Determinants of participation in colonoscopic screening by siblings of colorectal cancer patients in France

International audienceBACKGROUND: Targeted colonosocopic screening is recommended for first-degree relatives of colorectal cancer patients diagnosed before the age of 60 and offers the possibility of reducing morbidity and mortality, but participation remains too low. The objective of this study was to determine in a French population the factors that affect siblings' participation in screening, notably those relating to the individuals, their medical care, their family and their social network. METHODS: A cross sectional survey was conducted in siblings of index patients having undergone surgery for colorectal cancer between 1999 and 2002 in two French counties. Siblings were contacted during 2007 and 2008 through the index patient. The factors affecting participation in colonoscopic screening were studied by logistic regression taking into account family cluster effect. RESULTS: 172 siblings of 74 index cases were included. The declared rate of undergoing at least one colonoscopy among siblings was 66%; 95%CI 59-73%. Five variables were independently associated with colonoscopic screening: perceiving fewer barriers to screening (OR = 3.2; 95%CI 1.2-8.5), having received the recommendation to undergo screening from a physician (OR = 4.9; 1.7-13.7), perceiving centres practising colonoscopy as more accessible (OR = 3.2, 1.3-7.8), having discussed screening with all siblings (OR = 3.9; 1.6-9.6) and being a member of an association (OR = 2.6; 1.0-6.6). CONCLUSIONS: The factors independently associated with participation in CRC screening by an individual at increased risk belonged to each of four dimensions relating to his individual psychosocial characteristics, to his relationship with a physician, within the family and social environment. The relevance of these results to clinical practice may help to improve compliance to recommendations in a global preventive strategy including all stages of the information pathway from the physician to the index patient and his relatives

Deep execution monitor for robot assistive tasks

We consider a novel approach to high-level robot task execution for a robot assistive task. In this work we explore the problem of learning to predict the next subtask by introducing a deep model for both sequencing goals and for visually evaluating the state of a task. We show that deep learning for monitoring robot tasks execution very well supports the interconnection between task-level planning and robot operations. These solutions can also cope with the natural non-determinism of the execution monitor. We show that a deep execution monitor leverages robot performance. We measure the improvement taking into account some robot helping tasks performed at a warehouse

Building safer robots: Safety driven control

In recent years there has been a concerted effort to address many of the safety issues associated with physical human-robot interaction (pHRI). However, a number of challenges remain. For personal robots, and those intended to operate in unstructured environments, the problem of safety is compounded. In this paper we argue that traditional system design techniques fail to capture the complexities associated with dynamic environments. We present an overview of our safety-driven control system and its implementation methodology. The methodology builds on traditional functional hazard analysis, with the addition of processes aimed at improving the safety of autonomous personal robots. This will be achieved with the use of a safety system developed during the hazard analysis stage. This safety system, called the safety protection system, will initially be used to verify that safety constraints, identified during hazard analysis, have been implemented appropriately. Subsequently it will serve as a high-level safety enforcer, by governing the actions of the robot and preventing the control layer from performing unsafe operations. To demonstrate the effectiveness of the design, a series of experiments have been conducted using a MobileRobots PeopleBot. Finally, results are presented demonstrating how faults injected into a controller can be consistently identified and handled by the safety protection system. © The Author(s) 2012

AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

Acting in robotics is driven by reactive and deliberative reasonings which take place in the competition between execution and planning processes. Properly balancing reactivity and deliberation is still an open question for harmonious execution of deliberative plans in complex robotic applications. We propose a flexible algorithmic framework to allow continuous real-time planning of complex tasks in parallel of their executions. Our framework, named AMPLE, is oriented towards robotic modular architectures in the sense that it turns planning algorithms into services that must be generic, reactive, and valuable. Services are optimized actions that are delivered at precise time points following requests from other modules that include states and dates at which actions are needed. To this end, our framework is divided in two concurrent processes: a planning thread which receives planning requests and delegates action selection to embedded planning softwares in compliance with the queue of internal requests, and an execution thread which orchestrates these planning requests as well as action execution and state monitoring. We show how the behavior of the execution thread can be parametrized to achieve various strategies which can differ, for instance, depending on the distribution of internal planning requests over possible future execution states in anticipation of the uncertain evolution of the system, or over different underlying planners to take several levels into account. We demonstrate the flexibility and the relevance of our framework on various robotic benchmarks and real experiments that involve complex planning problems of different natures which could not be properly tackled by existing dedicated planning approaches which rely on the standard plan-then-execute loop

An Execution Control System for Autonomous Robots

This paper presents some recent developments of the LAAS architecture for autonomous mobile robots. In particular, we specify the role of the Execution Control level of this architecture. This level has a fault protection role with respect to the commands issued by the decisional level, which are transmitted to the real system (through the functional level). We introduce a new approach and a new tool inspired from the model checking domain. We present a new language to specify the model of acceptable and required states of the system (valid contexts for requests to functional module and resources usage). This language is compiled in an OBDD (Ordered Binary Decision Diagram) like structure which is then used online to check the specified constraints in real-time. Such model checking approach could be extended to check o# line more complex temporal properties of the system