Relevant bibliographies by topics / First-person hand activity recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'First-person hand activity recognition'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "First-person hand activity recognition"

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Medarevic, Jelena, Marija Novicic, and Marko Markovic. "Feasibility test of activity index summary metric in human hand activity recognition." Serbian Journal of Electrical Engineering 19, no. 2 (2022): 225–38. http://dx.doi.org/10.2298/sjee2202225m.

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Activity monitoring is a technique for assessing the physical activity that a person undertakes over some time. Activity Index (AI) is a metric that summarizes the raw measurements from tri-axial accelerometers, often used for measuring physical activity. Our research compared the Activity Index for different activity groups and hand usage [1]. We also tested this metric as a classification feature, and how different data acquisition and segmentation parameter configurations influence classification accuracy. Data acquisition was done with a previously developed system that includes a smartwat
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Senyurek, Volkan, Masudul Imtiaz, Prajakta Belsare, Stephen Tiffany, and Edward Sazonov. "Electromyogram in Cigarette Smoking Activity Recognition." Signals 2, no. 1 (2021): 87–97. http://dx.doi.org/10.3390/signals2010008.

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In this study, information from surface electromyogram (sEMG) signals was used to recognize cigarette smoking. The sEMG signals collected from lower arm were used in two different ways: (1) as an individual predictor of smoking activity and (2) as an additional sensor/modality along with the inertial measurement unit (IMU) to augment recognition performance. A convolutional and a recurrent neural network were utilized to recognize smoking-related hand gestures. The model was developed and evaluated with leave-one-subject-out (LOSO) cross-validation on a dataset from 16 subjects who performed t
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Ramirez, Heilym, Sergio A. Velastin, Paulo Aguayo, Ernesto Fabregas, and Gonzalo Farias. "Human Activity Recognition by Sequences of Skeleton Features." Sensors 22, no. 11 (2022): 3991. http://dx.doi.org/10.3390/s22113991.

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In recent years, much effort has been devoted to the development of applications capable of detecting different types of human activity. In this field, fall detection is particularly relevant, especially for the elderly. On the one hand, some applications use wearable sensors that are integrated into cell phones, necklaces or smart bracelets to detect sudden movements of the person wearing the device. The main drawback of these types of systems is that these devices must be placed on a person’s body. This is a major drawback because they can be uncomfortable, in addition to the fact that these
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Ray, Sujan, Khaldoon Alshouiliy, and Dharma P. Agrawal. "Dimensionality Reduction for Human Activity Recognition Using Google Colab." Information 12, no. 1 (2020): 6. http://dx.doi.org/10.3390/info12010006.

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Human activity recognition (HAR) is a classification task that involves predicting the movement of a person based on sensor data. As we can see, there has been a huge growth and development of smartphones over the last 10–15 years—they could be used as a medium of mobile sensing to recognize human activity. Nowadays, deep learning methods are in a great demand and we could use those methods to recognize human activity. A great way is to build a convolutional neural network (CNN). HAR using Smartphone dataset has been widely used by researchers to develop machine learning models to recognize hu
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Gao, Zhiqiang, Dawei Liu, Kaizhu Huang, and Yi Huang. "Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors." Remote Sensing 11, no. 21 (2019): 2531. http://dx.doi.org/10.3390/rs11212531.

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Today’s smartphones are equipped with embedded sensors, such as accelerometers and gyroscopes, which have enabled a variety of measurements and recognition tasks. In this paper, we jointly investigate two types of recognition problems in a joint manner, e.g., human activity recognition and smartphone on-body position recognition, in order to enable more robust context-aware applications. So far, these two problems have been studied separately without considering the interactions between each other. In this study, by first applying a novel data preprocessing technique, we propose a joint recogn
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Guo, Jiang, Jun Cheng, Yu Guo, and Jian Xin Pang. "A Real-Time Dynamic Gesture Recognition System." Applied Mechanics and Materials 333-335 (July 2013): 849–55. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.849.

