Accurate Estimation of a Coil Magnetic Dipole Moment

In this paper, a technique for accurate estimation of the moment of magnetic dipole is proposed. The achievable accuracy is investigated, as a function of measurement noise affecting estimation of magnetic field cartesian components. The proposed technique is validated both via simulations and experimentally.Comment: Preprin

The SmartVision local navigation aid for blind and visually impaired persons

The SmartVision prototype is a small, cheap and easily wearable navigation aid for blind and visually impaired persons. Its functionality addresses global navigation for guiding the user to some destiny, and local navigation for negotiating paths, sidewalks and corridors, with avoidance of static as well as moving obstacles. Local navigation applies to both in- and outdoor situations. In this article we focus on local navigation: the detection of path borders and obstacles in front of the user and just beyond the reach of the white cane, such that the user can be assisted in centering on the path and alerted to looming hazards. Using a stereo camera worn at chest height, a portable computer in a shoulder-strapped pouch or pocket and only one earphone or small speaker, the system is inconspicuous, it is no hindrence while walking with the cane, and it does not block normal surround sounds. The vision algorithms are optimised such that the system can work at a few frames per second

Navigational Guidance – A Deep Learning Approach

The useful navigation guidance is favorable to considerably reducing navigation time. The navigation problems involved with multiple destinations are formulated as the Directed Steiner Tree (DST) problems over directed graphs. In this paper, we propose a deep learning (to be exact, graph neural networks) based approach to tackle the DST problem in a supervised manner. Experiments are conducted to evaluate the proposed approach, and the results suggest that our approach can effectively solve the DST problems. In particular, the accuracy of the network model can reach 95.04% or even higher

TDoA Based Positioning using Ultrasound Signals and Wireless Nodes

In this paper, a positioning technique based on Time Difference of Arrival (TDoA) measurements is analyzed. The proposed approach is designed to consent range and position estimation, using ultrasound transmissions of a stream of chirp pulses, received by a set of wireless nodes. A potential source of inaccuracy introduced by lack of synchronization between transmitting node and receiving nodes is identified and characterized. An algorithm to identify and correct such inaccuracies is presented.Comment: Preprin

a smart multi sensor approach to monitoring weak people in indoor environments

This paper deals with a novel system to assist weak people while exploring indoor environments. The proposed architecture is aimed to monitor the position and inertial behavior of users as well as environmental status (e.g. temperature, humidity, gases leakage, or smoke). The system is based on a Wireless Sensor Network and smart paradigms which extract relevant information from data collected through the multi-sensor architecture. The data collected are then processed to build awareness of User-Environment Interaction and User-Environment Contextualization. This knowledge is used to build information that is useful to the user for safe and efficient exploitation of the environment and to the supervisor for a suitable assessment and management of hazard situations. The paper mainly focuses on the multi-sensor system architecture and smart paradigms used to implement the User-Environment Contextualization feature

Assistive mobility devices focusing on smart walkers : classification and review

In an aging society it is extremely important to develop devices, which can support and aid the elderly in their daily life. This demands means and tools that extend independent living and promote improved health. Thus, the goal of this article is to review the state of the art in the robotic technology for mobility assistive devices for people with mobility disabilities. The important role that robotics can play in mobility assistive devices is presented, as well as the identification and survey of mobility assistive devices subsystems with a particular focus on the walkers technology. The advances in the walkers’ field have been enormous and have shown a great potential on helping people with mobility disabilities. Thus it is presented a review of the available literature of walkers and are discussed major advances that have been made and limitations to be overcome

A Contextual Inquiry of People with Vision Impairments in Cooking

Individuals with vision impairments employ a variety of strategies for object identification, such as pans or soy sauce, in the culinary process. In addition, they often rely on contextual details about objects, such as location, orientation, and current status, to autonomously execute cooking activities. To understand how people with vision impairments collect and use the contextual information of objects while cooking, we conducted a contextual inquiry study with 12 participants in their own kitchens. This research aims to analyze object interaction dynamics in culinary practices to enhance assistive vision technologies for visually impaired cooks. We outline eight different types of contextual information and the strategies that blind cooks currently use to access the information while preparing meals. Further, we discuss preferences for communicating contextual information about kitchen objects as well as considerations for the deployment of AI-powered assistive technologies.Comment: CHI 202

Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the dierent operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects oered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%

Evaluating the impact of lane marking quality on the operation of Autonomous Vehicles

The quality of lane markings is pivotal for safe operations and efficient trajectory generations of connected and autonomous vehicles (AVs). However, most studies are devoted to enhancing in-vehicle detection systems and ignore the impact of faulty lane markings. An instrumented vehicle was employed to mimic the data input of an AV and real-world trials were conducted on (1) live motorways; and (2) a controlled motorway facility. From the live motorway data, causal factors affecting computer vision lane detection and classification algorithms were examined, and an enhanced lane classification algorithm was developed to overcome the limitations posed by poor lane markings. In the controlled motorway facility, experiments to modify the physical appearance of the lane markings were conducted to further test the performance of the developed algorithm. The detection rates of the developed lane classification algorithm were compared with the lane departure warning (LDW) system already implemented in the vehicle. Findings revealed that the LDW system is accurate over 95% and 54% of the time when lanes are faded by 50% and 75% respectively. Further testing on the quality of the lane markings was carried out virtually in such a way that the experiments were replicated in a simulation environment to (1) identify lane marking conditions that can be reliably adopted for safe operations of AVs, (2) estimate the effect of adverse weather and lighting conditions on road markings detection, and (3) address localization issues for AVs. Simulation results show that poor lane markings have a significant negative impact on AV safety, especially in inclement weather and poor light conditions inducing an increase in conflicts and delays. This can be compensated for if more sophisticated sensors are employed in AVs, and the operators of road networks develop lane-based digital road maps. Nicolette Formosa, Ph.D. https://orcid.org/0000-0002-2403-9984 [email protected], Mohammed Quddus, Ph.D. [email protected], Cheuk Ki Man, Ph.D. [email protected], Mohit Kumar Singh, Ph.D., Aff.M.ASCE https://orcid.org/0000-0001-7736-5583 [email protected], Craig Morton, Ph.D. [email protected], and Cansu Bahar Masera