Pair Of Smart Glasses Research Articles

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired.

Over a billion people around the world are disabled, among whom 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, and poor environments and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required in order to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach using a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on the smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image-processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system’s hardware and software design, having constructed their prototype implementations and tested them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than USD 80. Essentially, we provide designs of an inexpensive, miniature green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach enables faster inference and decision-making using relatively low energy with smaller data sizes, as well as faster communications for edge, fog, and cloud computing.

A multi camera unsupervised domain adaptation pipeline for object detection in cultural sites through adversarial learning and self-training

Object detection algorithms allow to enable many interesting applications which can be implemented in different devices, such as smartphones and wearable devices. In the context of a cultural site, implementing these algorithms in a wearable device, such as a pair of smart glasses, allow to enable the use of augmented reality (AR) to show extra information about the artworks and enrich the visitors’ experience during their tour. However, object detection algorithms require to be trained on many well annotated examples to achieve reasonable results. This brings a major limitation since the annotation process requires human supervision which makes it expensive in terms of time and costs. A possible solution to reduce these costs consist in exploiting tools to automatically generate synthetic labeled images from a 3D model of the site. However, models trained with synthetic data do not generalize on real images acquired in the target scenario in which they are supposed to be used. Furthermore, object detectors should be able to work with different wearable devices or different mobile devices, which makes generalization even harder. In this paper, we present a new dataset collected in a cultural site to study the problem of domain adaptation for object detection in the presence of multiple unlabeled target domains corresponding to different cameras and a labeled source domain obtained considering synthetic images for training purposes. We present a new domain adaptation method which outperforms current state-of-the-art approaches combining the benefits of aligning the domains at the feature and pixel level with a self-training process. We release the dataset at the following link https://iplab.dmi.unict.it/OBJ-MDA/ and the code of the proposed architecture at https://github.com/fpv-iplab/STMDA-RetinaNet.

VASE: Smart glasses for the visually impaired

In this paper, Virtual Assistive Smart Eyes (VASE) is described which introduces a new advanced smart glass technology for the visually impaired. Smart glasses in general, have come up as the most calculative device in the modern age to amalgamate humans and machines with the help of machine learning and information technology. These devices are frequently used in industries like medicine and gaming, but their exploration in the field of assistive technology, specifically for the visually impaired is very limited. Majority of these devices are focusing on only a fraction of the features required. It is unfair to leave the average person no choice but to invest in products that are expensive and are not covering all aspects of the problems faced, that they would need to be autonomous in their every-day lives. The proposed VASE is a cost-effective pair of smart glasses which includes many features such as obstruction detection and object recognition. VASE would immensely benefit the visually impaired people in doing their daily tasks and lead an independent life. It will impact the society enormously, as it aids in the prevention of accidents and any other unfortunate incidents, bringing about a major technological advancement benefiting the whole world.

Efficient Electromagnetic Analysis of a Dispersive Head Model Due to Smart Glasses Embedded Antennas at Wi-Fi and 5G Frequencies

Numerical study of electromagnetic interaction between an adjacent antenna and a human head model requires long computation time and large computer memory. In this paper, two speeding up techniques for a dispersive algorithm based on finite-difference time-domain method are used to reduce the required computation time and computer memory. In order to evaluate the validity of these two speeding up techniques, specific absorption rate (SAR) and temperature rise distributions in a dispersive human head model due to radiation from an antenna integrated into a pair of smart glasses are investigated. The antenna integrated into the pair of smart glasses have wireless connectivity at 2.4 GHz and 5th generation (5G) cellular connectivity at 4.9 GHz. Two different positions for the antenna integrated into the frame are considered in this investigation. These techniques provide remarkable reduction in computation time and computer memory.

Wearable video documentation devices in anatomic pathology autopsies

Introduction In the past decade, many wearable devices for video documentation have been released on the free market. However, they have seldom been implemented into autopsy practice. Aim This research aimed to compare several different types of video recording devices and compare their feasibility, both in regards to their form factor and video quality, in everyday autopsy practice. Materials and Methods Five separate wearable devices for video documentation devices were used in the standard autopsy practice of a single pathologist – two box-style sports cameras - Kitvision Escape KVESCAPE4KW (Kondor, Hapton, Lancashire, England) and GoPro Hero 7 Silver (San Mateo, California, USA), a pair of camera glasses – NCS0002 (Spardar Smart Technology Co., Ltd., Shenzhen, China), a pair of smart glasses – Cloud-I II (Topsky Digital Technology Co., Ltd., Shenzhen, China), and Google Glass – XE V2 (Google LLC, Mountain View, California, USA). The five devices were compared both for their pros and cons and for their feasibility in autopsy and educational practice. Results Only the box-style sports cameras and Google Glass provided sufficient video resolution on the initial test to be considered efficient aids. A total of sixty-five full autopsies were documented, using the box-style sports cameras and Google Glass, with ten autopsies being recorded simultaneously with the two devices. Flaws present in both types of recording devices were in their relatively short battery life and the limitation of data that could be stored. Conclusion Video documentation of autopsies using new generation wearable devices is a feasible option for both individual autopsy cases and educational purposes of both students and young pathologists. The different designs are susceptible to individual preferences, however, box-style sports cameras seem to be best suited for autopsy practices.

A Practical Evaluation of Commercial Industrial Augmented Reality Systems in an Industry 4.0 Shipyard

The principles of the Industry 4.0 are guiding manufacturing companies toward more automated and computerized factories. Such principles are also applied in shipbuilding, which usually involves numerous complex processes whose automation will improve its efficiency and performance. Navantia, a company that has been building ships for 300 years, is modernizing its shipyards according to the Industry 4.0 principles with the help of the latest technologies. Augmented reality (AR), which when utilized in an industrial environment is called industrial AR (IAR), is one of such technologies, since it can be applied in numerous situations in order to provide useful and attractive interfaces that allow shipyard operators to obtain information on their tasks and to interact with certain elements that surround them. This article first reviews the state of the art on IAR applications for shipbuilding and smart manufacturing. Then, the most relevant IAR hardware and software tools are detailed, as well as the main use cases for the application of IAR in a shipyard. Next, it is described Navantia’s IAR system, which is based on a fog-computing architecture. Such a system is evaluated when making the use of three IAR devices (a smartphone, a tablet, and a pair of smart glasses), two AR software development kits (ARToolKit and Vuforia) and multiple IAR markers, with the objective of determining their performance in a shipyard workshop and inside a ship under construction. The results obtained show a remarkable performance differences among the different IAR tools and the impact of factors like lighting, pointing out the best combinations of markers, and hardware and software to be used depending on the characteristics of the shipyard scenario.

Running with Technology

The use of wearable devices during running has become commonplace. Although there is ongoing research on interaction techniques for use while running, the effects of the resulting interactions on the natural movement patterns have received little attention so far. While previous studies on pedestrians reported increased task load and reduced walking speed while interacting, running movement further restricts interaction and requires minimizing interferences, e.g. to avoid injuries and maximize comfort. In this paper, we aim to shed light on how interacting with wearable devices affects running movement. We present results from a motion-tracking study (N=12) evaluating changes in movement and task load when users interact with a smartphone, a smartwatch, or a pair of smartglasses while running. In our study, smartwatches required less effort than smartglasses when using swipe input, resulted in less interference with the running movement and were preferred overall. From our results, we infer a number of guidelines regarding interaction design targeting runners.