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Deep learning is a promising method for image classification, including satellite images acquired by various sensors. However, the synergistic use of geospatial data for water body extraction from Sentinel-1 data using deep learning and the applicability of existing deep learning models have not been thoroughly tested for operational flood monitoring. Here, we present a novel water body extraction model based on a deep neural network that exploits Sentinel-1 data and flood-related geospatial datasets. For the model, the U-Net was customised and optimised to utilise Sentinel-1 data and other flood-related geospatial data, including digital elevation model (DEM), Slope, Aspect, Profile Curvature (PC), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), and Buffer for the Southeast Asia region. Testing and validation of the water body extraction model was applied to three Sentinel-1 images for Vietnam, Myanmar, and Bangladesh. By segmenting 384 Sentinel-1 images, model performance and segmentation accuracy for all of the 128 cases that the combination of stacked layers had determined were evaluated following the types of combined input layers. Of the 128 cases, 31 cases showed improvement in Overall Accuracy (OA), and 19 cases showed improvement in both averaged intersection over union (IOU) and F1 score for the three Sentinel-1 images segmented for water body extraction. The averaged OA, IOU, and F1 scores of the ‘Sentinel-1 VV’ band are 95.77, 80.35, and 88.85, respectively, whereas those of ‘band combination VV, Slope, PC, and TRI’ are 96.73, 85.42, and 92.08, showing improvement by exploiting geospatial data. Such improvement was further verified with water body extraction results for the Chindwin river basin, and quantitative analysis of ‘band combination VV, Slope, PC, and TRI’ showed an improvement of the F1 score by 7.68 percent compared to the segmentation output of the ‘Sentinel-1 VV’ band. Through this research, it was demonstrated that the accuracy of deep learning-based water body extraction from Sentinel-1 images can be improved up to 7.68 percent by employing geospatial data. To the best of our knowledge, this is the first work of research that demonstrates the synergistic use of geospatial data in deep learning-based water body extraction over wide areas. It is anticipated that the results of this research could be a valuable reference when deep neural networks are applied for satellite image segmentation for operational flood monitoring and when geospatial layers are employed to improve the accuracy of deep learning-based image segmentation.

This paper suggests a novel and reliable method to detect water body from Sentinel-1 SAR satellite data using deep learning technique. There have been a lot of studies to extract water body from SAR images with deep learning. Although they achieved good performance, most of them used training data without guaranteeing good quality. In this study, land cover map generated by an official government agency were used for labelling ground truth data. After identifying the acquisition date of aerial photo used for generating the land cover map, vector polygons for river or reservoir were extracted and used as label data. This new method reduced producing time and cost to generate reliable training data. After training our deep learning model, it showed 0.874 of f1score. We also tested our deep learning model to the heavy rain season in Korea (August 2020) and successfully detected river flooding.

Recently, interests in spreading the culture of sharing, especially at the social level, are increasing due to the income polarization, poverty, the rapid growth of civil society, and the increase of importance of public interest in private organizations. According to the Ministry of Government Administration and Home Affairs, Korea’s volunteer participation rate of 2015 has steadily increased to 114% compared to 2011, and according to the World Donation Index evaluation, the ranks in interest and participation in non-monetary sharing have increased to 75th in 2016 and 60th in 2018. Even though social interest is increasing, not many empirical studies on the contribution to talent donation have been made yet. In 2016, a survey was conducted on the change of donation attitude and recognition of donation. The major topics in the area for spreading the donation culture in general were the convenience of donation methods and the transparency of donor organizations. In order to solve the convenience of donation, the mobile and Web based platforms such as Wikipedia, Airbnb, Delivery nation, and Yogiyo, make interactions possible without constraints on place, conditions and the number of user to induce the public to participate in talent donation. In addition, block chain is the distributed computing-based technology of a data forgery and tamper proofing. One person cannot modify the data arbitrarily, but anyone can see the result of the data change. The reason that block chain technology is in the spotlight is because anyone can access the result of data changes. That is, the transparency of the transaction can be secured. In this thesis, we intend to apply these block chain’s characteristics to the talent donation platform.

Recruitment corruption makes people demand fair and objective hiring procedures. The development of IT technology has promoted the emergence of recruitment system based on AI which make interview conduct without human intervention. As companies can reduce costs and efforts through the AI recruitment system, more and more companies are using the system. As the result, the research for figuring out the user intention on the system is needed. We designed the research model to find factors affecting the intention to use of AI-based recruitment system utilizing constructs of TAM and TOE. Also the moderating effect by the number of employees in the corporation will be estimated.

