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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.

Affective computing, with its potential to enhance human-computer interaction, is experiencing an expansion of its use in many areas such as health care and the gaming industry. One obstacle to its widespread adoption can be the high cost requirement for biofeedback. Indeed, typical laboratory setups are often expensive which makes them out of reach for many. This paper explores lower-cost alternatives to expensive laboratory solutions. Data from several recent studies totaling over 200 hours of physiological recordings are leveraged to compare high-end solutions to a lower cost one. Heart rate, electrodermal activity, facial action units, head movement, and eye movement – five of the most used bio-behavioural signals – have their respective higher and lower cost sensors compared. The resulting comparison illustrates that lower-cost solutions are not drop-in replacements. While a correlation of 0.62 between electrodermal activity readings was found, notable differences between reported heart rate readings over small timescales were also observed. Head tracking recordings shared similarity (0.51), but eye tracking did not (0.18). As for facial action units recognition, only those linked to smiling had significant correlation (around 0.48). These results should broaden the range of contexts in which biofeedback could be exploited. This aim may be fulfilled by informing the reader of the extent of lower cost solution applications.

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 important 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. Adaptive games 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 analysis and discuss means by which low accuracy classification could still improve gaming experience.

This paper presents a novel architecture of cable-driven parallel mechanism that yields large orientation capability around one axis. Design decisions and justifications are presented with the aim of producing a human-scale haptic interface used for the rehabilitation of patients in a fully immersive virtual environment. The rehabilitation task to be reproduced by the haptic interface consists in carrying a crate from a shelf to a nearby table. This task requires a large rotation of the crate about the vertical axis, which cannot be achieved by conventional cable-driven parallel architectures. The novel architecture presented can generate Schonflies motion which should be useful in other tasks accomplished by cable-driven parallel robots.

A Kinect-based pose estimation system is presented for the study of movement problems within a rehab context. The performance of the system is compared to ground-truth data obtained by an expensive MoCap system. The results show that the proposed system performs well and could be used within a virtual rehabilitation context synchronized with other systems (e.g., robots).

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.