Nowadays, with the sustainable economic development in China, the management of the urban constructional planning in various cities in China has also made great progress and improvement, and urban ...infrastructure construction is also constantly being improved. To make people’s work and life more convenient, municipal projects are also increasing. Municipal water supply and drainage engineering construction in cities are indispensable in urban infrastructure construction. Long-distance drainage often occurs in the construction of water supply and drainage. In order to solve this situation, relevant staff have specially developed a series of long-distance pipe jacking technology and applied it to the construction of municipal water supply and drainage projects, which greatly improves the quality and the efficiency of municipal water supply and drainage projects.
Radar-based human activity recognition (HAR) finds various applications like assisted living and driver behavior monitoring. As radar data are heavily environment-dependent, it is becoming ...increasingly important to develop a transfer learning mechanism that enables a radar-based HAR system with desirable cross-environment adaptation feasibility. This paper concerns the issue of how radar-based HAR system can adapt to a new environment without source data. To this end, we devote to using the source hypothesis transfer learning architecture to build such an environment adaptation mechanism towards cross-environment radar-based HAR. In doing this, it is a challenging task to develop a reliable self-supervised labeling strategy for generating pseudo labels associated with the unlabeled target data, which is crucial to facilitate the learning of a target-specific feature extractor being responsible for environment adaptation. This paper presents the neighbor-aggregating-based labeling method and incorporates it with the existing clustering-based labeling method to perform the self-supervised labeling task. The logic behind our approach is that the above two labeling methods are complementary to each other in terms of making use of both local and global structures of adaptation data to supervise the labeling task. The coordination of both labeling methods is motivated to be implemented in the weighted combination form, which contributes to improving the reliability of generated labels. Experimental results on a public HAR dataset based on the frequency modulated continuous wave (FMCW) radar demonstrate the effectiveness of our approach.
Activity recognition is fundamental to many applications envisaged in pervasive computing, especially in smart environments where the resident's data collected from sensors will be mapped to human ...activities. Previous research usually focuses on scripted or pre-segmented sequences related to activities, whereas many real-world deployments require information about the ongoing activities in real time. In this paper, we propose an online activity recognition model on streaming sensor data that incorporates the spatio-temporal correlation-based dynamic segmentation method and the stigmergy-based emergent modeling method to recognize activities when new sensor events are recorded. The dynamic segmentation approach integrating sensor correlation and time correlation judges whether two consecutive sensor events belong to the same window or not, avoiding events from very different functional areas or with a long time interval in the same window, thus obtaining the segmented window for every single event. Then, the emergent paradigm with marker-based stigmergy is adopted to build activity features that are explicitly represented as a directed weighted network to define the context for the last sensor event in this window, which does not need sophisticated domain knowledge. We validate the proposed method utilizing the real-world dataset Aruba from the CASAS project and the results show the effectiveness.
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With the acceleration of aging process of population structure, the single resident lifestyle is increasing on account of the high cost of care services and the privacy invasion concern. It is ...essential to monitor the activities of solitary elderly to find the emergency and lifestyle deviation, as independent life cannot be maintained due to physical or mental problems. The unobtrusive systems are the most preferred choice for the real-life long-term monitoring, while the camera and wearable devices based systems are not suitable due to the privacy and uncomfortableness, respectively. We propose a novel sensor-based activity recognition model based on the two-layer multi-granularity framework and the emergent paradigm with marker-based stigmergy. The stigmergy based marking subsystem builds features by aggregating the context-aware information and generating the two-dimensional activity pheromone trail. The two-layer framework consists of coarse-grained and fine-grained classification subsystems. The coarse-grained subsystem identifies whether the input completed activity segmented by the traditional method is easily-confused, and utilizes our generalized segmentation method to increase the inter-cluster distance. The fine-grained subsystem employs machine learning or deep learning classifiers to realize the activity recognition task. The proposed model is a data-driven model based on the information self-organization. It does not need sophisticated domain knowledge, and can fully mine the hidden feature structure containing semantically related information and spatio-temporal characteristics. The experimental results demonstrate the effectiveness of the proposed method.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Non-invasive fetal electrocardiography (NI-FECG) plays an important role in fetal heart rate (FHR) measurement during the pregnancy. However, despite the large number of methods that have been ...proposed for adult ECG signal processing, the analysis of NI-FECG remains challenging and largely unexplored. In this study, we propose a prefix tree-based framework, called QRStree, for FHR measurement directly from the abdominal ECG (AECG). The procedure is composed of three stages: Firstly, a preprocessing stage is employed for noise elimination. Secondly, the proposed prefix tree-based method is used for fetal QRS complexes (FQRS) detection. Finally, a correction stage is applied for false positive and false negative correction. The novelty of the framework relies on using the range of FHR to establish the connections between the FQRS. The consecutive FQRS can be considered as strings composed of alphabet items, thus we can use the prefix tree to store them. A vertex of the tree contains an alphabet, thus a path of the tree gives a string. Such that, by storing the connections of the FQRS into the prefix tree structure, the problem of FQRS detection converts to a problem of optimal path selection. Specifically, after selecting the optimal path of the tree, the nodes in the optimal path are collected as detected FQRS. Since the prefix tree can cover every possible combination of the FQRS candidates, it has the potential to reduce the occurrence of miss detections. Results on two different databases show that the proposed method is effective in FHR measurement from single-channel AECG. The focus on single-channel FHR measurement facilitates the long-term monitoring for healthcare at home.
