Comparison of methods for determining power generated on a rope-braked cycle ergometer during low-intensity exercise

This study compares the instrumentation and analysis techniques used when determining the power expended pedalling a rope-braked ergometer manufactured by Monark (Sweden) during a low intensity test. Power values were generated by eight subjects. The instrumentation consisted of load cells to measure the rope brake forces, a tachometer to measure the flywheel velocity and instrumented pedal cranks manufactured by Schoberer Rad Messtechnik (SRM). The subjects pedalled a rope-braked ergometer at 60 rev min^-1, against a resistance of 3 kg, for 5 minutes. Three different measurements of the mean power were recorded and these were compared with the value given by Monark. The SRM cranks provided two sets of results using different software packages supplied with the cranks. SRM standard software is used for taking measurements during training and cycle races over long time periods. An additional piece of software is provided by SRM called Ptnew, which gives readings of torque and pedal cadence over periods up to 30 seconds. Using the values supplied by Monark each subject generated 180 W of power. The mean power for the eight subjects, measured using the SRM cranks, was 170.36 W (SD 4.11) using the alternative SRM software (Ptnew) over a 30 second period and 173.68 W (SD 2.21) using the standard SRM software. From the direct measurement of the brake forces and flywheel velocity the mean power across the eight subjects was 148.90 W (SD 5.89). The SRM cranks measure the input power, whereas the direct measurement system measures the power output excluding mechanical losses. These values give a figure for the mechanical efficiency for the roped-braked ergometer of 88%. It was found that Monark overestimates the power generated by the subjects when compared with both the SRM systems and the direct measurement instrumentation.     

Predicting ground reaction forces in running using micro-sensors and neural networks

Measurement of ground reaction force (GRF) in running provides a direct indication of the loads to which the body is subjected at each foot-ground contact, and can provide an objective explanation for performance outcomes. Traditionally, the collection of three orthogonal component GRF data in running requires an athlete to complete a series of return loops along a laboratory based runway, within which a force platform is embedded, in order to collect data from a discrete footfall. The major disadvantages associated with this GRF data collection methodology include the inability to assess multiple consecutive foot contacts and the fact that measurements are typically confined to the laboratory. The objective of this research was to investigate the potential for wearable instrumentation to be employed, in conjunction with artificial neural network (ANN) and multiple linear regression (MLR) models, for the estimation of GRF in middle distance running. A modular wearable data acquisition system was developed to acquire in-shoe force (ISF) data. Matched data sets from wearable instrumentation (source data) and force plate (target data) records were collected from elite middle-distance runners under controlled laboratory conditions for the purposes of ANN and MLR model development (MD) and model validation (MV). In terms of statistical measures of prediction accuracy the MLR model was found to provide a superior level of accuracy for the prediction of the vertical and medio-lateral components of GRF and alternatively, the ANN model provided the most accurate predictions of the anterior-posterior component of GRF. The prediction accuracy of each component of GRF was found to be governed by the inherent signal variability, in which case the vertical and anterior-posterior components were more reliable and subsequently predicted significantly more accurately than the medio-lateral component. The emerging capability for obtaining continuous GRF records from wearable instrumentation has the potential to permit unprecedented quantification of training stress and competition demands in running.     

Ninth International Conference on Cultural Economics Boston,Mass., May 8–12, 1996 Call for papers

Ninth International Conference on Cultural Economics Boston, Mass., May 8–12, 1996 Call for papers     

Planning, teaching and assessing the curriculum for pupils with learning difficulties: a response from EQUALS

EQUALS is a national organisation that has a major concern for the development of a curriculum that promotes the inclusion of all pupils. The EQUALS movement has, in the past, published its own materials in support of curriculum development for pupils with learning difficulties. In this article, Keith Humphreys, EQUALS' quality assurance manager, provides a response to the publication of the QCA/DfEE guidelines. He discusses EQUALS' response to these guidelines and proposes a way forward for schools already using EQUALS materials. While acknowledging that the implementation of the QCA/DfEE guidelines will lead to an evolution of good practice, he articulates a concern that staff should continue to appreciate and value their own development work. Keith Humphreys details the ways in which EQUALS intends to support this process of evolution in the future.     

Recent Developments in the Funding of University Research

The UGC norm-based allocation of 1986, the current proposals for the rationalisation of departments, and the proposals of the Advisory Board for the Research Councils (ABRC) for R, X and T class universities are assessed against the actual operations of productive science departments and their patterns of expenditure on research as revealed born a recent sample survey. It is concluded that the UGC's norm-based allocation did not in any systematic way secure increases related to current research activity except for those cases where this came from the student number-based teaching element. Only the creation of larger departments seems likely to increase research output, and often that will best be achieved by increases in student numbers and their associated funding. Institutions similar to T class universities can be achieved by encouraging the recruitment of students at the expense of the public sector, but the distribution between R and X class universities is unsustainable.