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In this paper, we present a dynamic gesture recognition system. We focus on the visual sensory information to recognize human activity in form of hand movements from a small, predefined vocabulary. A fast and effective method is presented for hand detection and tracking at first for the trajectory extraction. A novel trajectory correction method is applied for simply but effectively trajectory correction. Gesture recognition is achieved by means of a matching technique by determining the distance between the unknown input direction code sequence and a set of previously defined templates. A dyn
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Bieck, Richard, Reinhard Fuchs, and Thomas Neumuth. "Surface EMG-based Surgical Instrument Classification for Dynamic Activity Recognition in Surgical Workflows." Current Directions in Biomedical Engineering 5, no. 1 (2019): 37–40. http://dx.doi.org/10.1515/cdbme-2019-0010.

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AbstractWe introduce a wearable-based recognition system for the classification of natural hand gestures during dynamic activities with surgical instruments. An armbandbased circular setup of eight EMG-sensors was used to superficially measure the muscle activation signals over the broadest cross-section of the lower arm. Instrument-specific surface EMG (sEMG) data acquisition was performed for 5 distinct instruments. In a first proof-of-concept study, EMG data were analyzed for unique signal courses and features, and in a subsequent classification, both decision tree (DTR) and shallow artific
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Bragin, A. D., and V. G. Spitsyn. "Motor imagery recognition in electroencephalograms using convolutional neural networks." Computer Optics 44, no. 3 (2020): 482–87. http://dx.doi.org/10.18287/2412-6179-co-669.

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Electroencephalography is a widespread method to record brain signals with the use of electrodes located on the surface of the head. This method of recording the brain activity has become popular because it is relatively cheap, compact, and does not require implanting the electrodes directly into the brain. The article is devoted to a problem of recognition of motor imagery by electroencephalogram signals. The nature of such signals is complex. Characteristics of electroencephalograms are individual for every person, also depending on their age and mental state, as well as the presence of nois
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Liu, Dan, Mao Ye, and Jianwei Zhang. "Improving Action Recognition Using Sequence Prediction Learning." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (2020): 2050029. http://dx.doi.org/10.1142/s0218001420500299.

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Skeleton-based action recognition distinguishes human actions using the trajectories of skeleton joints, which can be a good representation of human behaviors. Conventional methods usually construct classifiers with hand-crafted or the learned features to recognize human actions. Different from constructing a direct action classifier for action recognition task, this paper attempts to identify human actions based on the development trends of behavior sequences. Specifically, we first utilize the memory neural network to construct action predictors for each kind of activity. These action predic
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Yin, Guanghao, Shouqian Sun, Dian Yu, Dejian Li, and Kejun Zhang. "A Multimodal Framework for Large-Scale Emotion Recognition by Fusing Music and Electrodermal Activity Signals." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 3 (2022): 1–23. http://dx.doi.org/10.1145/3490686.

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Considerable attention has been paid to physiological signal-based emotion recognition in the field of affective computing. For reliability and user-friendly acquisition, electrodermal activity (EDA) has a great advantage in practical applications. However, EDA-based emotion recognition with large-scale subjects is still a tough problem. The traditional well-designed classifiers with hand-crafted features produce poorer results because of their limited representation abilities. And the deep learning models with auto feature extraction suffer the overfitting drop-off because of large-scale indi
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Dissertations / Theses on the topic "First-person hand activity recognition"

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Boutaleb, Mohamed Yasser. "Egocentric Hand Activity Recognition : The principal components of an egocentric hand activity recognition framework, exploitable for augmented reality user assistance." Electronic Thesis or Diss., CentraleSupélec, 2022. http://www.theses.fr/2022CSUP0007.

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Les êtres humains utilisent leurs mains pour diverses tâches dans la vie quotidienne et professionnelle, ce qui fait que la recherche dans ce domaine a récemment suscitée un grand intérêt. De plus, l'analyse et l'interprétation du comportement humain à l'aide de signaux visuels est l'un des domaines les plus actifs et les plus explorés de la vision par ordinateur. Avec l'arrivée des nouvelles technologies de réalité augmentée, les chercheurs s'intéressent de plus en plus à la compréhension de l'activité de la main d'un point de vue de la première personne, en explorant la pertinence de son uti
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Zhan, Kai. "First-Person Activity Recognition." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12948.