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Falls are a major health risk for the elderly, decreasing their ability to live independently. This article proposes a novel feature-specific floor pressure imaging system that uses a smart floor ...embedded with fiber sensors. The potential applications include indoor human-activity analysis for intelligent surveillance. In particular, the authors focus on the application of fall detection in a bathroom scenario. Their fall detection method consists of two steps: floor pressure imaging for target posture classification and the fall event decision based on the classified target postures. Fall detection experiments validated the proposed method. This article is part of a special issue on domestic pervasive computing.
To investigate the factors influencing hyperuricemia in children and adolescents and to provide a scientific basis for early prevention and treatment.
A retrospective study (2017–2021) of the ...prevalence of hyperuricemia in children and adolescents was conducted, and the factors influencing hyperuricemia were analyzed by multi-factor logistic regression.
The overall prevalence of hyperuricemia in children and adolescents aged 6–17 years in northeast Sichuan Province was 55.12% (8676/15,739), of which 60.68% (5699/9392) in boys and 46.90% (2977/6347) in girls; the prevalence of hyperuricemia from 2017 to 2021 was 52.40% ( 1540/2939), 52.56% (1642/3124), 52.11% (1825/3502), 58.33% (1691/2899), and 60.40% (1978/3275), respectively; the prevalence rates of 6–12 years old were 48.92% (864/1766), 50.46% (769/1524), and 52.73% (685/1299), 56.99% (693/1216), 35.46% (444/1252), 46.33% (524/1131), 60.50% (720/1190), and 66.82% (739/1106), 58.95% (652/1106), and 62.17% (761/1106) for 13–17 years old, respectively, 62.17% (761/1224), 63.19% (855/1353), and 61.70% (970/1572), respectively. Logistic regression showed that the prevalence of male (OR = 1.451, 95% CI 1.034 to 2.035, p = 0.031), age (OR = 1.074, 95% CI 1.024 to 1.126, p = 0.003), overweight/obesity (OR = 1.733, 95% CI 1.204∼2.494, p = 0.003), blood creatinine (OR = 1.018, 95% CI 1.005∼1.031, p = 0.007), triglycerides (OR = 1.450, 95% CI 1.065∼1.972, p = 0.018), blood calcium (OR = 6.792, 95% CI 1.373∼33.594, p = 0.019), and systolic blood pressure (OR = 1.037, 95% CI 1.018∼1.057, p < 0.001) were influential factors for the development of hyperuricemia.
The prevalence of hyperuricemia was higher in children and adolescents aged 6–17 years in northeastern Sichuan Province, with a higher prevalence in boys than in girls, and the prevalence increased with age.
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Infrared radiation changes (IRCs) induced by human activity can provide important information about activity patterns. This paper presents an IRC-based compressive classification method for ...recognizing the physical activities of interest in home-based assisted living. To fully capture the IRC compressively, a multi-view infrared motion sensing system is developed, which consists of three IRC sensing modules, that is, one module on the ceiling and two modules on opposite tripods facing each other. A pilot study is conducted in the context of classifying six typical physical activities with the incorporation of the classification techniques, including hidden Markov model and support vector machine, which demonstrates the effectiveness of our system.
Polymer degradation is a common problem in the extrusion process. In this work, Raman spectroscopy, a robust, rapid, and non-destructive tool for in-line monitoring, was utilized to in-line monitor ...the degradation of polypropylene (PP) under multiple extrusions. Raw spectra were pretreated by chemometrics methods to extract variations of spectra and eliminate noise. The variation of Raman intensity with the increasing number of extrusions was caused by the scission of PP chains and oxidative degradation, and the variation trend of Raman intensity indicated that long chains were more likely to be damaged by the extrusion. For the quantitative analysis of degradation, the partial least square was used to build a model to predict the degree of PP degradation measured by gel permeation chromatography (GPC). For the calibration set, the coefficient of determination (R2) and the root mean square error of cross-validation (RMSECV) were 0.9859 and 1.2676%, and for the prediction set, R2 and the root mean square error of prediction (RMSEP) were 0.9752 and 1.7228%, which demonstrated the accuracy of the proposed model. The in-line Raman spectroscopy combined with the chemometrics methods was proved to be an accurate and highly effective tool, which can monitor the degradation of polymer in real time.
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Device-Free Localization (DFL) based on the Radio Frequency (RF) is an emerging wireless sensing technology to perceive the position information of the target. To realize the real-time DFL with lower ...power, Back-projection Radio Tomographic Imaging (BRTI) has been used as a lightweight method to achieve the goal. However, the multipath noise in the RF sensing network may interfere with the measurement and the BRTI reconstruction performance. To resist the multipath interference in the observed data, it is necessary to recognize the informative RF link measurements that are truly affected by the target appearance. However, the existing methods based on the RF link state analysis are limited by the complex distribution of the RF link state and the high time complexity. In this paper, to enhance the performance of RF link state analysis, the RF link state analysis is transformed into a decomposition problem of the RF link state matrix, and an efficient RF link recognition method based on the low-rank and sparse decomposition is proposed to sense the spatiotemporal variation of the RF link state and accurately figure out the target-affected RF links. From the experimental results, the RF links recognized by the proposed method effectively reflect the target-induced RSS measurement variation with less time. Besides, the proposed method by recognizing the informative measurement is helpful to improve the accuracy of BRTI and enhance the efficiency in actual DFL applications.
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