Recently, interest in the spread of the culture of sharing has been increasing at a social level due to the deepening income polarization, poverty, and the rapid growth of civil society and the importance of public interest activities of private organizations. In 2016, the National Statistical Office conducted a survey on changes in donation attitudes and recognition of donations, items that took up a large part in the area for the spread of the overall donation culture were the convenience of donation methods and the enhancement of transparency of donation organizations. The existing donation system is a way for donors to donate directly to several donor organizations. However, this method has its disadvantage: it is difficult for donors to see how their donations are spent. To address the convenience of such donation methods, based on the platform that is the core of the fourth industrial era, it enables unrestricted interaction in the network environment such as mobile or PC, inducing open participation in talent donation to the general public. Blockchain is a data forgery/modulation prevention technology based on distributed computing technology. Because the block chain records continuously changing data on all participating nodes, it is impossible to arbitrarily manipulate the data by the operators of distributed nodes. Blockchain technology is in the spotlight because it can secure transparency in transactions. In this paper, to solve these problems, we try to introduce the block chain technology into the donation system.

Assessment of bridge deflection and serviceability is essential for structural condition and infrastructure maintenance. In this study, we aim to estimate the high-resolution displacements of bridges using X-band Cosmo-SkyMed and C-band Sentinel-1 A/B SAR data. We applied multi-temporal Interferometric analysis to Geobuksan bridge and Dolsan Bridge in the Yeosu region. In addition to the bridge displacements, the geolocation error of scatterers were corrected by estimating DEM error.

Ship detection in coastal regions accompanied by machine learning could be effective in economic and martial issues. However, conventional researches on ship detection mainly focused on modifying the training model itself instead of obtaining qualified training data. In order to ameliorate the training data in the aspect of quality and quantity, this research aims to directly obtain training data from AIS information. From discrete AIS information, interpolation on SAR acquisition time was conducted, followed by adjusting the Doppler frequency shift caused by each vessel's velocity. Training data was constructed from the adjusted position using internal location of AIS sensor on each type of vessel. Extracted training data by the proposed algorithm from Sentinel-1 images was tested with CNN model. As the detection performance of the extracted training data exceeded that from visual interpretation, this study concluded that qualified training data could be extracted from the proposed algorithm.

Development of convolutional neural network (CNN) optimized for object detection, led to significant developments in ship detection. Although training data critically affect the performance of the CNN-based training model, previous studies focused mostly on enhancing the architecture of the training model. This study developed a sophisticated and automatic methodology to generate verified and robust training data by employing synthetic aperture radar (SAR) images and automatic identification system (AIS) data. The extraction of training data initiated from interpolating the discretely received AIS positions to the exact position of the ship at the time of image acquisition. The interpolation was conducted by applying a Kalman filter, followed by compensating the Doppler frequency shift. The bounding box for the ship was constructed tightly considering the installation of the AIS equipment and the exact size of the ship. From 18 Sentinel-1 SAR images using a completely automated procedure, 7489 training data were obtained, compared with a different set of training data from visual interpretation. The ship detection model trained using the automatic training data obtained 0.7713 of overall detection performance from 3 Sentinel-1 SAR images, which exceeded that of manual training data, evading the artificial structures of harbors and azimuth ambiguity ghost signals from detection.

Development of Big data, artificial intelligence and 5G networks, and advances have changed the marketing environment with the arrival of a hyper-connected society where everything is connected. And Kotler has described this change by naming it Market 4.0. In this environment, connectivity and communication between businesses and consumers have been emphasized, and constant communication is emphasized to match a brand identity and brand image in order to build a strong brand of business. Thus, from the point of view of communication, the brand identity prism model by Kapferer, which is an analytical framework of brand identity and brand image, has been framed. This paper used semantic network analysis to measure brand identity and brand image and analyzed difference in order to study semantic network analysis as strategic tool for building strong brand of enterprise. The brand identity was extracted from the web page managed by Samsung Electronics while the brand image was extracted through SNS and Samsung Members Page. The extracted data were interpreted by means of centrality after data preprocessing. This study found that Samsung Electronics’ brand identity and brand image matched that of “A global innovative technology company” And efficiency was demonstrated by comparing with prior studies studied around case analysis.