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With advances in sensing technology, automatic recognition of human activities has become a popular research topic. Miniaturised wearable devices can now collect and process the data during activities of daily living. Such technologies rely on algorithms that can effectively combine and interpret wearable sensor data to identify different activities. There are four contributions in this thesis based on a novel wearable device - `Smart Glasses'. The device is able to recognise the subjects' activities of daily living (ADLs) using their first-person vision and motion data. This system consists
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Tadesse, Girmaw Abebe. "Human activity recognition using a wearable camera." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/668914.

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Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first­ person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple
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Fathi, Alireza. "Learning descriptive models of objects and activities from egocentric video." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/48738.

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Recent advances in camera technology have made it possible to build a comfortable, wearable system which can capture the scene in front of the user throughout the day. Products based on this technology, such as GoPro and Google Glass, have generated substantial interest. In this thesis, I present my work on egocentric vision, which leverages wearable camera technology and provides a new line of attack on classical computer vision problems such as object categorization and activity recognition. The dominant paradigm for object and activity recognition over the last decade has been based on usi
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Liu, Hsuan-Ming, and 劉軒銘. "Activity Recognition in First-Person Camera View Based onTemporal Pyramid." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/92962830683022916719.

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碩士<br>國立臺灣大學<br>資訊網路與多媒體研究所<br>101<br>We present a simple but effective online recognition system for detecting interleaved activities of daily life (ADLs) in first-person-view videos. The two major difficulties in detecting ADLs are interleaving and variability in duration. We use temporal pyramid in our system to attack these difficulties, and this means we can use relatively simple models instead of time dependent probability ones such as Hidden semi-Markov model or nested models. The proposed solution includes the combination of conditional random fields (CRF) and an online inference algor
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Lei, Yan-Jing, and 雷晏菁. "Activity Recognition of First-Person Vision and Sleep Posture Analysis." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/ygh973.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>105<br>First-person vision camera technology is getting wildly used in our daily life to record every seconds of our activities, exercise, adventures, and so on. We present a succinct and robust 3D Convolutional Neural Network (CNN) architecture for both long-term and short-term activity recognition in first-person-view (FPV) videos. Recognizing activities allow us to categorize the amorphous input videos into meaningful chapters, enable efficient browsing, and find the fragments we need immediately. Previous methods for this task are based on hand-craft features, s
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Xia, Lu active 21st century. "Recognizing human activity using RGBD data." Thesis, 2014. http://hdl.handle.net/2152/24981.

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Traditional computer vision algorithms try to understand the world using visible light cameras. However, there are inherent limitations of this type of data source. First, visible light images are sensitive to illumination changes and background clutter. Second, the 3D structural information of the scene is lost when projecting the 3D world to 2D images. Recovering the 3D information from 2D images is a challenging problem. Range sensors have existed for over thirty years, which capture 3D characteristics of the scene. However, earlier range sensors were either too expensive, difficult to use
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Books on the topic "First-person hand activity recognition"

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Norris, Pippa. Political Activism: New Challenges, New Opportunities. Edited by Carles Boix and Susan C. Stokes. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199566020.003.0026.

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This article discusses political activism and provides an overview that highlights four key themes that have emerged during the last ten years. The first two themes are the growing recognition of the importance of the institutional context of formal rules for electoral turnout and the widespread erosion of party membership in established democracies and questions about its consequences. The last two themes, on the other hand, are the substantial revival of interest in voluntary associations and social trust spurred by theories of social capital and the expansion of diverse forms of cause-orien
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Book chapters on the topic "First-person hand activity recognition"

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Siddiqi, Faisal. "Paradoxes of Strategic Labour Rights Litigation: Insights from the Baldia Factory Fire Litigation." In Interdisciplinary Studies in Human Rights. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73835-8_4.

