ABSTRACT
INTRODUCTION: The cycling activity has increased in recent years, either as a means of leisure or physical activity or as means of transport. Discomfort is one of the main complaints for cyclists, especially when related to the type o pavement they use while riding. This work presents a study of measurement and evaluation of human exposure to hand-arm vibration in the leisure cyclist activity in different pavements in order to classify according to vibration discomfort and to vibration injury risk. METHODS: Vibration measurements are performed for three pavement types, asphalt (AS), precast concrete slab (PC), and interlocking concrete blocks (BI), using two bicycle models (time trial speed racing bike, S and mountain bike, MB), and cyclists with different physical characteristics. It is performed a quantitative analysis of each configuration - pavement type × bike model × cyclist - where the daily vibration exposure A(8) is evaluated, as defined in ISO 5349-1 Standard, for 2h daily exposure. It is also evaluated the maximum daily exposure in order to reach limit values, as defined by Directive 2002/44/EC. RESULTS: Based on a subjective analysis (survey), it is evaluated the comfort degree for vibration exposure for each tested pavement, according to a survey within cyclists. Finally, the results are compared using both quantitative and subjective analysis. CONCLUSIONS: Not surprisingly, it has been noticed that the most comfortable pavement type is the asphalt pavement (AS), followed by the precast concrete pavement (PC) and by the interlocking concrete blocks pavement (BI), confirming the opinion pool within cyclists. As a new finding, for some pavement types, bikes and daily journey activities, the vibration levels may reach health limit levels which justify the originality of the work and the importance as guidance for healthy public decisions for new cycle paths.
ABSTRACT
INTRODUCTION: The human body may interact with the structures and these interactions are developed through the application of contact forces, for instance due to walking movement. A structure may undergo changes in the dynamic behaviour when subjected to loads and human bodies. The aim of this paper is to propose a methodology using Artificial Neural Networks (ANN) to calibrate a force platform in order to reduce uncertainties in the vertical Ground Reaction Force measurements and positioning of the applied force for the human gait. METHODS: Force platforms have been used to evaluate the pattern of applied human forces and to fit models for the interaction between pedestrians and structures. The designed force platform consists in two force plates placed side by side in the direction of walking. The reference voltages applied to the Wheatstone bridge were used for calibration as the input data to the ANN, while the output data were the estimated values of the standard weights applied to the force platform. RESULTS: It was presented a framework to enhance traditional calibration methods for force platforms (vertical component) using an ANN. The use of ANN shows significant improvements for the measured variables, leading to better results with lower uncertain values that are smaller than those using a simple traditional calibration. CONCLUSION: The results suggest that the calibration with the ANN method may be useful in obtaining more accurate vertical Ground Reaction Forces and positioning measurements in a force platform for human gait analysis.