RHM: Robot House Multi-view Human Activity Recognition Dataset

© 2023, IARIA.With the recent increased development of deep neural networks and dataset capabilities, the Human Action Recognition (HAR) domain is growing rapidly in terms of both the available datasets and deep models. Despite this, there are some lacks at datasets specifically covering the Robotics field and Human-Robot interaction. We prepare and introduce a new multi-view dataset to address this. The Robot House Multi-View dataset (RHM) contains four views: Front, Back, Ceiling, and Robot Views. There are 14 classes with 6701 video clips for each view, making a total of 26804 video clips for the four views. The lengths of the video clips are between 1 to 5 seconds. The videos with the same number and the same classes are synchronized in different views. In the second part of this paper, we consider how single streams afford activity recognition using established state-of-the-art models. We then assess the affordance for each of the views based on information theoretic modelling and mutual information concept. Furthermore, we benchmark the performance of different views, thus establishing the strengths and weaknesses of each view relevant to their information content and performance of the benchmark. Our results lead us to conclude that multi-view and multi-stream activity recognition has the added potential to improve activity recognition results

Lightweight human activity recognition for ambient assisted living

© 2023, IARIA.Ambient assisted living (AAL) systems aim to improve the safety, comfort, and quality of life for the populations with specific attention given to prolonging personal independence during later stages of life. Human activity recognition (HAR) plays a crucial role in enabling AAL systems to recognise and understand human actions. Multi-view human activity recognition (MV-HAR) techniques are particularly useful for AAL systems as they can use information from multiple sensors to capture different perspectives of human activities and can help to improve the robustness and accuracy of activity recognition. In this work, we propose a lightweight activity recognition pipeline that utilizes skeleton data from multiple perspectives to combine the advantages of both approaches and thereby enhance an assistive robot's perception of human activity. The pipeline includes data sampling, input data type, and representation and classification methods. Our method modifies a classic LeNet classification model (M-LeNet) and uses a Vision Transformer (ViT) for the classification task. Experimental evaluation on a multi-perspective dataset of human activities in the home (RH-HAR-SK) compares the performance of these two models and indicates that combining camera views can improve recognition accuracy. Furthermore, our pipeline provides a more efficient and scalable solution in the AAL context, where bandwidth and computing resources are often limited

Affordable robot mapping using omnidirectional vision

© 2021 EPSRC UK-Robotics and Autonomous Systems (UK-RAS) Network. This is an open access conference paper distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Mapping is a fundamental requirement for robot navigation.In this paper, we introduce a novel visual mapping method that relies solely on a single omnidirectional camera.We present a metric that allows us to generate a map from the input image by using a visual Sonar approach.The combination of the visual sonars with the robot's odometry enables us to determine a relation equation and subsequently generate a map that is suitable for robot navigation.Results based on visual map comparison indicate that our approach is comparable with the established solutions based on RGB-D cameras or laser-based sensors. We now embark on evaluating our accuracy against the established methods

Robot house human activity recognition dataset

© 2021 EPSRC UK-Robotics and Autonomous Systems (UK-RAS) Network. This is an open access conference paper distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Human activity recognition is one of the most challenging tasks in computer vision. State-of-the art approaches such as deep learning techniques thereby often rely on large labelled datasets of human activities. However, currently available datasets are suboptimal for learning human activities in companion robotics scenarios at home, for example, missing crucial perspectives. With this as a consideration, we present the University of Hertfordshire Robot House Human Activity Recognition Dataset (RH-HAR-1). It contains RGB videos of a human engaging in daily activities, taken from four different cameras. Importantly, this dataset contains two non-standard perspectives: a ceiling-mounted fisheye camera and a mobile robot's view. In the first instance, RH-HAR-1 covers five daily activities with a total of more than 10,000 videos.Human activity recognition is one of the most challenging tasks in computer vision. State-of-the art approaches such as deep learning techniques thereby often rely on large labelled datasets of human activities. However, currently available datasets are suboptimal for learning human activities in companion robotics scenarios at home, for example, missing crucial perspectives. With this as a consideration, we present the University of Hertfordshire Robot House Human Activity Recognition Dataset (RH-HAR-1). It contains RGB videos of a human engaging in daily activities, taken from four different cameras. Importantly, this dataset contains two non-standard perspectives: a ceiling-mounted fisheye camera and a mobile robot's view. In the first instance, RH-HAR-1 covers five daily activities with a total of more than 10,000 videos.Human activity recognition is one of the most challenging tasks in computer vision. State-of-the art approaches such as deep learning techniques thereby often rely on large labelled datasets of human activities. However, currently available datasets are suboptimal for learning human activities in companion robotics scenarios at home, for example, missing crucial perspectives. With this as a consideration, we present the University of Hertfordshire Robot House Human Activity Recognition Dataset (RH-HAR-1). It contains RGB videos of a human engaging in daily activities, taken from four different cameras. Importantly, this dataset contains two non-standard perspectives: a ceiling-mounted fisheye camera and a mobile robot's view. In the first instance, RH-HAR-1 covers five daily activities with a total of more than 10,000 videos