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AbstractThis chapter focuses on the legal activism that followed the Ali Enterprises factory fire and its aftermath in Pakistan. This chapter has two purposes: firstly, it documents the legal proceedings that were initiated and pursued in the courts of Pakistan as well as its interconnected developments. Secondly, I aim to use this engagement with the legal proceedings of the Baldia factory fire aftermath as an opportunity for an in-depth reflection on the capacity and, finally, suitability of the judicial process to bring about justice in struggles over human and labour rights. Providing a rare and insider account of the legal proceedings in the Pakistani courts and its interconnected developments, I hope to lay the empirical foundation for the theoretical and strategic claims of this study. It is against the background and based on the experience with the litigation and legal advocacy following the Baldia fire that I examine the two what I perceive as “paradoxes” at the heart of the litigation. The first is the inseparability of the “limited justice” that may result from such litigation on one hand, and the “structural injustice” that informs and determines the conditions the litigation seeks to address—and transform—on the other hand. The second paradox concerns the inseparability of both law and lawlessness as regards the legal context of the litigation, advocacy and policy proposal elements that are here in play.My argument is that these apparently contradictory phenomena not only coexist alongside one another but that they guarantee each other’s existence. This analysis leads me to the conclusion that in order to understand and improve such forms of strategic litigation, it is necessary to measure its success and failure in terms of three distinct but interconnected criteria. These are the tactical, strategic and structural impacts of the litigation. Ultimately, I will argue for rejecting what is often perceived by involved stakeholders to be an unavoidable choice between nihilism, euphoria or incremental reform in this context. But, to the contrary, I will argue for a conception of legal struggles as a means of building sustainable and fruitful forms of resistance and of change based on the recognition and exploitation of these irreconcilable paradoxes rather than fruitless attempts to ignore or transcend these irreconcilable contradictions.
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Verma, Kamal Kant, and Brij Mohan Singh. "A Six-Stream CNN Fusion-Based Human Activity Recognition on RGBD Data." In Challenges and Applications for Hand Gesture Recognition. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9434-6.ch007.

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RGBD-based activity recognition is quite an interesting task in computer vision. Inspired by the exemplary results obtained from automatic features learning from RGBD data, in this work a six-stream CNN fusion approach has been addressed, which is developed on 2D-convolution neural network (2DCNN) and spatial-temporal 3D-convolution neural networks (ST3DCNN). The proposed approach has six streams and runs in parallel, where the first and second streams are used to extract space and time features with the help of a ST3DCNN model. Similarly, the remaining four streams have been used to extract the temporal features by means of two motion templates on motion history image (MHI) and motion energy image (MEI) via a 2DCNN. Further, a support vector machine (SVM) is employed to generate the score from each stream. Finally, a decision level fusion scheme particularly a weighted product model (WPM) to fuse the scores is obtained from all the streams. The effectiveness of the proposed approach has been tested on popular benchmark public datasets, namely UTD-MHAD, and gives promising results.
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Oladapo Adenaiye, Oluwasanmi, Kathleen Marie McPhaul, and Donald K. Milton. "Acute Respiratory Infections: Diagnosis, Epidemiology, Management, and Prevention." In Modern Occupational Diseases Diagnosis, Epidemiology, Management and Prevention. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815049138122010012.

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Acute respiratory infections (ARI) are infectious diseases of the respiratory tract caused by viruses, bacteria, and atypical bacteria. They range in severity and even mild cases may cause a significant reduction in workplace productivity. ARIs commonly occur in outbreaks and disproportionally impact workers in occupations where workers are in close proximity to co-workers, members of the public, or where they reside in densely populated housing. High-risk workers include those in the healthcare sector, protective service, food and meat processing, service, and education industries. A person can become infected by inhaling virus-laden aerosols, having virus-contaminated sprayborne drops impinge on exposed mucous membranes, and touching contaminated surfaces followed by self-inoculation. More than one transfer process may be involved in the transmission, and the dominant route may differ for different causative agents, environments, and activity patterns. Preventing ARI transmission in the workplace must be holistic in approach and begin with anticipation and recognition of potential risks, reinforced by the continuous evaluation and implementation of control strategies. Control measures should be layered and multiple routes of transmission should be addressed. Controls should be adapted to the specific workplace and the ARI to prevent pathogen introduction, rapidly detect cases, and promptly eliminate exposure. Prevention and control can be accomplished by promoting vaccination, improving ventilation and air cleaning, providing paid sick leave, flexible working conditions, and work-from-home options. Promoting hand sanitation and providing appropriate personal protective equipment are important but never sufficient in isolation. Occupational health professionals should partner with workplace engineers and human resource departments to design effective programs.
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Jeon, Moon-Jin, Sang Wan Lee, and Zeungnam Bien. "Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation." In Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-1870-1.ch008.