Submarine groundwater discharge (SGD) is a global phenomenon that carries large volumes of groundwater and dissolved chemical species such as nutrient, metals, and organic compounds to coastal zones. We report the influence of SGD on the coastal waters of Jeju Island, Korea, using high-resolution aerial thermal infrared (TIR) mapping techniques and field investigations. An aircraft-based system was implemented using a cost-effective TIR camera for aerial TIR mapping. Ground-based calibrations and system integration with GPS/IMU (global positioning system/inertial measurement unit) were performed for the aerial systems. The aerial surveys showed distinct low-temperature signatures of SGD along the coasts of Jeju Island, revealing large groundwater inputs from the coastal aquifers to the ocean. Multiple aerial surveys over a range of seasons and tidal stages revealed that SGD rates dynamically affect the sea surface temperature (SST) of the coastal zone. The in-situ measurements supported that SGD has a substantial influence on the coastal water chemistry as well as SST. Our observations highlight the extent to which aerial-based TIR mapping can serve as a powerful tool for studying SGD and other coastal processes. Copyright © 2016 John Wiley & Sons, Ltd.

This study aims to monitor the long term behavior of the bridge using time series interferometric technique. The accurate interpretation of the interferometric phase in the case of bridge, relies on the understanding of contributions of components such as DEM error, thermal expansion, and linear displacement. To decompose each contribution, this study applied the PSI algorithm using multi-temporal Cosmo-Skymed SAR data for Kimdaejung bridge. This study was able to find the unique displacement pattern, which is called as a deflection. The deflection is commonly caused by the creep, shrinkage, and ageing. Due to the early stage after the completion of the construction, the physical change of the bridge characteristics leads to the deflection. Thus, this paper verifies the potential to monitoring the long term deflection and implies that the bridge need to be monitored continuously.

The Sea Surface Temperature (SST) is one of the most important oceanic environmental factors in determining the change of marine environments and ecological activities. Satellite thermal infrared images can be effective for understanding the global trend of sea surface temperature due to large scale. However, their low spatial resolution caused some limitations in some areas where complicated and refined coastal shapes due to many islands are present as in the Korean Peninsula. The coastal ocean is also very important because human activities interact with the environmental change of coastal area and most aqua farming is distributed in the coastal ocean. Thus, low-cost airborne thermal infrared remote sensing with high resolution capability is considered for verifying its possibility to extract SST and to monitor the changes of coastal environment. In this study, an airborne thermal infrared system was implemented using a low-cost and ground-based thermal infrared camera (FLIR), and more than 8 airborne acquisitions were carried out in the western coast of the Korean Peninsula during the periods between May 23, 2012 and December 7, 2013. The acquired thermal infrared images were radiometrically calibrated using an atmospheric radiative transfer model with a support from a temperature-humidity sensor, and geometrically calibrated using GPS and IMU sensors. In particular, the airborne sea surface temperature acquired in June 25, 2013 was compared and verified with satellite SST as well as ship-borne thermal infrared and in-situ SST data. As a result, the airborne thermal infrared sensor extracted SST with an accuracy of 1oC.

Vertical land motion at tide gauges influences sea level rise acceleration; this must be addressed for interpreting reliable sea level projections. In recent years, tide gauge records for the Eastern coast of Korea have revealed rapid increases in sea level rise compared with the global mean. Pohang Tide Gauge Station has shown a +3.1 cm/year sea level rise since 2013. This study aims to estimate the vertical land motion that influences relative sea level rise observations at Pohang by applying a multi-track Persistent Scatter Interferometric Synthetic Aperture Radar (PS-InSAR) time-series analysis to Sentinel-1 SAR data acquired during 2015–2017. The results, which were obtained at a high spatial resolution (10 m), indicate vertical ground motion of −2.55 cm/year at the Pohang Tide Gauge Station; this was validated by data from a collocated global positioning system (GPS) station. The subtraction of InSAR-derived subsidence rates from sea level rise at the Pohang Tide Gauge Station is 6 mm/year; thus, vertical land motion significantly dominates the sea level acceleration. Natural hazards related to the sea level rise are primarily assessed by relative sea level changes obtained from tide gauges; therefore, tide gauge records should be reviewed for rapid vertical land motion along the vulnerable coastal areas.

