The Experts below are selected from a list of 25998 Experts worldwide ranked by ideXlab platform
C. Odd - One of the best experts on this subject based on the ideXlab platform.
-
on behalf of the British Occupational Hygiene Society doi:10.1093/annhyg/men010 Sharps Injuries in Healthcare Waste Handlers
2015Co-Authors: J. I. Blenkharn, C. OddAbstract:Clinical waste disposal carries with it a risk of serious and possibly life-threatening infection. Combining confidential questionnaires and structured interviews with Discrete Observation, the attitudes and approach to safe handling of bulk clinical wastes by staff in a specialist waste treatment facility were assessed. With particular attention to glove use and hand hygiene, ob-servations were supplemented by review of group-wide accident and incident records, with emphasis on sharps injuries and related blood and bloodstained body fluid exposures. Deficiencies in glove selection and use, and in hand hygiene, were noted despite extensive and on-going training and supervision of waste handlers. Though ballistic puncture-resistant gloves protect against sharps injury, these were uncomfortable in use and were sometimes re-jected by waste handlers who preferred thin-walled nitrile gloves that were more comfortable in use though provide no resistance to penetrating injury. Among the waste handlers working for a single specialist waste disposal company, sharps injuries (n 5 40) occurred at a rate of approximately 1 per 29 000 man hours. Injuries were caused by hypodermic needles from im-properly closed or overfilled sharps boxes (n 5 6) or from sharps incorrectly discarded into thin-walled plastic sacks intended only for soft wastes (n 5 34). Most injuries occurred t
-
Sharps Injuries in Healthcare Waste Handlers
The Annals of occupational hygiene, 2008Co-Authors: J. I. Blenkharn, C. OddAbstract:Clinical waste disposal carries with it a risk of serious and possibly life-threatening infection. Combining confidential questionnaires and structured interviews with Discrete Observation, the attitudes and approach to safe handling of bulk clinical wastes by staff in a specialist waste treatment facility were assessed. With particular attention to glove use and hand hygiene, Observations were supplemented by review of group-wide accident and incident records, with emphasis on sharps injuries and related blood and bloodstained body fluid exposures. Deficiencies in glove selection and use, and in hand hygiene, were noted despite extensive and on-going training and supervision of waste handlers. Though ballistic puncture-resistant gloves protect against sharps injury, these were uncomfortable in use and were sometimes rejected by waste handlers who preferred thin-walled nitrile gloves that were more comfortable in use though provide no resistance to penetrating injury. Among the waste handlers working for a single specialist waste disposal company, sharps injuries (n = 40) occurred at a rate of approximately 1 per 29 000 man hours. Injuries were caused by hypodermic needles from improperly closed or overfilled sharps boxes (n = 6) or from sharps incorrectly discarded into thin-walled plastic sacks intended only for soft wastes (n = 34). Most injuries occurred to the fingers or hands. No seroconversions occurred, though two individuals suffered anxiety/ stress disorder necessitating prolonged leave of absence with professional counselling and support. Glove use and hand hygiene must feature prominently in the on-going training of waste handlers. Though ballistic gloves afford protection against sharps injury, the initial segregation and safe disposal of clinical wastes by healthcare professionals must provide the primary control measure. Despite robust and unambiguous legislation and good practice guidelines, serious errors by healthcare staff that result in the disposal of hypodermic needles and other sharps to thin-walled plastic waste sacks places waste handlers at risk of bloodborne virus infection. Further improvement in the standards of waste segregation and disposal by healthcare professionals are still required to protect ancillary and support staff and waste handlers working in the disposal sector.
Vijay Kumar - One of the best experts on this subject based on the ideXlab platform.
-
stochastic motion planning under partial observability for mobile robots with continuous range measurements
IEEE Transactions on Robotics, 2020Co-Authors: Ke Sun, Brent Schlotfeldt, George J Pappas, Vijay KumarAbstract:In this article, we address the problem of stochastic motion planning under partial observability, more specifically, how to navigate a mobile robot equipped with continuous range sensors, such as LIDAR. In contrast to many existing robotic motion planning methods, we explicitly consider the uncertainty of the robot state by modeling the system as a partially observable Markov decision process (POMDP). Recent work on general purpose POMDP solvers is typically limited to Discrete Observation spaces, and does not readily apply to the proposed problem due to the continuous measurements from LIDAR. In this article, we build upon an existing Monte Carlo tree search method, partially observable Monte Carlo planning (POMCP), and propose a new algorithm POMCP++. Our algorithm can handle continuous Observation spaces with a novel measurement selection strategy. The POMCP++ algorithm overcomes overoptimism in the value estimation of a rollout policy by removing the implicit perfect state assumption at the rollout phase. We validate POMCP++ in theory by proving it is a Monte Carlo tree search algorithm. Through comparisons with other methods that can also be applied to the proposed problem, we show that POMCP++ yields significantly higher success rate and total reward.