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As an emerging human-computer interaction (HCI) technology, recognition of human hand gesture is considered a very powerful means for human intention reading. To construct a system with a reliable and robust hand gesture recognition algorithm, it is necessary to resolve several major difficulties of hand gesture recognition, such as inter-person variation, intra-person variation, and false positive error caused by meaningless hand gestures. This paper proposes a learning algorithm and also a classification technique, based on multivariate fuzzy decision tree (MFDT). Efficient control of a fuzzified decision boundary in the MFDT leads to reduction of intra-person variation, while proper selection of a user dependent (UD) recognition model contributes to minimization of inter-person variation. The proposed method is tested first by using two benchmark data sets in UCI Machine Learning Repository and then by a hand gesture data set obtained from 10 people for 15 days. The experimental results show a discernibly enhanced classification performance as well as user adaptation capability of the proposed algorithm.
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Anderson, Cindy L., and Kevin M. Anderson. "Practical Examples of Using Switch-Adapted and Battery-Powered Technology to Benefit Persons With Disabilities." In Handmade Teaching Materials for Students With Disabilities. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6240-5.ch009.

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Handmade switch-adapted toys and LED lights were created by a first grader student as part of a makerspace activity to aid a person with disabilities. Commercial toys and light strings were adapted for ease of use by interrupting the electrical current by use of a handmade battery interrupter and the addition of remote switches. In addition, an illuminated glove was created using conductive thread, LED lights, and an Arduino LilyTiny controller to enable the person with disabilities to signal turns on a disability scooter using hand signs. Basic information on the creation of these materials and their possible use are presented in this chapter.
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Anderson, Cindy L., and Kevin M. Anderson. "Practical Examples of Using Switch-Adapted and Battery-Powered Technology to Benefit Persons With Disabilities." In Research Anthology on Physical and Intellectual Disabilities in an Inclusive Society. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3542-7.ch040.

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Handmade switch-adapted toys and LED lights were created by a first grader student as part of a makerspace activity to aid a person with disabilities. Commercial toys and light strings were adapted for ease of use by interrupting the electrical current by use of a handmade battery interrupter and the addition of remote switches. In addition, an illuminated glove was created using conductive thread, LED lights, and an Arduino LilyTiny controller to enable the person with disabilities to signal turns on a disability scooter using hand signs. Basic information on the creation of these materials and their possible use are presented in this chapter.
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Kumar Sharma, Avinash, Pratiyaksha Mittal, Ritik Ranjan, and Rishabh Chaturvedi. "Bank Robbery Detection System Using Computer Vision." In Advances in Transdisciplinary Engineering. IOS Press, 2023. http://dx.doi.org/10.3233/atde221322.

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We propose a computer-vision-based detection and recognition system which can be used in the banks to detect the anomalous activity of bank robbery. We propose CCTV based robbery detection along with tracking of thieves. We have used computer vision to detect theft and robbers in CCTV footage, without the use of sensors. This system concentrates on object detection. This detection of bank robbery is done based on detecting components, which are prohibited by using in the banks and are common in robbery, like handguns, a person wearing a helmet or a ski mask which comes under the object detection. Apart from these, recognition is done on the human postures like raising hands and kneel down which comes under the posture detection. The security official will be notified about the suspicious event by using Real-time analysis of the movement of any human from CCTV footage and thus gives a chance to avert the same, so that necessary action will be taken by the authority and prevent threat to bank as well as to the human life presents there.
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Tsatsoulis, P. Daphne, Aaron Jaech, Robert Batie, and Marios Savvides. "Multimodal Biometric Hand-Off for Robust Unobtrusive Continuous Biometric Authentication." In IT Policy and Ethics. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2919-6.ch018.

