From crowd to cloud: simplified automatic reconstruction of digital building assets for facility management

Purpose The purpose of this paper is to investigate the feasibility of an end-to-end simplified and automated reconstruction pipeline for digital building assets using the design science research approach. Current methods to create digital assets by capturing the state of existing buildings can provide high accuracy but are time-consuming, expensive and difficult. Design/methodology/approach Using design science research, this research identifies the need for a crowdsourced and cloud-based approach to reconstruct digital building assets. The research then develops and tests a fully functional smartphone application prototype. The proposed end-to-end smartphone workflow begins with data capture and ends with user applications. Findings The resulting implementation can achieve a realistic three-dimensional (3D) model characterized by different typologies, minimal trade-off in accuracy and low processing costs. By crowdsourcing the images, the proposed approach can reduce costs for asset reconstruction by an estimated 93% compared to manual modeling and 80% compared to locally processed reconstruction algorithms. Practical implications The resulting implementation achieves “good enough” reconstruction of as-is 3D models with minimal tradeoffs in accuracy compared to automated approaches and 15× cost savings compared to a manual approach. Potential facility management use cases include the issue and information tracking, 3D mark-up and multi-model configurators. Originality/value Through user engagement, development, testing and validation, this work demonstrates the feasibility and impact of a novel crowdsourced and cloud-based approach for the reconstruction of digital building assets.

SMARTPHONE APPLICATION FOR MANAGING MISSED AND FOUND BELONGINGS

Nowadays, due to our everyday stress and current stressful lifestyle, the loss of items appears a frequent issue and may be very inconvenient. In this regard, until the IoT becomes part of everyday life, we can use the software as an efficient tool to assist a person's searching, verifying, and finding lost belongings. This paper presents an Android-based application that we proposed and implemented to help users find lost items. Utilizing this software will enable the subscriber to record his request to the relevant authority. In addition, a special section offers to insert a contact telephone number or email to communicate between the person who found the item and the person who lost it. During testing, among other services, the platform showed its capabilities to register and log users, releasing a lot of information of lost items and automatically forwarding lost-and-found notifications. The paper can be useful for those who deal with the application of information technology.

Deep learning-based system development for black pine bast scale detection

The prevention of the loss of agricultural resources caused by pests is an important issue. Advances are being made in technologies, but current farm management methods and equipment have not yet met the level required for precise pest control, and most rely on manual management by professional workers. Hence, a pest detection system based on deep learning was developed for the automatic pest density measurement. In the proposed system, an image capture device for pheromone traps was developed to solve nonuniform shooting distance and the reflection of the outer vinyl of the trap while capturing the images. Since the black pine bast scale pest is small, pheromone traps are captured as several subimages and they are used for training the deep learning model. Finally, they are integrated by an image stitching algorithm to form an entire trap image. These processes are managed with the developed smartphone application. The deep learning model detects the pests in the image. The experimental results indicate that the model achieves an F1 score of 0.90 and mAP of 94.7% and suggest that a deep learning model based on object detection can be used for quick and automatic detection of pests attracted to pheromone traps.

A Smartphone Application as an Exploratory Endpoint in a Phase 3 Parkinson’s Disease Clinical Trial: A Pilot Study

<b><i>Background:</i></b> Smartphones can generate objective measures of Parkinson’s disease (PD) and supplement traditional in-person rating scales. However, smartphone use in clinical trials has been limited. <b><i>Objective:</i></b> This study aimed to determine the feasibility of introducing a smartphone research application into a PD clinical trial and to evaluate the resulting measures. <b><i>Methods:</i></b> A smartphone application was introduced part-way into a phase 3 randomized clinical trial of inosine. The application included finger tapping, gait, and cognition tests, and participants were asked to complete an assessment battery at home and in clinic alongside the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). <b><i>Results:</i></b> Of 236 eligible participants in the parent study, 88 (37%) consented to participate, and 59 (27 randomized to inosine and 32 to placebo) completed a baseline smartphone assessment. These 59 participants collectively completed 1,292 batteries of assessments. The proportion of participants who completed at least one smartphone assessment was 61% at 3, 54% at 6, and 35% at 12 months. Finger tapping speed correlated weakly with the part III motor portion (<i>r</i> = −0.16, left hand; <i>r</i> = −0.04, right hand) and total (<i>r</i> = −0.14) MDS-UPDRS. Gait speed correlated better with the same measures (<i>r</i> = −0.25, part III motor; <i>r</i> = −0.34, total). Over 6 months, finger tapping speed, gait speed, and memory scores did not differ between those randomized to active drug or placebo. <b><i>Conclusions:</i></b> Introducing a smartphone application midway into a phase 3 clinical trial was challenging. Measures of bradykinesia and gait speed correlated modestly with traditional outcomes and were consistent with the study’s overall findings, which found no benefit of the active drug.

