Blind's Mate - A Navigation System for Blind People

Visually challenged people rely on companions around them on daily basis. Basic chores of day to day life pose a challenge for people with impaired vision. Walking on streets while dodging obstacles and navigating through roads to reach desired destination independently is one such challenge. Proposed system 'Blind's Mate' is a combination of software and hardware technology that can be used to improve the walking experience of blind people. Blind’s Mate is a combination of smart stick and a mobile app that can alert people of impending obstacles and water pits while providing audio feed to the users about obstacles encountered during walking. This stick is embedded with ultrasonic sensor to detect any obstacles in front of the user. The app gives live feed to the user continuously via earphones. Speech warning messages are activated when any obstacle is detected. The proposed stick is of low cost, has fast response, low power consumption, light weight and an effective means to help blind people finding their way on streets with ease.

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Feasibility of using a mobile App to monitor and report COVID-19 related symptoms and people’s movements in Uganda

PLoS ONE ◽

10.1371/journal.pone.0260269 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0260269

Author(s):

Levicatus Mugenyi ◽

Rebecca Namugabwe Nsubuga ◽

Irene Wanyana ◽

Winters Muttamba ◽

Nazarius Mbona Tumwesigye ◽

...

Keyword(s):

Large Scale ◽

Mobile Apps ◽

Low Cost ◽

Scale Up ◽

Age Groups ◽

Daily Basis ◽

Mobile App ◽

Additional Tool ◽

Runny Nose ◽

Study Sites

Background Feasibility of mobile Apps to monitor diseases has not been well documented particularly in developing countries. We developed and studied the feasibility of using a mobile App to collect daily data on COVID-19 symptoms and people’s movements. Methods We used an open source software “KoBo Toolbox” to develop the App and installed it on low cost smart mobile phones. We named this App “Wetaase” (“protect yourself”). The App was tested on 30 selected households from 3 densely populated areas of Kampala, Uganda, and followed them for 3 months. One trained member per household captured the data in the App for each enrolled member and uploaded it to a virtual server on a daily basis. The App is embedded with an algorithm that flags participants who report fever and any other COVID-19 related symptom. Results A total of 101 participants were enrolled; 61% female; median age 23 (interquartile range (IQR): 17–36) years. Usage of the App was 78% (95% confidence interval (CI): 77.0%–78.8%). It increased from 40% on day 1 to a peak of 81% on day 45 and then declined to 59% on day 90. Usage of the App did not significantly vary by site, sex or age. Only 57/6617 (0.86%) records included a report of at least one of the 17 listed COVID-19 related symptoms. The most reported symptom was flu/runny nose (21%) followed by sneezing (15%), with the rest ranging between 2% and 7%. Reports on movements away from home were 45% with 74% going to markets or shops. The participants liked the “Wetaase” App and recommended it for use as an alert system for COVID-19. Conclusion Usage of the “Wetaase” App was high (78%) and it was similar across the three study sites, sex and age groups. Reporting of symptoms related to COVID-19 was low. Movements were mainly to markets and shops. Users reported that the App was easy to use and recommended its scale up. We recommend that this App be assessed at a large scale for feasibility, usability and acceptability as an additional tool for increasing alerts on COVID-19 in Uganda and similar settings.

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Steering a Robotic Wheelchair Based on Voice Recognition System Using Convolutional Neural Networks

Electronics ◽

10.3390/electronics11010168 ◽

2022 ◽

Vol 11 (1) ◽

pp. 168

Author(s):

Mohsen Bakouri ◽

Mohammed Alsehaimi ◽

Husham Farouk Ismail ◽

Khaled Alshareef ◽

Ali Ganoun ◽

...

Keyword(s):

Performance Test ◽

Low Cost ◽

Voice Recognition ◽

Recognition System ◽

Mobile App ◽

Raspberry Pi ◽

Speech Corpus ◽

Dc Motor Drives ◽

Software And Hardware ◽

Robotic Wheelchair

Many wheelchair people depend on others to control the movement of their wheelchairs, which significantly influences their independence and quality of life. Smart wheelchairs offer a degree of self-dependence and freedom to drive their own vehicles. In this work, we designed and implemented a low-cost software and hardware method to steer a robotic wheelchair. Moreover, from our method, we developed our own Android mobile app based on Flutter software. A convolutional neural network (CNN)-based network-in-network (NIN) structure approach integrated with a voice recognition model was also developed and configured to build the mobile app. The technique was also implemented and configured using an offline Wi-Fi network hotspot between software and hardware components. Five voice commands (yes, no, left, right, and stop) guided and controlled the wheelchair through the Raspberry Pi and DC motor drives. The overall system was evaluated based on a trained and validated English speech corpus by Arabic native speakers for isolated words to assess the performance of the Android OS application. The maneuverability performance of indoor and outdoor navigation was also evaluated in terms of accuracy. The results indicated a degree of accuracy of approximately 87.2% of the accurate prediction of some of the five voice commands. Additionally, in the real-time performance test, the root-mean-square deviation (RMSD) values between the planned and actual nodes for indoor/outdoor maneuvering were 1.721 × 10−5 and 1.743 × 10−5, respectively.

