Mcfarlandbeck6317

Advancements in motion sensing technology can potentially allow clinicians to make more accurate range-of-motion (ROM) measurements and informed decisions regarding patient management. The aim of this study was to systematically review and appraise the literature on the reliability of the Kinect, inertial sensors, smartphone applications and digital inclinometers/goniometers to measure shoulder ROM. Eleven databases were screened (MEDLINE, EMBASE, EMCARE, CINAHL, SPORTSDiscus, Compendex, IEEE Xplore, Web of Science, Proquest Science and Technology, Scopus, and PubMed). The methodological quality of the studies was assessed using the consensus-based standards for the selection of health Measurement Instruments (COSMIN) checklist. Reliability assessment used intra-class correlation coefficients (ICCs) and the criteria from Swinkels et al. (2005). Thirty-two studies were included. A total of 24 studies scored "adequate" and 2 scored "very good" for the reliability standards. Only one study scored "very good" and just over half of the studies (18/32) scored "adequate" for the measurement error standards. Good intra-rater reliability (ICC > 0.85) and inter-rater reliability (ICC > 0.80) was demonstrated with the Kinect, smartphone applications and digital inclinometers. Overall, the Kinect and ambulatory sensor-based human motion tracking devices demonstrate moderate-good levels of intra- and inter-rater reliability to measure shoulder ROM. Future reliability studies should focus on improving study design with larger sample sizes and recommended time intervals between repeated measurements.An innovative wireless device for bioimpedance analysis was developed for post-dual-site free vascularized lymph node transfer (VLNT) evaluation. Seven patients received dual-site free VLNT for unilateral upper or lower limb lymphedema. A total of 10 healthy college students were enrolled in the healthy control group. The device was applied to the affected and unaffected limbs to assess segmental alterations in bioimpedance. The affected proximal limb showed a significant increase in bioimpedance at postoperative sixth month (3.3 [2.8, 3.6], p = 0.001) with 10 kHz currents for better penetration, although the difference was not significant (3.3 [3.3, 3.8]) at 1 kHz. The bioimpedance of the affected distal limb significantly increased after dual-site free VLNT surgery, whether passing with the 1 kHz (1.6 [0.7, 3.4], p = 0.030, postoperative first month; 2.8 [1.0, 4.2], p = 0.027, postoperative third month; and 1.3 [1.3, 3.4], p = 0.009, postoperative sixth month) or 10 kHz current ((1.4 [0.5, 2.7], p = 0.049, postoperative first month; 3.2 [0.9, 6.3], p = 0.003, postoperative third month; and 3.6 [2.5, 4.1], p less then 0.001, postoperative sixth month). Bioimpedance alterations on the affected distal limb were significantly correlated with follow-up time (rho = 0.456, p = 0.029 detected at 10 kHz). This bioimpedance wireless device could quantitatively monitor the interstitial fluid alterations, which is suitable for postoperative real-time surveillance.This paper reviews 74 empirical publications that used high-frequency data collection tools to capture facets of small collaborative groups-i.e., papers that conduct Multimodal Collaboration Analytics (MMCA) research. We selected papers published from 2010 to 2020 and extracted their key contributions. For the scope of this paper, we focus on (1) the sensor-based metrics computed from multimodal data sources (e.g., speech, gaze, face, body, physiological, log data); (2) outcome measures, or operationalizations of collaborative constructs (e.g., group performance, conditions for effective collaboration); (3) the connections found by researchers between sensor-based metrics and outcomes; and (4) how theory was used to inform these connections. An added contribution is an interactive online visualization where researchers can explore collaborative sensor-based metrics, collaborative constructs, and how the two are connected. Based on our review, we highlight gaps in the literature and discuss opportunities for the field of MMCA, concluding with future work for this project.One of the axioms of structural health monitoring states that the severity of damage assessment can only be done in a learning mode under the supervision of an expert. Therefore, a numerical analysis was conducted to gain knowledge regarding the influence of the damage size on the propagation of elastic waves in a honeycomb sandwich composite panel. Core-skin debonding was considered as damage. For this purpose, a panel was modelled taking into account the real geometry of the honeycomb core using the time-domain spectral element method and two-dimensional elements. The presented model was compared with the homogenized model of the honeycomb core and validated in the experimental investigation. The result of the parametric study is a function of the influence of damage on the amplitude and energy of propagating waves.Creating and maintaining the microclimate in livestock buildings is associated with numerous engineering and technical challenges. Together with adequate feeding, the microclimate determines the health, reproductive ability, and production potential of the animals (obtaining a maximum amount of high-quality products). One of the deciding steps in improving the parameters of microclimate, i.e., temperature and humidity in agricultural facilities, particularly in livestock