Smart Medical Kit in Chronic Kidney Disease Management

Internet of things (IoT)-enabled devices perform remote monitoring of patients and keep them healthy. They also facilitate physicians to provide high-quality care to their patients with accurate data. Chronic disease involves a wide range of health issues like diabetics, asthma, heart disease, kidney disease, and other disorders. To avoid disease progression, the IoT-based smart medical kit helps in episodic patient monitoring, continuous patient monitoring in acute conditions, and patient alarm monitoring. The chapter focuses on the deployment of interconnected devices (sensors, actuators, monitors, detectors, and camera systems) to collect data from heterogeneous systems. The output is connected to a think speak dashboard for monitoring the variation over the period. The smart kit provides more accurate and reliable recommendations to assist patients in controlling their chronic disease and assists in remote monitoring of a patient's health conditions.

Image Assisted Total Stations for Structural Health Monitoring—A Review

Measuring structures and its documentation is one of the tasks of engineering geodesy. Structural health monitoring (SHM) is defined as a periodic or continuous method to provide information about the condition of the construction through the determination of measurement data and their analysis. In SHM, wide varieties of sensors are used for data acquisition. In the following, the focus is on the application of image assisted total stations (IATS). The combination of tacheometry and photogrammetric measurement offers high flexibility and precision. Different approaches of automated detecting and matching whose applications have been tested in practice are briefly explained. A distinction is made between built-in cameras (commercial) and external camera systems (prototypes). Various successful applications of IATS in the field of SHM are presented and explained.

Political Effects of Digital Transformation of Urban Governance (On the Example of Moscow)

The paper is devoted to the study of particular political effects of digitalization of urban governance in the Russian Federation. Based on the concept of «surveillance capitalism» and research on the digital transformation of public administration, the authors analyzes the structure and logic of functioning of the «smart city» model using the example of Moscow. Based on the material of street protests, the political effects of the use of digital infrastructure by the city authorities, in particular, camera systems with face recognition technologies, are examined. The study of the Russian situation correlates with the latest decisions of the United Nations (UN) Human Rights Council and the European Union’s initiatives to control remote biometric recognition technologies.

Perceptions About Nursing Practice Based on the Internet of Things: A Survey of Nursing Managers

The purpose of this study was to investigate the perceptions of nursing managers about adopting nursing practices based on the Internet of Things and to examine related ethical issues. Questionnaires were sent to 538 nursing managers in Japan, with 131 responses. Of these, 87% and 33% agreed that a system using radio frequency identifiers would be useful for locating patients and nurses, respectively, 58%–81% recognized the value for patient safety of various camera systems for nursing observation, such as cameras linked to biometric alarms, 73% agreed the usefulness of automatically prioritizing alarms, but only around 39% were in favor of using facial recognition to help nursing observation. Many nursing managers expressed concerns about privacy. Data storage for at least 6 months was supported by 53% for location data and 41% for ceiling camera videos. Thus, nursing practice based on the Internet of Things is widely accepted in Japan.

Design and Validation of a Camera-Based Safety System for Fenceless Robotic Work Cells

A two-dimensional (2-D) camera system with a real-time image processing-based safety technology is a cost-effective alternative that needs optimization of the cell layout, the number of cameras, and the camera’s locations and orientations. A design optimization study was performed using the multi-criteria linear fractional programming method and considering the number of cameras, the resolution, as well as camera positions and orientations. A table-top experimental setup was designed and built to test the effectiveness of the optimized design using two cameras. The designs at optimal and nonoptimal parameters were compared using a deep learning algorithm, ResNet-152. To eliminate blind spots, a simple but novel 2-D image merging technique was proposed as an alternative to commonly employed stereo imaging methods. Verification experiments were conducted by using two camera resolutions with two graphic processors under varying illuminance. It was validated that high-speed entrances to the safety system were detected reliably and with a 0.1 s response time. Moreover, the system was proven to work effectively at a minimum illuminance of 120 lux, while commercial systems cannot be operated under 400 lux. After determining the most appropriate 2-D camera type, positions, and angles within the international standards, the most cost-effective solution set with a performance-to-price ratio up to 15 times higher than high-cost 3-D camera systems was proposed and validated.

Application of Wireless Sensor Networks to Prevent the Spread of Forest Fires

The objective of this article is to identify current trends and prospects for the use of technical facilities and installations to prevent the spread of wildfires by analyzing the literature. The analysis of the literature has allowed an analysis of different ground-based wildfire detection and monitoring systems: optical sensors and digital camera systems, and wireless sensor network systems. The author concludes that the wireless sensor network can be seen as a partial solution when used in combination with other technologies. Keywords—observation towers, optical systems, optical sensors, digital cameras, wireless sensor network.

