A reconfigurable real-time morphological system for augmented vision

There is a significant number of visually impaired individuals who suffer sensitivity loss to high spatial frequencies, for whom current optical devices are limited in degree of visual aid and practical application. Digital image and video processing offers a variety of effective visual enhancement methods that can be utilised to obtain a practical augmented vision head-mounted display device. The high spatial frequencies of an image can be extracted by edge detection techniques and overlaid on top of the original image to improve visual perception among the visually impaired. Augmented visual aid devices require highly user-customisable algorithm designs for subjective configuration per task, where current digital image processing visual aids offer very little user-configurable options. This paper presents a highly user-reconfigurable morphological edge enhancement system on field-programmable gate array, where the morphological, internal and external edge gradients can be selected from the presented architecture with specified edge thickness and magnitude. In addition, the morphology architecture supports reconfigurable shape structuring elements and configurable morphological operations. The proposed morphology-based visual enhancement system introduces a high degree of user flexibility in addition to meeting real-time constraints capable of obtaining 93 fps for high-definition image resolution

A feasibility study for the provision of electronic healthcare tools and services in areas of Greece, Cyprus and Italy

Background: Through this paper, we present the initial steps for the creation of an integrated platform for the provision of a series of eHealth tools and services to both citizens and travelers in isolated areas of thesoutheast Mediterranean, and on board ships travelling across it. The platform was created through an INTERREG IIIB ARCHIMED project called INTERMED. Methods: The support of primary healthcare, home care and the continuous education of physicians are the three major issues that the proposed platform is trying to facilitate. The proposed system is based on state-of-the-art telemedicine systems and is able to provide the following healthcare services: i) Telecollaboration and teleconsultation services between remotely located healthcare providers, ii) telemedicine services in emergencies, iii) home telecare services for "at risk" citizens such as the elderly and patients with chronic diseases, and iv) eLearning services for the continuous training through seminars of both healthcare personnel (physicians, nurses etc) and persons supporting "at risk" citizens. These systems support data transmission over simple phone lines, internet connections, integrated services digital network/digital subscriber lines, satellite links, mobile networks (GPRS/3G), and wireless local area networks. The data corresponds, among others, to voice, vital biosignals, still medical images, video, and data used by eLearning applications. The proposed platform comprises several systems, each supporting different services. These were integrated using a common data storage and exchange scheme in order to achieve system interoperability in terms of software, language and national characteristics. Results: The platform has been installed and evaluated in different rural and urban sites in Greece, Cyprus and Italy. The evaluation was mainly related to technical issues and user satisfaction. The selected sites are, among others, rural health centers, ambulances, homes of "at-risk" citizens, and a ferry. Conclusions: The results proved the functionality and utilization of the platform in various rural places in Greece, Cyprus and Italy. However, further actions are needed to enable the local healthcare systems and the different population groups to be familiarized with, and use in their everyday lives, mature technological solutions for the provision of healthcare services

Index to NASA Tech Briefs, 1975

This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

Deep learning model for doors detection a contribution for context awareness recognition of patients with Parkinson’s disease

Freezing of gait (FoG) is one of the most disabling motor symptoms in Parkinson’s disease, which is described as a symptom where walking is interrupted by a brief, episodic absence, or marked reduction, of forward progression despite the intention to continue walking. Although FoG causes are multifaceted, they often occur in response of environment triggers, as turnings and passing through narrow spaces such as a doorway. This symptom appears to be overcome using external sensory cues. The recognition of such environments has consequently become a pertinent issue for PD-affected community. This study aimed to implement a real-time DL-based door detection model to be integrated into a wearable biofeedback device for delivering on-demand proprioceptive cues. It was used transfer-learning concepts to train a MobileNet-SSD in TF environment. The model was then integrated in a RPi being converted to a faster and lighter computing power model using TensorFlow Lite settings. Model performance showed a considerable precision of 97,2%, recall of 78,9% and a good F1-score of 0,869. In real-time testing with the wearable device, DL-model showed to be temporally efficient (~2.87 fps) to detect with accuracy doors over real-life scenarios. Future work will include the integration of sensory cues with the developed model in the wearable biofeedback device aiming to validate the final solution with end-users

