Real-time WebRTC-based design for a telepresence wheelchair

© 2017 IEEE. This paper presents a novel approach to the telepresence wheelchair system which is capable of real-time video communication and remote interaction. The investigation of this emerging technology aims at providing a low-cost and efficient way for assisted-living of people with disabilities. The proposed system has been designed and developed by deploying the JavaScript with Hyper Text Markup Language 5 (HTML5) and Web Real-time Communication (WebRTC) in which the adaptive rate control algorithm for video transmission is invoked. We conducted experiments in real-world environments, and the wheelchair was controlled from a distance using the Internet browser to compare with existing methods. The results show that the adaptively encoded video streaming rate matches the available bandwidth. The video streaming is high-quality with approximately 30 frames per second (fps) and round trip time less than 20 milliseconds (ms). These performance results confirm that the WebRTC approach is a potential method for developing a telepresence wheelchair system

Real-time video streaming with multi-camera for a telepresence wheelchair

© 2016 IEEE. This paper presents a new approach for telepresence wheelchairs equipped with multiple cameras. The aim of this system is to provide effective assistance for the elderly and people with disabilities. The work explores the integration of the Internet of Things, such as multimedia, wireless Internet communication, and automation control techniques into a powered wheelchair system. In particular, multiple videos are streamed in real-time from an array of cameras mounted on the wheelchair, allowing wide visualization surrounding the wheelchair. By using video communication and interaction, remote users can assist to navigate a wheelchair via the Internet through wireless connections in a distant location. The experimental results show that video streaming can achieve high-quality video with the streaming rate up to 30 frames per second (fps) in real-time. The average round-trip time is under 27 milliseconds (ms). The results confirmed the effectiveness of the proposed system for tele-monitoring and remote control to achieve safer navigation tasks for wheelchair users

Real-time transmission of panoramic images for a telepresence wheelchair

© 2015 IEEE. This paper proposes an approach to transmit panoramic images in real-time for a telepresence wheelchair. The system can provide remote monitoring and assistive assistance for people with disabilities. This study exploits technological advancement in image processing, wireless communication networks, and healthcare systems. High resolution panoramic images are extracted from the camera which is mounted on the wheelchair. The panoramic images are streamed in real-time via a wireless network. The experimental results show that streaming speed is up to 250 KBps. The subjective quality assessments show that the received images are smooth during the streaming period. In addition, in terms of the objective image quality evaluation the average peak signal-to-noise ratio of the reconstructed images is measured to be 39.19 dB which reveals high quality of images

A telepresence wheelchair with 360-degree vision using WebRTC

© 2020 The Author(s). This paper presents an innovative approach to develop an advanced 360-degree vision telepresence wheelchair for healthcare applications. The study aims at improving a wide field of view surrounding the wheelchair to provide safe wheelchair navigation and efficient assistance for wheelchair users. A dual-fisheye camera is mounted in front of the wheelchair to capture images which can be then streamed over the Internet. Aweb real-time communication (WebRTC) protocol was implemented to provide efficient video and data streaming. An estimation model based on artificial neural networks was developed to evaluate the quality of experience (QoE) of video streaming. Experimental results confirmed that the proposed telepresence wheelchair system was able to stream a 360-degree video surrounding the wheelchair smoothly in real-time. The average streaming rate of the entire 360-degree video was 25.83 frames per second (fps), and the average peak signal to noise ratio (PSNR) was 29.06 dB. Simulation results of the proposed QoE estimation scheme provided a prediction accuracy of 94%. Furthermore, the results showed that the designed system could be controlled remotely via the wireless Internet to follow the desired path with high accuracy. The overall results demonstrate the effectiveness of our proposed approach for the 360-degree vision telepresence wheelchair for assistive technology applications

A telepresence wheelchair using cellular network infrastructure in outdoor environments

© 2016 IEEE. Mobile wireless network technology has grown rapidly over the past decade with emerging applications and services. Particularly, the fourth generation (4G) cellular network has acted as a bridge between telecommunication technology and daily life applications. In this paper, we present an investigation into a telepresence wheelchair in outdoor environments employing cellular network infrastructure instead of using local wireless networks in indoor environments. Experiments were carried out to demonstrate remote interaction and control from a long distance and across countries. A large amount of communication data based on real network measurements was collected and analyzed to evaluate the system performance. The experimental results show that a wheelchair system can be controlled remotely in real-time with the acceptable round trip time of less than 400 ms. The results reveal the feasibility of using the 4G network for a telepresence wheelchair in healthcare applications

Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

This corrects the article DOI: 10.1038/sdata.2017.179

Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D

The genetic architecture of type 2 diabetes.

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes