No abstract

Conventionally, a cable driven parallel mechanism (CDPM) pose is obtained through the forward kinematics from measurements of the cable lengths. However, this estimation method can be limiting for some applications requiring more precision. This paper proposes to use cable angle position sensors in addition to cable length measurements in order to improve the accuracy of such mechanisms. The robot pose is first obtained individually by the cable length measurements and the cable angle position measurements. A data fusion scheme combining these two types of measurements is then proposed in order to improve the CPDM accuracy. Finally, simulations and experiments are presented in order to assess the benefits of using cable angle position sensors on the CDPM.

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Although emotion regulation has been proposed to be crucial for empathy, investigations on emotion regulation have been primarily limited to intrapersonal processes, leaving the interpersonal processes of self-regulation rather unexplored. Moreover, studies showed that emotion regulation and empathy are related with increased autonomic activation. How emotion regulation and empathy are related at the autonomic level, and more specifically during differently valenced social situations remains an open question. Healthy adults viewed a series of short videos illustrating a target who was expressing positive, negative, or no emotions during a social situation (Positive, Negative, or Neutral Social Scenes). Prior to each video, participants were instructed to reappraise their own emotions (Up-regulation, Down-regulation, or No-regulation). To assess autonomic activation, RR intervals (RRI), high frequency (HF) components of heart rate variability (HRV), and electrodermal activity phasic responses (EDRs) were calculated. Situational empathy was measured through a visual analogue scale. Participants rated how empathic they felt for a specific target. Up- and Down-regulation were related to an increase and a decrease in situational empathy and an increase in RRI and HF, respectively, compared to the control condition (No-regulation). This suggests increased activity of the parasympathetic branch during emotion regulation of situational empathic responses. Positive compared to Negative Social Scenes were associated with decreased situational empathy, in addition to a slightly but non-significantly increased HF. Altogether, this study demonstrates that emotion regulation may be associated with changes in situational empathy and autonomic responses, preferentially dominated by the parasympathetic branch and possibly reflecting an increase of regulatory processes. Furthermore, the current study provides evidence that empathy for different emotional valences is associated with distinct changes in situational empathy and autonomic responses.

No abstract

Finding a physiological signature of a player's fun is a goal yet to be achieved in the field of adaptive gaming. The research presented in this paper tackles this issue by gathering physiological, behavioural and self-report data from over 200 participants who played off-the-shelf video games from the Assassin's Creed series within a minimally invasive laboratory environment. By leveraging machine learning techniques the prediction of the player's fun from its physiological and behavioural markers becomes a possibility. They provide clues as to which signals are the most relevant in establishing a physiological signature of the fun factor by providing an importance score based on the predictive power of each signal. Identifying those markers and their impact will prove crucial in the development of adaptive video games. Adataptive games that tailor their gameplay to the affective state of a player in order to deliver the optimal gaming experience. Indeed, an adaptive video game needs a continuous reading of the fun level to be able to respond to these changing fun levels in real time. While the predictive power of the presented classifier remains limited with a gain in the F1 score of 15% against random chance, it brings insight as to which physiological features might be the most informative for further analyses and discuss means by which low accuracy classification could still improve gaming experience.

No abstract

Physical interactions within virtual environments are often limited to visual information within a restricted workspace. A new system exploiting a cable-driven parallel robot to combine visual and haptic information related to environmental physical constraints (e.g. shelving, object weight) was developed. The aim of this study was to evaluate the impact on user movement patterns of adding haptic feedback in a virtual environment with this robot. Twelve healthy participants executed a manual handling task under three conditions: 1) in a virtual environment with haptic feedback; 2) in a virtual environment without haptic feedback; 3) in a real physical environment. Temporal parameters (movement time, peak velocity, movement smoothness, time to maximum flexion, time to peak wrist velocity) and spatial parameters of movement (maximum trunk flexion, range of motion of the trunk, length of the trajectory, index of curvature and maximum clearance from the shelf) were analysed during the reaching, lowering and lifting phases. Our results suggest that adding haptic feedback improves spatial parameters of movement to better respect the environmental constraints. However, the visual information presented in the virtual environment through the head mounted display appears to have an impact on temporal parameters of movement leading to greater movement time. Taken together, our results suggest that a cable-driven robot can be a promising device to provide a more ecological context during complex tasks in virtual reality.