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Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

Journal of Sensor and Actuator Networks ◽

10.3390/jsan10010013 ◽

2021 ◽

Vol 10 (1) ◽

pp. 13

Author(s):

Claudia Campolo ◽

Giacomo Genovese ◽

Antonio Iera ◽

Antonella Molinaro

Keyword(s):

Resource Constraints ◽

Smart Cities ◽

Traditional Approach ◽

Low Cost ◽

Iot Applications ◽

Software And Hardware ◽

Iot Devices ◽

Consumer Devices ◽

Hardware Platforms ◽

Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

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New Omnidirectional Sensor Based on Open-Source Software and Hardware for Tracking and Backtracking of Dual-Axis Solar Trackers in Photovoltaic Plants

Sensors ◽

10.3390/s21030726 ◽

2021 ◽

Vol 21 (3) ◽

pp. 726

Author(s):

Francisco J. Gómez-Uceda ◽

José Ramirez-Faz ◽

Marta Varo-Martinez ◽

Luis Manuel Fernández-Ahumada

Keyword(s):

Open Source Software ◽

Energy Production ◽

Scientific Community ◽

Radiative Capture ◽

Low Cost ◽

Free Software ◽

Solar Tracking ◽

Software And Hardware ◽

Celestial Sphere ◽

Photovoltaic Plants

In this work, an omnidirectional sensor that enables identification of the direction of the celestial sphere with maximum solar irradiance is presented. The sensor, based on instantaneous measurements, functions as a position server for dual-axis solar trackers in photovoltaic plants. The proposed device has been developed with free software and hardware, which makes it a pioneering solution because it is open and accessible as well as capable of being improved by the scientific community, thereby contributing to the rapid advancement of technology. In addition, the device includes an algorithm developed ex professo that makes it possible to predetermine the regions of the celestial sphere for which, according to the geometric characteristics of the PV plant, there would be shading between the panels. In this way, solar trackers do not have to locate the Sun’s position at all times according to astronomical models, while taking into account factors such as shadows or cloudiness that also affect levels of incident irradiance on solar collectors. Therefore, with this device, it is possible to provide photovoltaic plants with dual-axis solar tracking with a low-cost device that helps to optimise the trajectory of the trackers and, consequently, their radiative capture and energy production.

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Satellite-Based Personal UV Dose Estimation

Atmosphere ◽

10.3390/atmos12020268 ◽

2021 ◽

Vol 12 (2) ◽

pp. 268

Author(s):

Todd C. Harris ◽

Laurent Vuilleumier ◽

Claudine Backes ◽

Athanasios Nenes ◽

David Vernez

Keyword(s):

Measurement Errors ◽

Low Cost ◽

Public Health Research ◽

Mobile App ◽

Population Exposure ◽

Dose Estimation ◽

Annual Dose ◽

Significant Step ◽

Uv Dose ◽

Dosimetry Measurement

Epidemiology and public health research relating to solar ultraviolet (UV) exposure usually relies on dosimetry to measure UV doses received by individuals. However, measurement errors affect each dosimetry measurement by unknown amounts, complicating the analysis of such measurements and their relationship to the underlying population exposure and the associated health outcomes. This paper presents a new approach to estimate UV doses without the use of dosimeters. By combining new satellite-derived UV data to account for environmental factors and simulation-based exposure ratio (ER) modelling to account for individual factors, we are able to estimate doses for specific exposure periods. This is a significant step forward for alternative dosimetry techniques which have previously been limited to annual dose estimation. We compare our dose estimates with dosimeter measurements from skiers and builders in Switzerland. The dosimetry measurements are expected to be slightly below the true doses due to a variety of dosimeter-related measurement errors, mostly explaining why our estimates are greater than or equal to the corresponding dosimetry measurements. Our approach holds much promise as a low-cost way to either complement or substitute traditional dosimetry. It can be applied in a research context, but is also fundamentally well-suited to be used as the basis for a dose-estimating mobile app that does not require an external device.