A gas-lift reactor having a high mass transfer coefficient (k L a = 80.28 h−1) for a relatively insoluble gas (carbon monoxide; CO) was used to enrich (homo)acetogens from animal feces. Samples of fecal matter from cow, rabbit, chicken, and goat were used as sources of inoculum for the enrichment of CO and H2 utilizing microbial consortia. To confirm the successful enrichment, the Hungate roll tube technique was employed to count and then isolate putative CO utilizers. The results of this work showed that CO and H2 utilizing consortia were established for each inoculum source after 8 days. The number of colony-forming units in cow, rabbit, chicken, and goat fecal samples were 3.83 × 109, 1.03 × 109, 8.3 × 108, and 3.25 × 108 cells/ml, respectively. Forty-two colonies from the animal fecal samples were screened for the ability to utilize CO/H2. Ten of these 42 colonies were capable of utilizing CO/H2. Five isolates from cow feces (samples 5, 6, 8, 16, and 22) were highly similar to previously unknown (homo)acetogen, while cow-7 has shown 99 % similarity with Acetobacterium sp. as acetogens. On the other hand, four isolates from chicken feces (samples 3, 8, 10, and 11) have also shown high CO/H2 utilizing activity. Hence, it is expected that this research could be used as the basis for the rapid enrichment of (homo)acetogenic consortia from various environmental sources.

Although space-borne satellites are useful in obtaining information all around the world, they cannot observe at a suitable time and place. In order to overcome these limitations, an airborne remote sensing system was developed in this study. It is composed of a SAR sensor and a thermal infrared sensor. Additionally GPS, IMU, and thermometer/hygrometer were attached to the plane for radiometric and geometric calibration. The brightness of SAR image varies depending on surface roughness, and capillary waves on the sea surface, which are easily generated by sea winds, induce the surface roughness. Thus, sea surface wind can be estimated using the relationship between quantified SAR backscattering coefficient and the sea surface wind. On the other hand, thermal infrared sensor is sensitive to measure object`s temperature. Sea surface temperature is obtained from the thermal infrared sensor after correcting the atmospheric effects which are located between sea surface and the sensor. Using these two remote sensing sensors mounted on airplane, four test flights were carried out along the west coast of Korea. The obtained SAR and thermal infrared images have shown that these images were useful enough to monitor coastal environment and estimate marine meteorology data.

TanDEM-X (TerraSAR-X add-on for Digitial Elevation Measurement), a German SAR satellite, was launched in June 2010. Since 2012, TanDEM-X mission has provided the most detailed global DEM with unprecedented accuracy. Also, they carried out various experimental mode including long baseline XTI (cross track interferometry). Despite their progressive instrument calibration and high accurate orbit determination, the error of topographic height was still remained in the level of global DEM and was not entirely estimated. Particularly, the DEM accuracy in coastal area including tidal flats has never been reported. In this study, we evaluated the accuracy of TanDEM-X DEM in coastal area by comparing with RTK GPS measurements, and we investigated the optimal baseline condition to generate significant DEM in tidal flat.

Integrated watershed sewage works program(IWSWP) is defined as implementing, by one of local governments or central government, sewage facilities planning, installation and management for a certain watershed in which criteria of water quality is established and two or more local governments are involved. It is shown that IWSWP is more efficient for the improvement of water quality of water body than conventional local sewage works program(CLSWP) and besides, economical benefits can be achieved by integration of facilities and M&O. In this research, case study for 8 watershed area were investigated to evaluate the difference of effect between IWSWP and CLSWP. The research shows that IWSWP has the money saving result of approximately 12 % by cost reduction for sewage works investment. B/C analysis result shows that B/C ratio of IWSWP was 1.749 and it is higher than B/C ratio of 1.439 for CLSWP. At the same time, the B/C sensitivity analysis is carried out for the rate of discount and the recreational use benefit.

Precise image-to-image registration is required to process multi-sensor data together. The purpose of this paper is to develop an algorithm that register between high-resolution optical and SAR images using linear features. As a pre-processing step, initial alignment was fulfilled using manually selected tie points to remove any dislocations caused by scale difference, rotation, and translation of images. Canny edge operator was applied to both images to extract linear features. These features were used to design a cost function that finds matching points based on their similarity. Outliers having larger geometric differences than general matching points were eliminated. The remaining points were used to construct a new transformation model, which was combined the piecewise linear function with the global affine transformation, and applied to increase the accuracy of geometric correction.