buildings, is to develop reliable and highly efficient air curtains in the vestibules. The objective of the manuscript is to investigate the parameters of the microclimate in livestock buildings using the air curtain, supported by automation and ICT technologies for rational operating modes. The presented theoretical and experimental studies on improving the microclimate parameters in livestock buildings were carried out using an innovative air curtain system. Its power is calculated based on the dimensions of the room, and the flow rate of warm air near the floor level is three times lower than at the installation site. The use of air curtains reduces consumption of thermal energy needed to maintain an optimal microclimate for livestock by 10-15%. Furthermore, the use of an automated digital control system maintains an optimal microclimate in the building. The developed energy-saving system for creating an optimal micro-climate in livestock buildings using air curtains was tested in a pigsty of the Research and Training Farm "Vorzel" of the National University of Life and Environmental Sciences of Ukraine, located in the Kiev region. The developed automated microclimate system using air curtains significantly improves the microclimate parameters and significantly reduces power consumption. The system can be further developed by adding remote control based on the Internet of Things (IoT) technology.Nowadays, faces in videos can be easily replaced with the development of deep learning, and these manipulated videos are realistic and cannot be distinguished by human eyes. Some people maliciously use the technology to attack others, especially celebrities and politicians, causing destructive social impacts. Therefore, it is imperative to design an accurate method for detecting face manipulation. However, most of the existing methods adopt single convolutional neural network as the feature extraction module, causing the extracted features to be inconsistent with the human visual mechanism. Moreover, the rich details and semantic information cannot be reflected with single feature, limiting the detection performance. Therefore, this paper tackles the above problems by proposing a novel face manipulation detection method based on a supervised multi-feature fusion attention network (SMFAN). Specifically, the capsule network is used for face manipulation detection, and the SMFAN is added to the original capsule network to extract details of the fake face image. Further, the focal loss is used to realize hard example mining. Finally, the experimental results on the public dataset FaceForensics++ show that the proposed method has better performance.The recent development in the area of IoT technologies is likely to be implemented extensively in the next decade. There is a great increase in the crime rate, and the handling officers are responsible for dealing with a broad range of cyber and Internet issues during investigation. IoT technologies are helpful in the identification of suspects, and few technologies are available that use IoT and deep learning together for face sketch synthesis. Convolutional neural networks (CNNs) and other constructs of deep learning have become major tools in recent approaches. A new-found architecture of the neural network is anticipated in this work. It is called Spiral-Net, which is a modified version of U-Net fto perform face sketch synthesis (the phase is known as the compiler network C here). Spiral-Net performs in combination with a pre-trained Vgg-19 network called the feature extractor F. It first identifies the top n matches from viewed sketches to a given photo. F is again used to formulate a feature map based on the cosine distance of a candidate sketch formed by C from the top n matches. A customized CNN configuration (called the discriminator D) then computes loss functions based on differences between the candidate sketch and the feature. Values of these loss functions alternately update C and F. The ensemble of these nets is trained and tested on selected datasets, including CUFS, CUFSF, and a part of the IIT photo-sketch dataset. Results of this modified U-Net are acquired by the legacy NLDA (1998) scheme of face recognition and its newer version, OpenBR (2013), which demonstrate an improvement of 5% compared with the current state of the art in its relevant domain.Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information.Water temperature, pH, dissolved oxygen (DO), electrical conductivity (EC), and salinity levels are the critical cultivation factors for freshwater aquaculture. This paper proposes a novel wireless multi-sensor system by integrating the temperature, pH, DO, and EC sensors with an ESP 32 Wi-Fi module for monitoring the water quality of freshwater aquaculture, which acquires the sensing data and salinity information directly derived from the EC level. The information of water temperature, pH, DO, EC, and salinity levels was displayed in the ThingSpeak IoT platform and was visualized in a user-friendly manner by ThingView APP. Firstly, these sensors were integrated with an ESP32 Wi-Fi platform. The observations of sensors and the estimated salinity from the EC level were then transmitted by a Wi-Fi network to an on-site Wi-Fi access point (AP). The acquired information was further transmitted to the ThingSpeak IoT and displayed in the form of a web-based monitoring system which can be directly visualized by online browsing or the ThingView APP.