Smart facemask for wireless CO2 monitoring

The use of facemasks by the general population is recommended worldwide to prevent the spread of SARS-CoV-2. Despite the evidence in favour of facemasks to reduce community transmission, there is also agreement on the potential adverse effects of their prolonged usage, mainly caused by CO2 rebreathing. Herein we report the development of a sensing platform for gaseous CO2 real-time determination inside FFP2 facemasks. The system consists of an opto-chemical sensor combined with a flexible, battery-less, near-field-enabled tag with resolution and limit of detection of 103 and 140 ppm respectively, and sensor lifetime of 8 h, which is comparable with recommended FFP2 facemask usage times. We include a custom smartphone application for wireless powering, data processing, alert management, results displaying and sharing. Through performance tests during daily activity and exercise monitoring, we demonstrate its utility for non-invasive, wearable health assessment and its potential applicability for preclinical research and diagnostics.

Lessons from the design, development and implementation of a three-dimensional (3D) neonatal resuscitation training smartphone application: Life-saving Instruction for Emergencies (LIFE app)

Neonatal mortality remains disproportionately high in sub-Saharan Africa partly due to insufficient numbers of adequately trained and skilled front-line health workers. Opportunities for improving neonatal care may result from upskilling frontline health workers using innovative technological approaches. This practice paper describes the key steps involved in the design, development and implementation of an innovative smartphone-based training application using an agile, human-centred design approach. The Life-saving Instruction for Emergencies (LIFE) app is a three-dimension (3D) scenario-based mobile app for smartphones and is free to download. Two clinical modules are currently included with further scenarios planned. Whilst the focus of the practice paper is on the lessons learned during the design and development process, we also share key learning related to project management and sustainability plans, which we hope will help researchers working on similar projects.

A two-step computer vision-based framework for bolt loosening detection and its implementation on a smartphone application

Bolt loosening detection is a labor-intensive and time-consuming process for field engineers. This paper develops a two-step computer vision-based framework to quickly identify bolt loosening angle from field images captured by unmanned aerial vehicle (UAV). In step one, a total of 1200 image samples of bolted structures were used to train faster region based convolutional neural network (Faster R-CNN) for bolt detection from UAV captured images. In step two, computer vision-based technologies, including Gaussian filter, perspective transform, and Hough transform (HT), were performed to quantify bolt loosening angle. The developed framework was then integrated into web server and an iOS application (app) was designed to enable fast data communication between field workplace (UAV captured images) and web server (bolt loosening angle quantification), so that field engineers can quickly view the inspection results on their phone screens. The proposed framework and designed smartphone app greatly help field engineers to improve the accuracy and efficiency for onsite inspection and maintenance of bolted structures.

Development of a New Smartphone Application to Increase Dietary Compliance in Patients with Chronic Kidney Disease

Aim: Nutritional therapy in chronic kidney disease (CKD) requires certain regulations in the diet of the patients. Patients’ self-management becomes possible with the development of mobile phones and their software. In the current study, a smartphone application that could be used to increase dietary compliance of CKD stage 4-5 and hemodialysis patients was developed. It is aimed that patients can control the dietary intake of energy, protein, sodium, potassium, phosphorus, and fluid by using the developed mobile application. Subjects and Method: The mobile application has been developed by the researchers until the final control and test phase. Later, the final control and test phase of the developed application were carried out by 5 expert dietitians, 5 specialist doctors, and 5 hemodialysis patients. Results: The majority of the participants stated that the application was easy to use, interesting, visually well designed, contains sufficient reliable information, and that they can recommend it to other patients. Participants who examined the application also offered suggestions about the application. Conclusion: The application was updated according to the evaluations and suggestions of the participants. The final application was formed to be ready for the use of the patients.

Development of IoT-Based Lighting Installation Practicum Module

With the advancement of technology today, there is known to be an innovation that is the internet of things where electronic devices can be monitored and controlled remotely. For this reason, the practicum module of internet of things-based lighting installation as a medium of learning for students. The workings of the tool will be made using the PZEM-004T sensor as a sensor reading the current voltage, power, and energy used and ESP-32 as an additional module on the Arduino Mega so that data reading voltage, current and power can be sent using the internet network to the smartphone. In the smartphone application, we can also control to extinguish or turn on the lights.