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Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering

Sensors ◽

10.3390/s20195697 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5697

Author(s):

Chang Sun ◽

Shihong Yue ◽

Qi Li ◽

Huaxiang Wang

Keyword(s):

Clustering Algorithm ◽

Two Phase Flow ◽

Low Cost ◽

Fast Response ◽

New Method ◽

Phase Flow ◽

Reference Distribution ◽

Two Phase ◽

Long Time ◽

Solid Liquid

Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics. However, when the existing ERT method is used to measure the CF value in solid–liquid two-phase flow in dredging engineering, there are at least three problems: (1) the dependence of reference distribution whose CF value is zero; (2) the size of the detected objects may be too small to be found by ERT; and (3) there is no efficient way to estimate the effect of artifacts in ERT. In this paper, we proposed a method based on the clustering technique, where a fast-fuzzy clustering algorithm is used to partition the ERT image to three clusters that respond to liquid, solid phases, and their mixtures and artifacts, respectively. The clustering algorithm does not need any reference distribution in the CF estimation. In the case of small solid objects or artifacts, the CF value remains effectively computed by prior information. To validate the new method, a group of typical CF estimations in dredging engineering were implemented. Results show that the new method can effectively overcome the limitations of the existing method, and can provide a practical and more accurate way for CF estimation.

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Advances in the Structural Health Monitoring of Bridges Using Piezoelectric Transducers

Sensors ◽

10.3390/s18124312 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4312 ◽

Cited By ~ 6

Author(s):

Yunzhu Chen ◽

Xingwei Xue

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Piezoelectric Materials ◽

Low Cost ◽

Rapid Development ◽

Concrete Strength ◽

Steel Corrosion ◽

Fast Response ◽

Future Application ◽

Structural Health

With the rapid development of the world’s transportation infrastructure, many long-span bridges were constructed in recent years, especially in China. However, these bridges are easily subjected to various damages due to dynamic loads (such as wind-, earthquake-, and vehicle-induced vibration) or environmental factors (such as corrosion). Therefore, structural health monitoring (SHM) is vital to guarantee the safety of bridges in their service lives. With its wide frequency response range, fast response, simple preparation process, ease of processing, low cost, and other advantages, the piezoelectric transducer is commonly employed for the SHM of bridges. This paper summarizes the application of piezoelectric materials for the SHM of bridges, including the monitoring of the concrete strength, bolt looseness, steel corrosion, and grouting density. For each problem, the application of piezoelectric materials in different research methods is described. The related data processing methods for four types of bridge detection are briefly summarized, and the principles of each method in practical application are listed. Finally, issues to be studied when using piezoelectric materials for monitoring are discussed, and future application prospects and development directions are presented.

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Aptamers for Detection and Diagnostics (ADD): Can mobile systems linked to biosensors support molecular diagnostics of SARS-CoV-2? Should molecular medicine explore multiple alternatives as adjuvants to or replacement for traditional and non-traditional vaccines?

10.33774/chemrxiv-2021-l393t-v36 ◽

2021 ◽

Author(s):

Shoumen Datta

Keyword(s):

Data Sharing ◽

Molecular Diagnostics ◽

Low Cost ◽

Mobile App ◽

Mobile Systems ◽

Molecular Medicine ◽

Invasive Monitoring ◽

Non Invasive ◽

Secure Information ◽

Biomedical Device

Proposed SARS-CoV-2 surveillance tool using a mobile app for non-invasive monitoring of humans and animals. <p>Engineering a biomedical device as a low-cost, non-invasive, detection, and diagnostic platform for surveillance of infections in humans, and animals. The system embraces the IoT <i>“digital by design”</i> metaphor by incorporating elements of connectivity, data sharing and (secure) information arbitrage. Using an array of aptamers to bind viral targets may help in detection, diagnostics, and potentially prevention in case of SARS-CoV-2. The ADD tool may become part of a broader platform approach.</p>

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Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App

10.20944/preprints202009.0574.v1 ◽

2020 ◽

Author(s):

Juan Carlos Laso Bayas ◽

Linda See ◽

Hedwig Bartl ◽

Tobias Sturn ◽

Mathias Karner ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Change Detection ◽

Survey Data ◽

Target Location ◽

Low Cost ◽

Mobile App ◽

Sample Survey ◽

In Situ Data

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in-situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in-situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens, and the photographs submitted through the app were not always of sufficient quality. For this reason, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree and a near real-time quality assurance system to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was the implementation of a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to present these new features and to compare the results obtained by the citizens with authoritative LUCAS data from 2018 in terms of LULC and change in LC. We also compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. Finally, we present the results from a user survey to discuss challenges encountered during the campaign and what further improvements could be made in the future, including better in-app navigation and offline maps, making FotoQuest a model for enabling the collection of large amounts of land cover data at a low cost.

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