Review of Machine Vision-Based Electronic Travel Aids

Visual impaired people have navigation and mobility problems on the road. Up to now, many approaches have been conducted to help them navigate around using different sensing techniques. This paper reviews several machine vision- based Electronic Travel Aids (ETAs) and compares them with those using other sensing techniques. The functionalities of machine vision-based ETAs are classified from low-level image processing such as detecting the road regions and obstacles to high-level functionalities such as recognizing the digital tags and texts. In addition, the characteristics of the ETA systems for blind people are particularly discussed

Are wearable electronic vision enhancement systems (wEVES) beneficial for people with age-related macular degeneration? A scoping review

INTRODUCTION: Age-related macular degeneration (AMD) is the most common cause of irreversible visual impairment in the United Kingdom. It has a wide-ranging detrimental impact on daily living, including impairment of functional ability and quality of life. Assistive technology designed to overcome this impairment includes wearable electronic vision enhancement systems (wEVES). This scoping review assesses the usefulness of these systems for people with AMD. METHODS: Four databases (Cumulative Index to Nursing and Allied Health Literature, PubMed, Web of Science and Cochrane CENTRAL) were searched to identify papers that investigated image enhancement with a head-mounted electronic device on a sample population that included people with AMD. RESULTS: Thirty-two papers were included: 18 studied the clinical and functional benefits of wEVES, 11 investigated use and usability and 3 discussed sickness and adverse effects. CONCLUSIONS: Wearable electronic vision enhancement systems provide hands-free magnification and image enhancement producing significant improvements in acuity, contrast sensitivity and aspects of laboratory-simulated daily activity. Adverse effects were infrequent, minor and spontaneously resolved with the removal of the device. However, when symptoms arose, they sometimes persisted with continued device usage. There are multi-factorial influences and a diversity of user opinions on promotors to successful device use. These factors are not exclusively driven by visual improvement and incorporate other issues including device weight, ease of use and inconspicuous design. There is insufficient evidence of any cost-benefit analysis for wEVES. However, it has been shown that a user's decision to make a purchase evolves over time, with their estimates of cost falling below the retail price of the devices. Additional research is needed to understand the specific and distinct benefits of wEVES for people with AMD. Further patient-centred research should assess the benefits of wEVES in user-led activities when directly compared with alternative coping strategies, allowing professionals and users to make better prescribing and purchasing decisions

Edge AI-Based Vein Detector for Efficient Venipuncture in the Antecubital Fossa

Assessing the condition and visibility of veins is a crucial step before obtaining intravenous access in the antecubital fossa, which is a common procedure to draw blood or administer intravenous therapies (IV therapies). Even though medical practitioners are highly skilled at intravenous cannulation, they usually struggle to perform the procedure in patients with low visible veins due to fluid retention, age, overweight, dark skin tone, or diabetes. Recently, several investigations proposed combining Near Infrared (NIR) imaging and deep learning (DL) techniques for forearm vein segmentation. Although they have demonstrated compelling results, their use has been rather limited owing to the portability and precision requirements to perform venipuncture. In this paper, we aim to contribute to bridging this gap using three strategies. First, we introduce a new NIR-based forearm vein segmentation dataset of 2,016 labelled images collected from 1,008 subjects with low visible veins. Second, we propose a modified U-Net architecture that locates veins specifically in the antecubital fossa region of the examined patient. Finally, a compressed version of the proposed architecture was deployed inside a bespoke, portable vein finder device after testing four common embedded microcomputers and four common quantization modalities. Experimental results showed that the model compressed with Dynamic Range Quantization and deployed on a Raspberry Pi 4B card produced the best execution time and precision balance, with 5.14 FPS and 0.957 of latency and Intersection over Union (IoU), respectively. These results show promising performance inside a resource-restricted low-cost device.Comment: Accepted for publication in MICAI 2023, Part II, LNCS 1439