Waterline detection in the intertidal areas was investigated through synthetic aperture radar (SAR) images and field measurements. Two valuable facts were found in this letter: 1) A discrepancy of waterlines between L- and P-band airborne SAR images was discovered and investigated through precise global positioning system measurements and the theory of the SAR imaging mechanism. In the intertidal areas having low slopes, the Bragg waves resonant with the radar signal can reside in different depths depending on the radar frequency, with the result that the boundary between water and land can be mapped differently in the respective SAR images. 2) Intertidal areas covered with a water film present low radar backscatter in SAR images, which can cause mislocation of waterlines

Along-track interferometric synthetic aperture radar (ATI-SAR) is an active coherent imaging system, utilizing two antennas separated along the platform flight direction. The phase information of ATI-SAR from the Doppler shift of the backscattered signal represents the line-of-sight velocity of the water scatterers. While the advent of ATI-SAR provided us with a potentially powerful technique for ocean surface current and wave mapping, the surface current has not been measured exactly from the ATI-SAR velocity because the Doppler shift is not simply proportional to the component of the mean surface current. It also includes other types of contributions associated with the phase velocity of the Bragg waves and orbital motions of all ocean waves that are longer than Bragg waves. In this paper, we review how the phase difference measured by ATI-SAR is related to the mean Doppler frequency, and we develop a new and practically useful method to extract the surface current component utilizing simultaneously measured L- and C-band ATI-SAR data. Since the measured ATI-SAR velocity shows a different value at a different radar-frequency, we investigate the influence of Bragg-resonant waves and long ocean wave motions on the ATI-SAR velocity according to the radar frequency. The Bragg-wave phase velocity component, which is a significant source of error for extracting the surface current, can be effectively eliminated by using L- and C-band ATI-SAR. The method is applied to L- and C-band ATI-SAR measurements acquired at the Ulsan coast in the southeastern part of the Korean peninsula. The resulting ocean surface current vectors are compared with in situ measurements collected by recording current meter. We furthermore extract ocean surface wave information from the ATI-SAR phase image using a quasi-linear transform.

With advance of mobile sensor and wireless communication technologies, it becomes more feasible to monitor daily lives than ever. A smart phone, capable of sensing human motion as well as tracking current location, can record motion behavior as well as motion boundary for pattern study of social life. A passive or active Radio-frequency identification (RFID) based location tracing systems provide the resourceful information of the wildlife habitats. Accelerometers and gyroscopes can help to monitor daily activities of certain patient such as exercise patterns of the obesity. Regardless of how good the sensor is, missing or noisy data can be generated at any time with any reason-caused by the nature of monitoring sensor (on-body sensor) or communication failure. In data analysis, missing or noisy data generates many problems. In the last few decades, many approaches have been proposed. However, traditional approaches of handling missing data may require very complicated method and computational power to estimate the missing data. In this paper, we propose the Canonical Correlation based k weighted Angular Similarity (CkWAS) to map the missing data with reference pattern data set. Our experimental results show that the precision of finding similar motion patterns is 92% with 5% missing data and 64% with 95% missing data.

Pervasive computing becomes very active research field these days. A watch that can trace human movement to record motion boundary as well as to study of finding social life pattern by one's localized visiting area. Pervasive computing also helps patient monitoring. A daily monitoring system helps longitudinal study of patient monitoring such as Alzheimer's and Parkinson's or obesity monitoring. Due to the nature of monitoring sensor (on-body wireless sensor), however, signal noise or faulty sensors errors can be present at any time. Many research works have addressed these problems any with a large amount of sensor deployment. In this paper, we present the faulty sensor detection and isolation using only two on-body sensors. We have been investigating three different types of sensor errors: the SHORT error, the CONSTANT error, and the NOISY SENSOR error (see more details on section V). Our experimental results show that the success rate of isolating faulty signals are an average of over 91.5% on fault type 1, over 92% on fault type 2, and over 99% on fault type 3 with the fault prior of 30% sensor errors.