RH-HAR-SK: A Multi-view Dataset with Skeleton Data for Ambient Assisted Living Research

© 2023 IARIA.Human and activity detection has always been a vital task in Human-Robot Interaction (HRI) scenarios, such as those involving assistive robots. In particular, skeleton-based Human Activity Recognition (HAR) offers a robust and effective detection method based on human biomechanics. Recent advancements in human pose estimation have made it possible to extract skeleton positioning data accurately and quickly using affordable cameras. In interaction with a human, robots can therefore capture detailed information from a close distance and flexible perspective. However, recognition accuracy is susceptible to robot movements, where the robot often fails to capture the entire scene. To address this we propose the adoption of external cameras to improve the accuracy of activity recognition on a mobile robot. In support of this proposal, we present the dataset RH-HAR-SK that combines multiple camera perspectives augmented with human skeleton extraction obtained by the HRNet pose estimation. We apply qualitative and quantitative analysis techniques to the extracted skeleton and its joints to demonstrate the additional value of external cameras to the robot's recognition pipeline. Results show that while the robot's camera can provide optimal recognition accuracy in some specific scenarios, an external camera increases overall performance

Media Representation of Hacker as an Edge worker: Toward a Cultural Criminological Analysis of Blue Whale Series

This study places itself within the scope of cultural criminology approach, a multidisciplinary research field that explores crime and reactions to its control from an anarchist view. Cultural criminology places the issues of meaning in the hearts of its studies. Cultural criminologists propose that both crime and its control operate as cultural processes. In this theoretical approach, crime and its control are conceptualized as creative cultural products which are changing in the dynamics of social interactions. It examines how the meaning of crime and its control is continuously constructed in the nonlinear cultural, criminal, and crime control processes. It focuses on the convergence of criminal subcultures, control agents and media processes. For cultural criminologists, the media-based images of crime are one of the main sources of mediated constructions of meaning. They emphasize the centrality of media representations in the construction of crime. In this regard, it chooses an interdisciplinary approach with sociological criminology, cultural studies and media Criminology.This study sought to analyze the media-constructed lived experience of Iranian hackers by using the theoretical approach of cultural criminology. Cultural criminologists draw attention, particularly mainstream variants of criminology, to the fact that the crime control agencies are not the only creators of the meaning of crime and its control. Proposing the idea of commodification and hall of mirrors, they argue that the media representations of criminals and criminal events become a tool for creating the meaning of crime. These images create and consume by criminals, criminal subcultures and control agents. In today's highly controlled world which subcultures become marginalized, media representations become significant, exceptional sources for creating deviant subcultural reality. These images are continuously recycling and reproducing by control agents and subcultures and even other media images. Hence, we are being surrounded in a world in which saturated by different, nonhomogeneous images of crime and its control.This research is accomplished via anarchist methodology of cultural criminology. To this end, we have used the ethnographic content analysis (­ECA­) developed by David Altheide (1980s) and virtual ethnography of hacker subculture. Due to the qualitative nature of the research, the Blue Whale television series were analyzed by using the purposive sampling method. Researches conducted in this area must focus not only on the everyday media images, but also on the complex set of reciprocal and interdependent subcultural relationships which together constitute the dynamic meaning of crime. In this regard, in the subcultural studies section, fifty-four in-depth, semi-structured interviews were conducted with hackers during the six months, and their behaviors were simply observed. All data were coded and analyzed using MAXQDA software.Our findings indicated that the meaning of the hacking is also created through the consumption of media products. Entertainment media can represent the real dimensions of criminal subcultures in the form of attractive media products. In many ways, Blue Whale blend the real-life and movie created footage and blurs the lines between reality and fantasy. The hacker represents a personality similar to many young people, which can evoke audience emotion. According to our virtual ethnography research, some hackers are students or graduates of various fields, especially computers, who suffer from economic problems like the character of the hacker shown in this series. They are humiliated by powerful adults and at the same time, have a creative mind. The fictional hacker of this film, like many hackers in the real world, feels prosperous and empowered by taking risks as part of edgework activities. The combination of skill, creative impulsive behavior, and economic problems lead the hacker to greater ambitions in the criminal profession, and finally, the metaphorical incident of the death of the whale occurs. Moreover, the Blue Whale provides information about the lesser-known dimensions of criminal edge workers that the criminal justice system seeks to distort to prevent delinquency. It also found that by representing parts of the realities of hackers' lived experiences and looping back to the content and form of previous media loops, the series screening a dystopia that provides the possibility of transcending established norms and rules of the life for the offender and the audience