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Conventional access control solutions rely on a single authentication to verify a user’s identity but do nothing to ensure the authenticated user is indeed the same person using the system afterwards. Without continuous monitoring, unauthorized individuals have an opportunity to “hijack” or “tailgate” the original user’s session. Continuous authentication attempts to remedy this security loophole. Biometrics is an attractive solution for continuous authentication as it is unobtrusive yet still highly accurate. This allows the authorized user to continue about his routine but quickly detects and blocks intruders. This chapter outlines the components of a multi-biometric based continuous authentication system. Our application employs a biometric hand-off strategy where in the first authentication step a strong biometric robustly identifies the user and then hands control to a less computationally intensive face recognition and tracking system that continuously monitors the presence of the user. Using multiple biometrics allows the system to benefit from the strengths of each modality. Since face verification accuracy degrades as more time elapses between the training stage and operation time, our proposed hand-off strategy permits continuous robust face verification with relatively simple and computationally efficient classifiers. We provide a detailed evaluation of verification performance using different pattern classification algorithms and show that the final multi-modal biometric hand-off scheme yields high verification performance.
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Lynch, John Roy. "Democrats in the South: The Race Question." In Reminiscences of an Active Life, edited by John Hope Franklin. University Press of Mississippi, 2008. http://dx.doi.org/10.14325/mississippi/9781604731149.003.0050.

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This chapter explores how, when John Roy Lynch came to Chicago, whether or not he should take an active part in politics was one of the first questions that occurred to him. He had no intention of actively participating politically in local matters, but it occurred to him that like some other retired army officers, he could, with propriety, take an active part in national matters. But after going over the field very carefully, Lynch found that conditions nationally, as well as locally, were not such as would justify him in doing so. In fact, beginning with the unfortunate administration of President William Howard Taft, the colored American had no standing with either of the two major parties. The Democratic party, nationally, was still a white man's party and, beginning with the Taft administration, the Republican party was no longer a champion of human rights. In fact, the policy inaugurated by President Taft was equivalent to transforming the Republican party, as far as it was in the power of an administration to do so, into a race proscriptive party. In other words, racial identity regardless of merit was made a bar to official recognition.
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Anisimov, Dmytro, Dmytro Petrushin, and Victor Boguslavsky. "IMPROVEMENT OF PHYSICAL TRAINING OF FIRST-YEAR CADETS OF DNIPROPETROVSK STATE UNIVERSITY OF INTERNAL AFFAIRS." In Scientific space in the conditions of global transformations of the modern world. Publishing House “Baltija Publishing”, 2022. http://dx.doi.org/10.30525/978-9934-26-255-5-1.

