Symbiotic interaction between humans and robot swarms

Comprising of a potentially large team of autonomous cooperative robots locally interacting and communicating with each other, robot swarms provide a natural diversity of parallel and distributed functionalities, high flexibility, potential for redundancy, and fault-tolerance. The use of autonomous mobile robots is expected to increase in the future and swarm robotic systems are envisioned to play important roles in tasks such as: search and rescue (SAR) missions, transportation of objects, surveillance, and reconnaissance operations. To robustly deploy robot swarms on the field with humans, this research addresses the fundamental problems in the relatively new field of human-swarm interaction (HSI). Four groups of core classes of problems have been addressed for proximal interaction between humans and robot swarms: interaction and communication; swarm-level sensing and classification; swarm coordination; swarm-level learning. The primary contribution of this research aims to develop a bidirectional human-swarm communication system for non-verbal interaction between humans and heterogeneous robot swarms. The guiding field of application are SAR missions. The core challenges and issues in HSI include: How can human operators interact and communicate with robot swarms? Which interaction modalities can be used by humans? How can human operators instruct and command robots from a swarm? Which mechanisms can be used by robot swarms to convey feedback to human operators? Which type of feedback can swarms convey to humans? In this research, to start answering these questions, hand gestures have been chosen as the interaction modality for humans, since gestures are simple to use, easily recognized, and possess spatial-addressing properties. To facilitate bidirectional interaction and communication, a dialogue-based interaction system is introduced which consists of: (i) a grammar-based gesture language with a vocabulary of non-verbal commands that allows humans to efficiently provide mission instructions to swarms, and (ii) a swarm coordinated multi-modal feedback language that enables robot swarms to robustly convey swarm-level decisions, status, and intentions to humans using multiple individual and group modalities. The gesture language allows humans to: select and address single and multiple robots from a swarm, provide commands to perform tasks, specify spatial directions and application-specific parameters, and build iconic grammar-based sentences by combining individual gesture commands. Swarms convey different types of multi-modal feedback to humans using on-board lights, sounds, and locally coordinated robot movements. The swarm-to-human feedback: conveys to humans the swarm's understanding of the recognized commands, allows swarms to assess their decisions (i.e., to correct mistakes: made by humans in providing instructions, and errors made by swarms in recognizing commands), and guides humans through the interaction process. The second contribution of this research addresses swarm-level sensing and classification: How can robot swarms collectively sense and recognize hand gestures given as visual signals by humans? Distributed sensing, cooperative recognition, and decision-making mechanisms have been developed to allow robot swarms to collectively recognize visual instructions and commands given by humans in the form of gestures. These mechanisms rely on decentralized data fusion strategies and multi-hop messaging passing algorithms to robustly build swarm-level consensus decisions. Measures have been introduced in the cooperative recognition protocol which provide a trade-off between the accuracy of swarm-level consensus decisions and the time taken to build swarm decisions. The third contribution of this research addresses swarm-level cooperation: How can humans select spatially distributed robots from a swarm and the robots understand that they have been selected? How can robot swarms be spatially deployed for proximal interaction with humans? With the introduction of spatially-addressed instructions (pointing gestures) humans can robustly address and select spatially- situated individuals and groups of robots from a swarm. A cascaded classification scheme is adopted in which, first the robot swarm identifies the selection command (e.g., individual or group selection), and then the robots coordinate with each other to identify if they have been selected. To obtain better views of gestures issued by humans, distributed mobility strategies have been introduced for the coordinated deployment of heterogeneous robot swarms (i.e., ground and flying robots) and to reshape the spatial distribution of swarms. The fourth contribution of this research addresses the notion of collective learning in robot swarms. The questions that are answered include: How can robot swarms learn about the hand gestures given by human operators? How can humans be included in the loop of swarm learning? How can robot swarms cooperatively learn as a team? Online incremental learning algorithms have been developed which allow robot swarms to learn individual gestures and grammar-based gesture sentences supervised by human instructors in real-time. Humans provide different types of feedback (i.e., full or partial feedback) to swarms for improving swarm-level learning. To speed up the learning rate of robot swarms, cooperative learning strategies have been introduced which enable individual robots in a swarm to intelligently select locally sensed information and share (exchange) selected information with other robots in the swarm. The final contribution is a systemic one, it aims on building a complete HSI system towards potential use in real-world applications, by integrating the algorithms, techniques, mechanisms, and strategies discussed in the contributions above. The effectiveness of the global HSI system is demonstrated in the context of a number of interactive scenarios using emulation tests (i.e., performing simulations using gesture images acquired by a heterogeneous robotic swarm) and by performing experiments with real robots using both ground and flying robots