2010년 11월 국제표준화기구(ISO)가 발표한 ISO26000은 지구화 과정에서 신자유주의에 대한 비판적 성찰의 장으로, 현대 소비자 정치사회구조의 메커니즘을 명확하게 분석할 수 있는 다층적 인자들의 정치적 행위였다. 한국의 경우 1990년대 기업 규모의 확대로 사회적 책임 및 역할의 증대가 요구되었고, 1997년 이후 기업의 사회적 책임은 기업지배구조 및 이윤창출을 위한 사회로의 확대전략을 의미한다. 이 과정에서 소비자들은 국가에 의해 시민으로 동원되면서도 다른 한편으로는, 시장의 공정한 분배질서를 요구하는 등 국가와 기업으로 하여금 공생적 사회질서의 적극적 수립을 요구하였다. 소비자는 이해관계자로의 특수이익과 공익실현이라는 이중적 형태를 띠게 된다. 국가는 시장과 사회의 요구에 대응하면서 공공정책을 통해 양자관계를 조정하는 중개역할이 더욱 중요해졌으며, 기업 역시 저성장시대에 사회적 요구를 시장전략의 일환으로 수용하고 있다.

Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approaches—decision trees (DT) and random forest (RF)—in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15 × 15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data.

This paper investigates a quantization and clustering issue on human motion performance constrained by disabilities. In a longitudinal study of medical therapy on motion disorder, stages of patient disability condition change over time. We investigate four different stages of one arm constrained walking motions by restricting 0%, 10%, 16% and 22% of arm swing angles. For analysis we use One-way ANOVA and K-mean clustering to indentify the most significant features and to partition four different motion constrained groups. Our experimental result shows that all four arm constraints during walking motion are clustered with an average accuracy of 91.7% on two different feature conditions: a mixture of singular value decomposition (SVD) and power spectral density (PSD); and SVD only on selected gait cycles. The proposed method can be integrated with a ubiquitous system (using wearable sensors) for a remote distance patient monitoring system analysis.

Significant amount of research and development is being directed on monitoring activities of daily living of senior citizens who live alone as well as those affected with certain disorders such as Alzheimer's and Parkinson's. A combination of sophisticated inertial sensing, wireless communication and signal processing technologies have made such a pervasive and remote monitoring possible. Due to the nature of the sensing and communication mechanisms, these monitoring sensors are susceptible to errors and failures. In this paper, we address the issue of identifying and isolating faulty sensors in a Body Sensor Network that is used for remote monitoring of daily living activities. We identify three different types of fault isolation strategies and propose both history-based and non-history based approaches.

Fundamental advances in low power circuits, wireless communication, physiological sensor design, and multimedia stream processing, have led to the deployment of body sensor networks for the real-time monitoring of individual health in diverse settings. In this paper we will present a summary of the state-of-the-art in the design and development of aggregation, processing, analysis, and retrieval techniques for body sensor network data streams. In particular, we will focus on multi-modal stream analysis techniques, in distributed and resource constrained environments, for effective real-time healthcare applications. We will describe the associated research challenges ranging from designing novel applications and mining algorithms to systems issues of resource-adaptation, reliability etc., and the intersection of these. We will also present practical deployments and emerging applications of body sensor networks both in individual healthcare as well as in applications for large-scale public health tracking of communities. We will conclude with a summary of open challenges in the field.

Recently, Body Sensor Networks (BSNs) are being deployed for monitoring and managing medical conditions as well as human performance in sports. These BSNs include various sensors such as accelerometers, gyroscopes, EMG (Electromyogram), EKG (Electro-cardiograms), and other sensors depending on the needs of the medical conditions. Data from these sensors are typically Time Series data and the data from multiple sensors form multiple, multidimensional time series data. This tutorial describes the technologies that go behind BSNs -- both in terms of the hardware infrastructure as well as the basic software. First, we outline the BSN hardware features and the related requirements. We then discuss the energy and communication choices for BSNs. Next, we discuss approaches for classification, data mining, visualization, and securing these data. We also show several demonstrations of body sensor networks as well as the software that aid in analyzing the data.

Human gait has been studied in biomechanical and clinical research for a long time. Researchers use many different tools to detect and analyze human walking motions. 3D kinematic data captured by motion capture system is one of powerful tools to analyze the human motions. However, not many researches have focused on coordination of upper extremities during human locomotion. In this paper, we introduce the concept of feature extractions to analyze the upper and lower extremities on human gait. For analyzing coordination between arm joint and leg joint movement, we developed a gait visualization tool to measure velocity and angle of each arm joint and leg joint pair, and compare three different gait speeds (e.g., slow walk, casual walk, and fast walk) for each feature extraction.