Browsing by Keyword "motion capture"
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Item Estimation of the centre of mass from motion capture and force plate recordings: A study on the elderly(2011) Cotton, S.; Vanoncini, M.; Fraisse, P.; Ramdani, N.; Demircan, E.; Murray, A. P.; Keller, T.; Tecnalia Research & InnovationThe estimation of the centre of mass position in humans is usually based on biomechanical models developed from anthropometric tables. This method can potentially introduce errors in studies involving elderly people, since the ageing process is typically associated with a modification of the distribution of the body mass. In this paper, an alternative technique is proposed, and evaluated with an experimental study on 9 elderly volunteers. The technique is based on a virtual chain, identified from experimental data and locating the subject's centre of mass. Its configuration defines the location of the centre of mass, and is a function of the anatomical joint angles measured on the subject. This method is a valuable investigation tool in the field of geronto-technology, since it overcomes some of the problems encountered with other CoM estimation methods.Item The influence of interaction technology on the learning of assembly tasks using virtual reality(2014-12-01) Vélaz, Yaiza; Arce, Jorge Rodríguez; Gutiérrez, Teresa; Lozano-Rodero, Alberto; Suescun, Angel; CIRMETALThis paper focuses on the use of virtual reality (VR) systems for teaching industrial assembly tasks and studies the influence of the interaction technology on the learning process. The experiment conducted follows a between-subjects design with 60 participants distributed in five groups. Four groups were trained on the target assembly task with a VR system, but each group used a different interaction technology: mouse-based, Phantom Omni® haptic, and two configurations of the Markerless Motion Capture (Mmocap) system (with 2D or 3D tracking of hands). The fifth group was trained with a video tutorial. A post-training test carried out the day after evaluated performance in the real task. The experiment studies the efficiency and effectiveness of each interaction technology for learning the task, taking in consideration both quantitative measures (such as training time, real task performance, evolution from the virtual task to real one), and qualitative data (user feedback from a questionnaire). Results show that there were no significant differences in the final performance among the five groups. However, users trained under mouse and 2D-tracking Mmocap systems took significantly less training time than the rest of the virtual modalities. This brings out two main outcomes: (1) the perception of collisions using haptics does not increase the learning transfer of procedural tasks demanding low motor skills and (2) Mmocap-based interactions can be valid for training this kind of tasks.