Automated breast profile segmentation for ROI detection using digital mammograms

Abstract—Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer. Finding an accurate, robust and efficient breast profile segmentation technique still remains a challenging problem in digital mammography. Extraction of the breast profile region and the pectoral muscle is an essential pre-processing step in the process of computer-aided detection. Primarily it allows the search for abnormalities to be limited to the region of the breast tissue without undue influence from the background of the mammogram. The presence of pectoral muscle in mammograms biases detection procedures, which recommends removing the pectoral muscle during mammogram pre-processing. In this paper we explore an automated technique for mammogram segmentation. The proposed algorithm uses morphological preprocessing and seeded region growing (SRG) algorithm in order to: (1) remove digitization noises, (2) suppress radiopaque artifacts, (3) separate background region from the breast profile region, and (4) remove the pectoral muscle, for accentuating the breast profile region. To demonstrate the capability of our proposed approach, digital mammograms from two separate sources are tested using Ground Truth (GT) images for evaluation of performance characteristics. Experimental results obtained indicate that the breast regions extracted accurately correspond to the respective GT images

Assessing disparities in medical students’ knowledge and attitude about monkeypox: a cross-sectional study of 27 countries across three continents

Background and aimsThe recent monkeypox (Mpox) outbreak confirmed by the World Health Organization (WHO) underscores the importance of evaluating the knowledge and attitude of medical students toward emerging diseases, given their potential roles as healthcare professionals and sources of public information during outbreaks. This study aimed to assess medical students’ knowledge and attitude about Mpox and to identify factors affecting their level of knowledge and attitude in low-income and high-income countries.MethodsA cross-sectional study was conducted on 11,919 medical students from 27 countries. A newly-developed validated questionnaire was used to collect data on knowledge (14 items), attitude (12 items), and baseline criteria. The relationship between a range of factors with knowledge and attitude was studied using univariate and multivariate analyses.Results46% of the study participants were males; 10.7% were in their sixth year; 54.6% knew about smallpox; 84% received the coronavirus disease 2019 (COVID-19) vaccine; and 12.5% had training on Mpox. 55.3% had good knowledge of Mpox and 51.7% had a positive attitude towards it. Medical students in their third, fifth, or sixth year high- income countries who obtained information on Mpox from friends, research articles, social media and scientific websites were positive predictors for good knowledge. Conversely, being male or coming from high-income countries showed a negative relation with good knowledge about Mpox. Additionally, a positive attitude was directly influenced by residing in urban areas, being in the fifth year of medical education, having knowledge about smallpox and a history of receiving the coronavirus disease 2019 (COVID-19) vaccine. Receiving information about Mpox from social media or scientific websites and possessing good knowledge about Mpox were also predictors of a positive attitude. On the other hand, being male, employed, or receiving a training program about Mpox were inversely predicting positive attitude about Mpox.ConclusionThere were differences in knowledge and attitude towards Mpox between medical students in low and high-income countries, emphasizing the need for incorporating epidemiology of re-emerging diseases like Mpox into the medical curriculum to improve disease prevention and control

Human-Swarm Interaction through Distributed Cooperative Gesture Recognition

The video presents the first results of a Swiss-funded project focusing on symbiotic peer-to-peer interaction and cooperation between humans and robot swarms. As a first step, we considered human-swarm interaction, and selected the use of hand gestures to let a human communicate with a swarm of relatively simple mobile robots. In our scenario, a hand gesture encodes a command, that the swarm will execute. The robots that we used are the foot-bots, developed in the Swarmanoid project [1]. Hand gestures are a powerful and intuitive way to communicate, and do not require the use of additional devices. However, real-time vision-based recognition of hand gestures is a challenging task for the single foot-bot, due to its limited processing power and field of view. We investigated how to exploit robot mobility, swarm spatial distribution, and multiho

Cancer Epidemiology and Control in the Arab World - Past, Present and Future

The Arab world, stretching from Lebanon and Syria in the north, through to Morocco in the west, Yemen in the south and Iraq in the east, is the home of more than 300 million people. Cancer is already a major problem and the lifestyle changes underlying the markedly increasing rates for diabetes suggest that the burden of neoplasia will only become heavier over time, especially with increasing obesity and aging of what are now still youthful populations. The age-distributions of the affected patients in fact might also indicate cohort effects in many cases. There are a number of active registries in the region and population-based data are now available for a considerable number of countries. A body of Arab scientists are also contributing to epidemiological research into the causes of cancer and how to develop effective control programs. The present review covers the relevant PubMed literature and cancer incidence data from various sources, highlighting similarities and variation in the different cancer types, with attempts to explain disparities with reference to possible environmental factors. In males, the predominant cancers vary, with lung, urinary bladder or liver in first place, while for females throughout the region breast cancer is the greatest problem. In both sexes, non-Hodgkins lymphomas and leukemias are relatively frequent, along with thyroid cancer in certain female populations. Adenocarcinomas of the breast, prostate and colorectum appear to be increasing. Coordination of activities within the Arab world could bring major benefits to cancer control in the eastern Mediterranean region