Clinicians' evaluations of fetal oximetry sensor placement in a multicentre randomised trial (the FOREMOST trial)

Background: Fetal pulse oximetry (FPO) may improve the assessment of the fetal well-being in labour. Reports of health-care provider's evaluations of new technology are important in the overall evaluation of that technology. Aims: To determine doctors' and midwives' perceptions of their experience placing FPO sensors. Methods: We surveyed clinicians (midwives and doctors) following placement of a FPO sensor during the FOREMOST trial (multicentre randomised trial of fetal pulse oximetry). Clinicians rated ease of sensor placement (poor, fair, good and excellent). Potential influences on ease of sensor placement (staff category, prior experience in Birth Suite, prior experience in placing sensors, epidural analgesia, cervical dilatation and fetal station) were examined by ordinal regression. Results: There were 281 surveys returned for the 294 sensor placement attempts (response rate 96%). Sensors were placed by midwives (29%), research midwives (48%), registrars (22%) and obstetricians (1%). The majority of clinicians had 1 or more years' Birth Suite experience, had placed six or more sensors previously, and rated ease of sensor placement as good. Advancing fetal station (P < 0.001) and the presence of epidural analgesia prior to sensor placement (P = 0.029) predicted improved ease of sensor placement. Having a clinician placing a sensor for the first time predicted a lower rating for ease of sensor placement (P = 0.001), compared to having placed one or more sensors previously. Conclusions: Clinicians with varying levels of Birth Suite experience successfully placed fetal oxygen saturation sensors, with the majority rating ease of sensor placement as good.

Location of innervation zones of sternocleidomastoid and scalene muscles - a basis for clinical and research electromyography applications

Objectives: Advances in surface electromyography (sEMG) techniques provide a clear indication that refinement of electrode location relative to innervation zones (IZ) is required in order to optimise the accuracy, relevance and repeatability of the sEMG signals. The aim of this study was to identify the IZ for the sternocleidomastoid and anterior scalene muscles to provide guidelines for electrode positioning for future clinical and research applications. Methods: Eleven volunteer subjects participated in this study. Myoelectric signals were detected from the sternal and clavicular heads of the stemocleidomastoid and the anterior scalene muscles bilaterally using a linear array of 8 electrodes during isometric cervical flexion contractions. The signals were reviewed and the IZ(s) were identified, marked on the subjects' skin and measurements were obtained relative to selected anatomical landmarks. Results: The position of the IZ lay consistently around the mid-point or in the superior portion of the muscles studied. Conclusions: Results suggest that electrodes should be positioned over the lower portion of the muscle and not the mid-point, which has been commonly used in previous studies. Recommendations for sensor placement on these muscles should assist investigators and clinicians to ensure improved validity in future sEMG applications. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

Achieving cooperation in a distributed multi-robot team

DMAPS (Distributed Multi-Agent Planning System) is a planning system developed for distributed multi-robot teams based on MAPS(Multi-Agent Planning System). MAPS assumes that each agent has the same global view of the environment in order to determine the most suitable actions. This assumption fails when perception is local to the agents: each agent has only a partial and unique view of the environment. DMAPS addresses this problem by creating a probabilistic global view on each agent by fusing the perceptual information from each robot. The experimental results on consuming tasks show that while the probabilistic global view is not identical on each robot, the shared view is still effective in increasing performance of the team.

Multi-robot control in highly dynamic, competitive environments

The control and coordination of multiple mobile robots is a challenging task; particularly in environments with multiple, rapidly moving obstacles and agents. This paper describes a robust approach to multi-robot control, where robustness is gained from competency at every layer of robot control. The layers are: (i) a central coordination system (MAPS), (ii) an action system (AES), (iii) a navigation module, and (iv) a low level dynamic motion control system. The multi-robot coordination system assigns each robot a role and a sub-goal. Each robot’s action execution system then assumes the assigned role and attempts to achieve the specified sub-goal. The robot’s navigation system directs the robot to specific goal locations while ensuring that the robot avoids any obstacles. The motion system maps the heading and speed information from the navigation system to force-constrained motion. This multi-robot system has been extensively tested and applied in the robot soccer domain using both centralized and distributed coordination.

A global vision system for a robot soccer team

This paper describes the real time global vision system for the robot soccer team the RoboRoos. It has a highly optimised pipeline that includes thresholding, segmenting, colour normalising, object recognition and perspective and lens correction. It has a fast ‘paint’ colour calibration system that can calibrate in any face of the YUV or HSI cube. It also autonomously selects both an appropriate camera gain and colour gains robot regions across the field to achieve colour uniformity. Camera geometry calibration is performed automatically from selection of keypoints on the field. The system acheives a position accuracy of better than 15mm over a 4m × 5.5m field, and orientation accuracy to within 1°. It processes 614 × 480 pixels at 60Hz on a 2.0GHz Pentium 4 microprocessor.

Biologically inspired joint control for a humanoid robot

The GuRm is a 1.2m tall, 23 degree of freedom humanoid consuucted at the University of Queensland for research into humanoid robotics. The key challenge being addressed by the GuRw projcct is the development of appropriate learning strategies for control and coodinadon of the robot’s many joints. The development of learning strategies is Seen as a way to sidestep the inherent intricacy of modeling a multi-DOP biped robot. This paper outlines the approach taken to generate an appmpria*e control scheme for the joinis of the GuRoo. The paper demonsrrates the determination of local feedback control parameters using a genetic algorithm. The feedback loop is then augmented by a predictive modulator that learns a form of feed-fonward control to overcome the irregular loads experienced at each joint during the gait cycle. The predictive modulator is based on thc CMAC architecture. Results from tats on the GuRoo platform show that both systems provide improvements in stability and tracking of joint control.

A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.