings in relation on how the patient trend to interact with the environment. They allows to suppose that autistic verbal behavior might be interpreted by its physically manifested sounds.
Ergonomic risk assessment is traditionally human-assisted and performed filling forms or through on-site inspection by ergonomists. This frequently leads to inaccuracies due to the subjective bias. Also, it is inefficient and costly given the time and technical knowledge required. Computer-based alternatives are slowly emerging, but so far there is few consensus or uniformity between the underlying practices and technologies. A standardization of data collection in video takes through computer vision offers the opportunity to obtain considerable replication levels, which would increase the reliability of the results and the quality of the data available to ergonomists. In this work we propose a workflow that employs two open-source neural networks: STAF for workers’ body joints detection and tracking, and VIBE for 3D movement estimation. Finally, the ergonomic risk is calculated based on REBA, which is one of the most widespread standard in industrial settings. As data collection may be a bottleneck (as usual in deep learning) we propose the use of virtual scenarios generated in Unity3D. This allows to evaluate and quantify several problems associated with actual video takes, including self-occlusions, camera positioning, illumination, noisy backgrounds, and many others. The results are positively conclusive about both the use of this workflow for actual risk assessment, and the feasibility of virtual environments for controlled experimentation.
On a daily basis, a human being relies on the ability to perceive, recognize, interpret and reproduce a stream of speech. The language is biologically encoded in our brain since the day we are born. Described briefly as the exchange of thoughts, ideas and knowledge, language was difficult to be precisely localized within the brain for many years. The most important areas of the brain involved in the speech and language systems in the brain have been researched and discussed starting with patients whose brain was damaged and the main effects were speech problems in talking or in comprehension of the speech. Definitions of language, descriptions of functionality and as accurate as can be spoken organization of the main systems within the human brain have been tried to be let in the research field. Looking back in the literature, we can see that the most important role in this field is taken by the latest neuroimaging methods developed over the years and which helped researchers in exploring and mapping the brain.
Due to the immense increase in the applications of the Internet of Things, our lives have immensely changed technologically. Smart health care is related to IoT-based smart devices, machines, patients, doctors, and sensors on the internet. Health care has become an important social-economic concern for health care, health needs and availability, and private care for the particularly elderly class of society. Various smart health services are working on these applications are working as individual services, and the need of the hour is to provide a management platform where all these services work as a single unit providing all these facilities to the users. This research focuses on Smart health care systems and proposes a model for a smart health care insurance policy model using Ontology engineering, a knowledge-based system developed for a health insurance policy.
