Abstract

Abstract

Abstract
The aim of this paper was to identify active drag determinants and classify swimmers based on such features. 67 young swimmers made a maximal 25m Front-Crawl to measure with a speedo-meter the swimming velocity (v), speed-fluctuation (dv) and dv normalized to v (dv/v). Another two 25m bouts with and without a perturbation device were made to estimate active drag coefficient (CDa). Trunk transverse surface area (S) was measured with photogrammetric technique on-land and in the hydrodynamic position. Cluster 1 was related to swimmers with a high speed fluctuation (i.e., dv and dv/v). Cluster 2 was characterized by the anthropometrics (i.e., S). Cluster 3 was associated with the high hydrodynamic profile (i.e.,CDa). The variable that seems to discriminate better the clusters was the dv/v (F=53.680; P<0.001), followed by the dv (F=28.506; P<0.001), CDa (F=21.025; P<0.001), S (F=6.297; P<0.01) and v (F=5.375; P=0.01). Stepwise discriminant analysis extracted 2 functions. Function 1 was mainly defined by dv/v and S (74.3% of variance), while Function 2 was mainly defined by CDa (25.7% of variance). So, it can be concluded that kinematics, anthropometrics and hydrodynamic features are determinant domains to classify and characterize swimmers’ profiles.

Abstract
Assistive Technologies may greatly contribute to give autonomy and independence for individuals with physical limitations. Electric wheelchairs are examples of those assistive technologies and nowadays each time becoming more intelligent due to the use of technology that provides assisted safer driving. Usually, the user controls the electric wheelchair with a conventional analog joystick. However, this implies the need for an appropriate methodology to map the position of the joystick handle, in a Cartesian coordinate system, to the wheelchair wheels intended velocities. This mapping is very important since it will determine the response behavior of the wheelchair to the user manual control. This paper describes the implementation of several joystick mappings in an intelligent wheelchair (IW) prototype. Experiments were performed in a realistic simulator using cerebral palsy users with distinct driving abilities. The users had 6 different joystick control mapping methods and for each user the usability and the users' preference order was measured. The results achieved show that a linear mapping, with appropriate parameters, between the joystick's coordinates and the wheelchair wheel speeds is preferred by the majority of the users.

Abstract

Abstract
ROS (Robot Operating System) is a framework for the development of robotic applications widely used throughout research community due to its modular architecture and distributed nature. Using this framework a robot application consists of several nodes that exchange data over a common middle-ware. Programming new nodes is done by using a ROS API (application programming interface) on one of the available programming languages, such as C++ and python. It is our intention to build a robot that needs to be partially programmed in IEC 61131-3, allowing the end-user to adapt it to any specific industrial environment. In this work we have specified a mapping between the concepts defined in IEC 61131-3 and ROS, and started implementing a library through which IEC 61131-3 programs may co-ordinate their actions with the remaining ROS based robotic application. © 2013 IEEE.