Applications of Computer Vision Technologies of Automated Crack Detection and Quantification for the Inspection of Civil Infrastructure Systems

Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation\u27s resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure. Conventional human visual inspection is still considered as the primary inspection method. However, it is well established that human visual inspection is subjective and often inaccurate. In order to improve current manual visual inspection for crack detection and evaluation of civil infrastructure, this study explores the application of computer vision techniques as a non-destructive evaluation and testing (NDE&T) method for automated crack detection and quantification for different civil infrastructures. In this study, computer vision-based algorithms were developed and evaluated to deal with different situations of field inspection that inspectors could face with in crack detection and quantification. The depth, the distance between camera and object, is a necessary extrinsic parameter that has to be measured to quantify crack size since other parameters, such as focal length, resolution, and camera sensor size are intrinsic, which are usually known by camera manufacturers. Thus, computer vision techniques were evaluated with different crack inspection applications with constant and variable depths. For the fixed-depth applications, computer vision techniques were applied to two field studies, including 1) automated crack detection and quantification for road pavement using the Laser Road Imaging System (LRIS), and 2) automated crack detection on bridge cables surfaces, using a cable inspection robot. For the various-depth applications, two field studies were conducted, including 3) automated crack recognition and width measurement of concrete bridges\u27 cracks using a high-magnification telescopic lens, and 4) automated crack quantification and depth estimation using wearable glasses with stereovision cameras. From the realistic field applications of computer vision techniques, a novel self-adaptive image-processing algorithm was developed using a series of morphological transformations to connect fragmented crack pixels in digital images. The crack-defragmentation algorithm was evaluated with road pavement images. The results showed that the accuracy of automated crack detection, associated with artificial neural network classifier, was significantly improved by reducing both false positive and false negative. Using up to six crack features, including area, length, orientation, texture, intensity, and wheel-path location, crack detection accuracy was evaluated to find the optimal sets of crack features. Lab and field test results of different inspection applications show that proposed compute vision-based crack detection and quantification algorithms can detect and quantify cracks from different structures\u27 surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application

Anti-collision systems in tunneling to improve effectiveness and safety in a system-quality approach: A review of the state of the art

Tunnelling and underground construction operations are often characterized by critical safety issues mainly due to poor visibility and blind spots around large vehicles and equipment. This can lead to collisions between vehicles or between vehicles and pedestrians or structural elements, causing accidents and fatalities. To improve the OS&H conditions, it is important to investigate the possible introduction of innovative techniques and technologies to reduce the occurrences and consequences of shared spaces (spaces used by both vehicles and pedestrians). For this reason, research was conducted to investigate the possible use of different technologies of anti-collision systems in tunnelling operations. First, to achieve this goal, an extensive review of the literature was carried out in accordance with the PRISMA statement to select the current techniques and technologies used by general anti-collision systems in civil and mining construction sites. Then, the operating principles, the relative advantages and disadvantages, combinations, and costs were examined for each of these. Eight types of systems and many examples of applications of anti-collision systems in underground environments were identified as a result of the analysis of the literature. Generally, it was noted that the anti-collision techniques available have found limited application in the excavation sites of underground civil works up to the present day, though the improvement in terms of safety and efficiency would be considerable

Incorporating Modular Arrangement of Predetermined Time Standard with a Wearable Sensing Glove

“Performance” – a common watchword in the present age, and that which is optimized through the most functional methodology of investigating the work procedure. This encompassed the auditing, updating of the tasks, while at the same time, applied automation and mechanization. The Modular Arrangement of Predetermined Time Standard (MODAPTS) is a useful application of a work measurement technique that allow a greater variety of work for manufacturing, engineering, and administrative service activities to be measured quickly with ease and accuracy. The MODAPTS, however, made it extremely difficult for engineers to use because it required an ample amount of time to analyze and code the raw data. A new design was proposed to help resolve the conventional system\u27s inadequacy because in MODAPTS, each task cycle of a minute required about 2 hours to calculate and document, and also, the judgment of the analysts varied for the same task. This study aimed to reduce the time taken for the traditional MODAPTS documentation usually took and produced unified results by integrating MODAPTS with a Sensing Wearable Glove while maintaining the same performance. The objective was to introduce an easy, cost-effective solution, and to compare the accuracy of coding between manual and automated calculated MODAPTS while maintaining consistent performance. This study discusses the glove and accompanying software design that detected movements using flex sensors, gyroscopes, microcontrollers, and pressure sensors. These movements were translated into analog data used to create MODAPTS codes as an output, which then sent the data wirelessly using the Bluetooth module. The device designed in this study is capable of sensing gestures for various operations, and the traditional method was compared to the proposed method. This was in turn, validated using the two-way ANOVA analysis. It was observed that the sensor-based glove provided efficient and reliable results, just like the traditional method results while maintaining the same performance

