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

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

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

Prevalence of Trypanosoma evansi in camels using molecular and parasitological methods in the southeast of Iran, 2011

Surra is caused by infection with the protozoal parasite, Trypanosoma evansi. This parasite was transmitted mechanically by biting flies which is widespread in camels in the world. The aim of this study is to determine the prevalence of T. evansi in camels in Rafsanjan, Kerman province, southeast of Iran. In this study, 95 suspected camels were randomly selected in 2011. Blood samples were taken from deep blood vessels. Thin and thick blood smears were prepared in laboratory. Blood smears were stained by Giemsa and studied under a light microscope. The positive blood samples were also used for further molecular analysis. Data were analyzed using SPSS 17.0 software and P B 0.05 was considered as statistical difference. A total of 95 camels were examined for infection with T.evansi using parasitological and molecular methods. The overall prevalence of infection was 2.1 %. It was found that the frequency of infection was significantly higher (P\ 0.05) in age group [6 years old than the corresponding younger camels. However, there was no significant difference when the gender was considered. PCR technique confirmed the two infected cases were T. evansi. Results of the present study indicated that surra is present in Rafsanjan county, Kerman province in an infection rate of 2.1 % in camels. To our knowledge, this is the first study reported from this province. Further investigations are needed to focus on vectors and to evaluate the risk factors

Comparison of cytotoxicity of Miltefosine and its niosomal form on chick embryo model

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Various drugs have been used for the treatment of leishmaniasis, but they often have adverse effects on the body's organs. In this study, we aimed to explore the effects of one type of drug, Miltefosine (MIL), and its analogue or modifier, liposomal Miltefosine (NMIL), on several fetal organs using both in silico analysis and practical tests on chicken embryos. Our in silico approach involved predicting the affinities of MIL and NMIL to critical proteins involved in leishmaniasis, including Vascular Endothelial Growth Factor A (VEGF-A), the Kinase insert domain receptor (KDR1), and apoptotic-regulator proteins (Bcl-2-associate). We then validated and supported these predictions through in vivo investigations, analyzing gene expression and pathological changes in angiogenesis and apoptotic mediators in MIL- and NMIL-treated chicken embryos. The results showed that NMIL had a more effective action towards VEGF-A and KDR1 in leishmaniasis, making it a better candidate for potential operative treatment during pregnancy than MIL alone. In vivo, studies also showed that chicken embryos under MIL treatment displayed less vascular mass and more degenerative and apoptotic changes than those treated with NMIL. These results suggest that NMIL could be a better treatment option for leishmaniasis during pregnancy.Peer reviewe

High Frequency of Diarrheagenic Escherichia coli in HIV-Infected Patients and Patients with Thalassemia in Kerman, Iran

This study was conducted on patients with thalassemia and HIV-infected patients to determine the frequency of diarrheagenic Escherichia coli in Kerman, Iran. We analyzed 68 and 49 E coli isolates isolated from healthy fecal samples of patients with thalassemia and HIV-infected patients, respectively. The E coli isolates were studied using a multiplex polymerase chain reaction to identify the enterotoxigenic E coli (ETEC), enterohemorrhagic E coli (EHEC), and enteropathogenic E coli (EPEC) groups. Statistical analysis was carried out to determine the correlation of diarrheagenic E coli between HIVinfected patients and patients with thalassemia using Stata 11.2 software. The frequency of having at least 1 diarrheagenic E coli was more common in patients with thalassemia (67.64%) than in HIV-infected patients (57.14%; P ¼ .25), including ETEC (67.64% versus 57.14%), EHEC (33.82% versus 26.53%), and EPEC (19.11% versus 16.32%). The results of this study indicate that ETEC, EHEC, and EPEC pathotypes are widespread among diarrheagenic E coli isolates in patients with thalassemia and HIV-infected patients