-
stochastic motion planning under partial observability for mobile robots with continuous range measurements
arXiv: Robotics, 2020Co-Authors: Ke Sun, Brent Schlotfeldt, George J Pappas, Vijay KumarAbstract:In this paper, we address the problem of stochastic motion planning under partial observability, more specifically, how to navigate a mobile robot equipped with continuous range sensors such as LIDAR. In contrast to many existing robotic motion planning methods, we explicitly consider the uncertainty of the robot state by modeling the system as a POMDP. Recent work on general purpose POMDP solvers is typically limited to Discrete Observation spaces, and does not readily apply to the proposed problem due to the continuous measurements from LIDAR. In this work, we build upon an existing Monte Carlo Tree Search method, POMCP, and propose a new algorithm POMCP++. Our algorithm can handle continuous Observation spaces with a novel measurement selection strategy. The POMCP++ algorithm overcomes over-optimism in the value estimation of a rollout policy by removing the implicit perfect state assumption at the rollout phase. We validate POMCP++ in theory by proving it is a Monte Carlo Tree Search algorithm. Through comparisons with other methods that can also be applied to the proposed problem, we show that POMCP++ yields significantly higher success rate and total reward.
J. I. Blenkharn - One of the best experts on this subject based on the ideXlab platform.
-
on behalf of the British Occupational Hygiene Society doi:10.1093/annhyg/men010 Sharps Injuries in Healthcare Waste Handlers
2015Co-Authors: J. I. Blenkharn, C. OddAbstract:Clinical waste disposal carries with it a risk of serious and possibly life-threatening infection. Combining confidential questionnaires and structured interviews with Discrete Observation, the attitudes and approach to safe handling of bulk clinical wastes by staff in a specialist waste treatment facility were assessed. With particular attention to glove use and hand hygiene, ob-servations were supplemented by review of group-wide accident and incident records, with emphasis on sharps injuries and related blood and bloodstained body fluid exposures. Deficiencies in glove selection and use, and in hand hygiene, were noted despite extensive and on-going training and supervision of waste handlers. Though ballistic puncture-resistant gloves protect against sharps injury, these were uncomfortable in use and were sometimes re-jected by waste handlers who preferred thin-walled nitrile gloves that were more comfortable in use though provide no resistance to penetrating injury. Among the waste handlers working for a single specialist waste disposal company, sharps injuries (n 5 40) occurred at a rate of approximately 1 per 29 000 man hours. Injuries were caused by hypodermic needles from im-properly closed or overfilled sharps boxes (n 5 6) or from sharps incorrectly discarded into thin-walled plastic sacks intended only for soft wastes (n 5 34). Most injuries occurred t
-
Sharps Injuries in Healthcare Waste Handlers
The Annals of occupational hygiene, 2008Co-Authors: J. I. Blenkharn, C. OddAbstract:Clinical waste disposal carries with it a risk of serious and possibly life-threatening infection. Combining confidential questionnaires and structured interviews with Discrete Observation, the attitudes and approach to safe handling of bulk clinical wastes by staff in a specialist waste treatment facility were assessed. With particular attention to glove use and hand hygiene, Observations were supplemented by review of group-wide accident and incident records, with emphasis on sharps injuries and related blood and bloodstained body fluid exposures. Deficiencies in glove selection and use, and in hand hygiene, were noted despite extensive and on-going training and supervision of waste handlers. Though ballistic puncture-resistant gloves protect against sharps injury, these were uncomfortable in use and were sometimes rejected by waste handlers who preferred thin-walled nitrile gloves that were more comfortable in use though provide no resistance to penetrating injury. Among the waste handlers working for a single specialist waste disposal company, sharps injuries (n = 40) occurred at a rate of approximately 1 per 29 000 man hours. Injuries were caused by hypodermic needles from improperly closed or overfilled sharps boxes (n = 6) or from sharps incorrectly discarded into thin-walled plastic sacks intended only for soft wastes (n = 34). Most injuries occurred to the fingers or hands. No seroconversions occurred, though two individuals suffered anxiety/ stress disorder necessitating prolonged leave of absence with professional counselling and support. Glove use and hand hygiene must feature prominently in the on-going training of waste handlers. Though ballistic gloves afford protection against sharps injury, the initial segregation and safe disposal of clinical wastes by healthcare professionals must provide the primary control measure. Despite robust and unambiguous legislation and good practice guidelines, serious errors by healthcare staff that result in the disposal of hypodermic needles and other sharps to thin-walled plastic waste sacks places waste handlers at risk of bloodborne virus infection. Further improvement in the standards of waste segregation and disposal by healthcare professionals are still required to protect ancillary and support staff and waste handlers working in the disposal sector.
Ke Sun - One of the best experts on this subject based on the ideXlab platform.