Estimating the Survival of Patients With Lung Cancer: What Is the Best Statistical Model?

Objectives: Investigating the survival of patients with cancer is vitally necessary for controlling the disease and for assessing treatment methods. This study aimed to compare various statistical models of survival and to determine the survival rate and its related factors among patients suffering from lung cancer. Methods: In this retrospective cohort, the cumulative survival rate, median survival time, and factors associated with the survival of lung cancer patients were estimated using Cox, Weibull, exponential, and Gompertz regression models. Kaplan-Meier tables and the log-rank test were also used to analyze the survival of patients in different subgroups. Results: Of 102 patients with lung cancer, 74.5% were male. During the follow-up period, 80.4% died. The incidence rate of death among patients was estimated as 3.9 (95% confidence [CI], 3.1 to 4.8) per 100 person-months. The 5-year survival rate for all patients, males, females, patients with non-small cell lung carcinoma (NSCLC), and patients with small cell lung carcinoma (SCLC) was 17%, 13%, 29%, 21%, and 0%, respectively. The median survival time for all patients, males, females, those with NSCLC, and those with SCLC was 12.7 months, 12.0 months, 16.0 months, 16.0 months, and 6.0 months, respectively. Multivariate analyses indicated that the hazard ratios (95% CIs) for male sex, age, and SCLC were 0.56 (0.33 to 0.93), 1.03 (1.01 to 1.05), and 2.91 (1.71 to 4.95), respectively. Conclusions: Our results showed that the exponential model was the most precise. This model identified age, sex, and type of cancer as factors that predicted survival in patients with lung cancer

Islam and Iran’s post-revolution war on drugs: a Durkheimian analysis

Despite widespread criticism of the failure to achieve the predetermined penal and criminological goals of Iran’s post-revolution war on drugs, the harsh penal practices remain in practice until today. By applying Durkheim’s attitude in his last major work, the elementary forms of religious life, the purpose of this paper is to analyze the rationale for the Iranian war on drugs from the perspective of religion and not penal code or criminology. This article draws on qualitative analysis, and data were collected through analysis of legal documents, literature discussing the war on drugs, news reports, and past journals. The findings of this article reveal that the war on drugs originates from an understanding that attributes evilness to such criminals to prevent the disintegration of Islamic society. This approach blurs the line between “preserving Islam” and the “Islamic society,” and the repressive policies are consecrated to avoid social disintegration. Our study confirms Durkheim’s attitude in which sacredness is highly contagious. Following the sanctity of preserving Islamic society from the profanity of drug crimes, the application of specialized mechanisms for fighting drugs, such as anticipating specialized criminal courts for violation of sacred values, setting special legislative authorities for the crime, and meting out harsh punishments, have all become plausible. Accordingly, all these practices would be treated as sacred because they are associated with fighting the “profane” phenomenon of drugs.