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In the conditions of a complex criminogenic situation, a high level of preparedness of police officers for effective actions in regular and extreme conditions is extremely important for successfully solving the tasks of ensuring proper law and order and public safety in the state. The professional training of employees is a decisive factor in the quality of law enforcement activities, ensuring legality in maintaining law and order and fighting crime.A high level of physical training as a component of professional training guarantees the effectiveness of operational and service tasks, ensuring the personal safety of the police officer and his surroundings.In modern society, it is no longer a secret for anyone how important it is for every person to be in good physical shape. The need for this is caused by the rhythm of modern life, new opportunities and challenges of today, and even the fashion for a healthy lifestyle. After all, with the development of society, new relationships also develop, the criminal world also actively uses new opportunities and gaps in the legislation, unprofessional actions or insufficient preparation of employees of the Security Service.Thus, the presence of a high level of physical training, perfect mastery of techniques and tactics for the application of measures of physical influence, self-defense skills and techniques of hand-to-hand combat in regular and extreme conditions of operational-service activity is the professional duty of every police officer.We can come to the conclusion that the level of physical training as a component of professional training affects the effectiveness of operational-service tasks, ensures the personal safety of the police officer and his environment, and therefore requires constant improvement and scientific research. The purpose of the research is the theoretical analysis and synthesis of scientific and methodical literature on the researched topic, as well as the expert examination of the preparedness of the first-year cadets of the Dnipropetrovsk State University of Internal Affairs. The following research methods were used to implement the tasks and achieve the goal: analysis of literary sources; synthesis of literary sources; generalization of literary sources; pedagogical observation; control exercises (tests); assessment of testing levels; mathematical statistics.In order to successfully solve the tasks for physical training classes, the motivational levers of the stable evaluation system, namely the “Scale for evaluating the results of physical training tests” were used. Resultsthe analysis of scientific and methodical literature and documents that regulate the educational process in special higher educational institutions of the armed forces gave reasons to assert that proper physical training is an important prerequisite for high-quality professional preparation of graduates of these universities for practical activities. At the same time, the problem of improving the quality of physical training of cadets in higher educational institutions of the Ministry of Internal Affairs of Ukraine, despite a significant number of developments, remains insufficiently studied.Taking into account the need for police officers to engage in confrontations with aggressive and, as a rule, well-trained violators, the problem of special physical training of cadets of higher educational institutions of the National Police of Ukraine has become extremely urgent.Also, based on our research, we can conclude that due to high workload, cadets do not have the opportunity to regularly engage in physical training and improve their physical level, and classes according to the educational and methodological plan cannot fully develop physical qualities cadets, that is why we see an insufficient development of strength in the cadets of the first year of the DDUVS. This is evidenced by the fact that, according to our research, only 32% (n = 8) of the subjects have an excellent level. Good was noted in 40% (n = 10), and a satisfactory level of strength development was found in 28% (n = 7) of the cadets. Practical implications. The scientific novelty of the obtained results lies in the fact that a complex theoretical and applied monodisciplinary study was conducted, dedicated to solving scientific and practical problems of the state of physical training of first-year cadets of the Dnipropetrovsk State University of Internal Affairs. Value/originality. The obtained research results can be used to improve the level of physical training of cadets of the National Police of Ukraine.
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Conference papers on the topic "First-person hand activity recognition"

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Grewe, Lynne L., Chengzhi Hu, Krishna Tank, et al. "First person perspective video activity recognition." In Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX, edited by Lynne L. Grewe, Erik P. Blasch, and Ivan Kadar. SPIE, 2020. http://dx.doi.org/10.1117/12.2557922.

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Ma, Minghuang, Haoqi Fan, and Kris M. Kitani. "Going Deeper into First-Person Activity Recognition." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.209.

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Iwashita, Yumi, Asamichi Takamine, Ryo Kurazume, and M. S. Ryoo. "First-Person Animal Activity Recognition from Egocentric Videos." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.739.

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Zhan, Kai, Vitor Guizilini, and Fabio Ramos. "Dense motion segmentation for first-person activity recognition." In 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, 2014. http://dx.doi.org/10.1109/icarcv.2014.7064291.

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Demachi, Kazuyuki, and Shi Chen. "Development of Malicious Hand Behaviors Detection Method by Movie Analysis." In 2018 26th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/icone26-81643.

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An urgent lesson learned from Fukushima Daiichi accident is what can happen by natural disaster can also occur by human design. The accident raised a fear that terrorists could cause a similar accident by acts of sabotage against nuclear power plant (NPP) and it is noticeable that threats of terrorism for nuclear security are increased after the accident. When considering sabotage, the prime threat to nuclear power plants, due attention should be paid to sabotage by insiders. Generally, insiders are the individuals with authorized access to nuclear facilities in transport who could attempt una
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Ozkan, Fatih, Mehmet Ali Arabaci, Elif Surer, and Alptekin Temizel. "Boosted multiple kernel learning for first-person activity recognition." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081368.

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Prabhakar, Manav, and Snehasis Mukherjee. "First-person Activity Recognition by Modelling Subject - Action Relevance." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892547.

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Spriggs, Ekaterina H., Fernando De La Torre, and Martial Hebert. "Temporal segmentation and activity classification from first-person sensing." In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009. http://dx.doi.org/10.1109/cvprw.2009.5204354.

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Garcia-Hernando, Guillermo, Shanxin Yuan, Seungryul Baek, and Tae-Kyun Kim. "First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00050.

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Baydoun, Mohamad, Alejandro Betancourt, Pietro Morerio, Lucio Marcenaro, Matthias Rauterberg, and Carlo Regazzoni. "Hand pose recognition in First Person Vision through graph spectral analysis." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952481.

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