Automatic Posture Correction Utilizing Electrical Muscle Stimulation

Habitually poor posture can lead to repetitive strain injuries that lower an individual\u27s quality of life and productivity. Slouching over computer screens and smart phones, asymmetric weight distribution due to uneven leg loading, and improper loading posture are some of the common examples that lead to postural problems and health ramifications. To help cultivate good postural habits, researchers have proposed slouching, balance, and improper loading posture detection systems that alert users through traditional visual, auditory or vibro-tactile feedbacks when posture requires attention. However, such notifications are disruptive and can be easily ignored. We address these issues with a new physiological feedback system that uses sensors to detect these poor postures, and electrical muscle stimulation to automatically correct the poor posture. We compare our automatic approach against other alternative feedback systems and through different unique contexts. We find that our approach outperformed alternative traditional feedback systems by being faster and more accurate while delivering an equally comfortable user experience

Wearable Technology Supported Home Rehabilitation Services in Rural Areas:– Emphasis on Monitoring Structures and Activities of Functional Capacity Handbook

The sustainability of modern healthcare systems is under threat. – the ageing of the population, the prevalence of chronic disease and a need to focus on wellness and preventative health management, in parallel with the treatment of disease, pose significant social and economic challenges. The current economic situation has made these issues more acute. Across Europe, healthcare expenditure is expected to rice to almost 16% of GDP by 2020. (OECD Health Statistics 2018). Coupled with a shortage of qualified personnel, European nations are facing increasing challenges in their ability to provide better-integrated and sustainable health and social services. The focus is currently shifting from treatment in a care center to prevention and health promotion outside the care institute. Improvements in technology offers one solution to innovate health care and meet demand at a low cost. New technology has the potential to decrease the need for hospitals and health stations (Lankila et al., 2016. In the future the use of new technologies – including health technologies, sensor technologies, digital media, mobile technology etc. - and digital services will dramatically increase interaction between healthcare personnel and customers (Deloitte Center for Health Solutions, 2015a; Deloitte Center for Health Solutions 2015b). Introduction of technology is expected to drive a change in healthcare delivery models and the relationship between patients and healthcare providers. Applications of wearable sensors are the most promising technology to aid health and social care providers deliver safe, more efficient and cost-effective care as well as improving people’s ability to self-manage their health and wellbeing, alert healthcare professionals to changes in their condition and support adherence to prescribed interventions. (Tedesco et al., 2017; Majumder et al., 2017). While it is true that wearable technology can change how healthcare is monitored and delivered, it is necessary to consider a few things when working towards the successful implementation of this new shift in health care. It raises challenges for the healthcare systems in how to implement these new technologies, and how the growing amount of information in clinical practice, integrates into the clinical workflows of healthcare providers. Future challenges for healthcare include how to use the developing technology in a way that will bring added value to healthcare professionals, healthcare organizations and patients without increasing the workload and cost of the healthcare services. For wearable technology developers, the challenge will be to develop solutions that can be easily integrated and used by healthcare professionals considering the existing constraints. This handbook summarizes key findings from clinical and laboratory-controlled demonstrator trials regarding wearables to assist rehabilitation professionals, who are planning the use of wearable sensors in rehabilitation processes. The handbook can also be used by those developing wearable sensor systems for clinical work and especially for use in hometype environments with specific emphasis on elderly patients, who are our major health care consumers

Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving

Excessive alcohol consumption causes disability and death. Digital interventions are promising means to promote behavioral change and thus prevent alcohol-related harm, especially in critical moments such as driving. This requires real-time information on a person's blood alcohol concentration (BAC). Here, we develop an in-vehicle machine learning system to predict critical BAC levels. Our system leverages driver monitoring cameras mandated in numerous countries worldwide. We evaluate our system with n=30 participants in an interventional simulator study. Our system reliably detects driving under any alcohol influence (area under the receiver operating characteristic curve [AUROC] 0.88) and driving above the WHO recommended limit of 0.05g/dL BAC (AUROC 0.79). Model inspection reveals reliance on pathophysiological effects associated with alcohol consumption. To our knowledge, we are the first to rigorously evaluate the use of driver monitoring cameras for detecting drunk driving. Our results highlight the potential of driver monitoring cameras and enable next-generation drunk driver interaction preventing alcohol-related harm

Mobile gaze tracking system for outdoor walking behavioral studies

Most gaze tracking techniques estimate gaze points on screens, on scene images, or in confined spaces. Tracking of gaze in open-world coordinates, especially in walking situations, has rarely been addressed. We use a headmounted eye tracker combined with two inertial measurement units (IMU) to track gaze orientation relative to the heading direction in outdoor walking. Head movements relative to the body are measured by the difference in output between the IMUs on the head and body trunk. The use of the IMU pair reduces the impact of environmental interference on each sensor. The system was tested in busy urban areas and allowed drift compensation for long (up to 18 min) gaze recording. Comparison with ground truth revealed an average error of 3.38 while walking straight segments. The range of gaze scanning in walking is frequently larger than the estimation error by about one order of magnitude. Our proposed method was also tested with real cases of natural walking and it was found to be suitable for the evaluation of gaze behaviors in outdoor environments