-
stochastic motion planning under partial observability for mobile robots with continuous range measurements
IEEE Transactions on Robotics, 2020Co-Authors: Ke Sun, Brent Schlotfeldt, George J Pappas, Vijay KumarAbstract:In this article, we address the problem of stochastic motion planning under partial observability, more specifically, how to navigate a mobile robot equipped with continuous range sensors, such as LIDAR. In contrast to many existing robotic motion planning methods, we explicitly consider the uncertainty of the robot state by modeling the system as a partially observable Markov decision process (POMDP). Recent work on general purpose POMDP solvers is typically limited to Discrete Observation spaces, and does not readily apply to the proposed problem due to the continuous measurements from LIDAR. In this article, we build upon an existing Monte Carlo tree search method, partially observable Monte Carlo planning (POMCP), and propose a new algorithm POMCP++. Our algorithm can handle continuous Observation spaces with a novel measurement selection strategy. The POMCP++ algorithm overcomes overoptimism in the value estimation of a rollout policy by removing the implicit perfect state assumption at the rollout phase. We validate POMCP++ in theory by proving it is a Monte Carlo tree search algorithm. Through comparisons with other methods that can also be applied to the proposed problem, we show that POMCP++ yields significantly higher success rate and total reward.
-
stochastic motion planning under partial observability for mobile robots with continuous range measurements
arXiv: Robotics, 2020Co-Authors: Ke Sun, Brent Schlotfeldt, George J Pappas, Vijay KumarAbstract:In this paper, we address the problem of stochastic motion planning under partial observability, more specifically, how to navigate a mobile robot equipped with continuous range sensors such as LIDAR. In contrast to many existing robotic motion planning methods, we explicitly consider the uncertainty of the robot state by modeling the system as a POMDP. Recent work on general purpose POMDP solvers is typically limited to Discrete Observation spaces, and does not readily apply to the proposed problem due to the continuous measurements from LIDAR. In this work, we build upon an existing Monte Carlo Tree Search method, POMCP, and propose a new algorithm POMCP++. Our algorithm can handle continuous Observation spaces with a novel measurement selection strategy. The POMCP++ algorithm overcomes over-optimism in the value estimation of a rollout policy by removing the implicit perfect state assumption at the rollout phase. We validate POMCP++ in theory by proving it is a Monte Carlo Tree Search algorithm. Through comparisons with other methods that can also be applied to the proposed problem, we show that POMCP++ yields significantly higher success rate and total reward.
Brent Schlotfeldt - One of the best experts on this subject based on the ideXlab platform.
-
stochastic motion planning under partial observability for mobile robots with continuous range measurements
IEEE Transactions on Robotics, 2020Co-Authors: Ke Sun, Brent Schlotfeldt, George J Pappas, Vijay KumarAbstract:In this article, we address the problem of stochastic motion planning under partial observability, more specifically, how to navigate a mobile robot equipped with continuous range sensors, such as LIDAR. In contrast to many existing robotic motion planning methods, we explicitly consider the uncertainty of the robot state by modeling the system as a partially observable Markov decision process (POMDP). Recent work on general purpose POMDP solvers is typically limited to Discrete Observation spaces, and does not readily apply to the proposed problem due to the continuous measurements from LIDAR. In this article, we build upon an existing Monte Carlo tree search method, partially observable Monte Carlo planning (POMCP), and propose a new algorithm POMCP++. Our algorithm can handle continuous Observation spaces with a novel measurement selection strategy. The POMCP++ algorithm overcomes overoptimism in the value estimation of a rollout policy by removing the implicit perfect state assumption at the rollout phase. We validate POMCP++ in theory by proving it is a Monte Carlo tree search algorithm. Through comparisons with other methods that can also be applied to the proposed problem, we show that POMCP++ yields significantly higher success rate and total reward.
-
stochastic motion planning under partial observability for mobile robots with continuous range measurements
arXiv: Robotics, 2020Co-Authors: Ke Sun, Brent Schlotfeldt, George J Pappas, Vijay KumarAbstract:In this paper, we address the problem of stochastic motion planning under partial observability, more specifically, how to navigate a mobile robot equipped with continuous range sensors such as LIDAR. In contrast to many existing robotic motion planning methods, we explicitly consider the uncertainty of the robot state by modeling the system as a POMDP. Recent work on general purpose POMDP solvers is typically limited to Discrete Observation spaces, and does not readily apply to the proposed problem due to the continuous measurements from LIDAR. In this work, we build upon an existing Monte Carlo Tree Search method, POMCP, and propose a new algorithm POMCP++. Our algorithm can handle continuous Observation spaces with a novel measurement selection strategy. The POMCP++ algorithm overcomes over-optimism in the value estimation of a rollout policy by removing the implicit perfect state assumption at the rollout phase. We validate POMCP++ in theory by proving it is a Monte Carlo Tree Search algorithm. Through comparisons with other methods that can also be applied to the proposed problem, we show that POMCP++ yields significantly higher success rate and total reward.