Kinematic Characteristics of National and College Level Weightlifters during the Snatch Technique Using Wearable Inertial Sensors

Weightlifting performance is strongly dependent on technique, explosive strength, and flexibility. There are two major lifts involved in competition: the snatch and the clean and jerk, and the snatch is the most technical component of the weightlifting competition. Most technical analyses have previously been performed using either video analysis or conventional optical camera systems. However, few studies have investigated the kinematic characteristics of the weightlifters using inertial measurement unit (IMU) sensors. In this study, we investigated the joint kinematics of the trunk, shoulder, elbow, hip, and knee as well as the main phases during the snatch technique for national and college level weightlifters using multiple IMU sensors. Seven female Mongolian weightlifters (three national level and four college level) participated. Each participant performed three snatch attempts at 70% of their one-repetition maximum. The joint angles were calculated using three-axis acceleration and three-axis gyroscope data from the IMU sensors. The six main phases of the snatch technique were defined based on knee flexion. All parameters were compared between the national and college level weightlifters. The national team showed a higher elbow range of motion and a greater extension of the hip and knee joints at the second pull compared with college-level athletes. In addition, the college team did not exhibit the transition phase, and the proportion of the turnover phase was larger. This study provides a kinematic difference between the two different level weightlifters, which may help coaches and athletes to improve their training strategy and weightlifting performance.

Results of comparing astronomical-geodetic and navigational-geodetic methods of determining the components of the deflection of vertical

The authors present the results of comparing the components of deflection of vertical obtained through astronomical-geodetic and navigational-geodetic methods. The first one is based on comparing astronomical and geodetic coordinates of a location. This method has recently been widely implemented in a digital zenith camera systems using a small-sized digital telescope with an astronomical camera based on CCD or CMOS technologies, a high-precision inclinometer and satellite navigation system receiver. In this case, the combination of a telescope, an astronomical camera and an inclinometer enables determining the local direction of the plumb line, expressed by astronomical coordinates, from observations of stars at the zenith and using high-precision star catalogs. The navigational-geodetic method is based on comparing the results of the normal heights’ increments, defined through geometric leveling, and geodetic heights, computed with the relative method of satellite coordinate determinations. For each method, random and systematic components of the error and its confidence bounds were calculated; the absolute values of the deflection of vertical components at two geographically separated points were compared.

Improved Accuracy for Automated Counting of a Fish in Baited Underwater Videos for Stock Assessment

The ongoing need to sustainably manage fishery resources can benefit from fishery-independent monitoring of fish stocks. Camera systems, particularly baited remote underwater video system (BRUVS), are a widely used and repeatable method for monitoring relative abundance, required for building stock assessment models. The potential for BRUVS-based monitoring is restricted, however, by the substantial costs of manual data extraction from videos. Computer vision, in particular deep learning (DL) models, are increasingly being used to automatically detect and count fish at low abundances in videos. One of the advantages of BRUVS is that bait attractants help to reliably detect species in relatively short deployments (e.g., 1 h). The high abundances of fish attracted to BRUVS, however, make computer vision more difficult, because fish often obscure other fish. We build upon existing DL methods for identifying and counting a target fisheries species across a wide range of fish abundances. Using BRUVS imagery targeting a recovering fishery species, Australasian snapper (Chrysophrys auratus), we tested combinations of three further mathematical steps likely to generate accurate, efficient automation: (1) varying confidence thresholds (CTs), (2) on/off use of sequential non-maximum suppression (Seq-NMS), and (3) statistical correction equations. Output from the DL model was more accurate at low abundances of snapper than at higher abundances (>15 fish per frame) where the model over-predicted counts by as much as 50%. The procedure providing the most accurate counts across all fish abundances, with counts either correct or within 1–2 of manual counts (R2 = 88%), used Seq-NMS, a 45% CT, and a cubic polynomial corrective equation. The optimised modelling provides an automated procedure offering an effective and efficient method for accurately identifying and counting snapper in the BRUV footage on which it was tested. Additional evaluation will be required to test and refine the procedure so that automated counts of snapper are accurate in the survey region over time, and to determine the applicability to other regions within the distributional range of this species. For monitoring stocks of fishery species more generally, the specific equations will differ but the procedure demonstrated here could help to increase the usefulness of BRUVS.