Increasing presence is one of the primary goals of virtual reality research. A crucial aspect is that users are capable of distinguishing their self from the external virtual world. The hypothesis we investigate is that wearable haptics play an important role in the body experience and could thereby contribute to the immersion of the user in the virtual environment. A within-subject study (n=32) comparing the embodiment of a virtual hand with different implementations of haptic feedback (force feedback, vibrotactile feedback, and no haptic feedback) is presented. Participants wore a glove with haptic feedback devices at thumb and index finger. They were asked to put virtual cubes on a moving virtual target. Touching a virtual object caused vibrotactile-feedback, force-feedback or no feedback depending on the condition. These conditions were provided both synchronously and asynchronously. Embodiment was assessed quantitatively with the proprioceptive drift and subjectively via a questionnaire. Results show that haptic feedback significantly improves the subjective embodiment of a virtual hand and that force feedback leads to stronger responses to certain subscales of subjective embodiment. These outcomes are useful guidelines for wearable haptic designer and represent a basis for further research concerning human body experience, in reality, and in virtual environments.Index Terms-wearable haptics, embodiment, haptics in VR, tactile feedback, virtual hand illusion.

The rubber hand illusion describes a phenomenon in which participants experience a rubber hand as being part of their body by the synchronous application of visuotactile stimulation to the real and the artificial limb. In the recently introduced robotic hand illusion (RobHI), a robotic hand is incorporated into one’s body representation due to the integration of synchronous visuomotor information. However, there are no setups so far that combine visuotactile and visuomotor feedback, which is expected to unravel mechanisms that cannot be detected in experimental designs applying this information in isolation. We developed a robotic hand, controlled by a sensor glove and equipped with pressure sensors, and varied systematically and separately the synchrony for motor feedback (MF) and tactile feedback (TF). In Experiment 1, we implemented a ball-grasping task and assessed the perceived proprioceptive drift of one’s own hand as a behavioral measure of the spatial calibration of body coordinates as well as explicit embodiment experiences by a questionnaire. Results revealed significant main effects of both MF and TF for proprioceptive drift data, but we only observed main effects for MF on perceived embodiment. Furthermore, for the proprioceptive drift we found that synchronous feedback in one factor compensates for asynchronous feedback in the other. In Experiment 2, including a new sample of naïve participants, we further explored this finding by adding unimodal conditions, in which we manipulated the presence or absence of MF and/or TF. These findings replicated the results from Experiment 1 and we further found evidence for a supper-additive multisensory effect on spatial body representation caused by the presence of both factors. Results on conscious body perception were less consistent across both experiments. The findings indicate that sensory and motor input equally contribute to the representation of spatial body coordinates which for their part are subject to multisensory enhancing effects. The results outline the potential of human-in-the-loop approaches and might have important implications for clinical applications such as for the future design of robotic prostheses.

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The assessment of Parkinson's disease (PD) poses a significant challenge as it is influenced by various factors which lead to a complex and fluctuating symptom manifestation. Thus, a frequent and objective PD assessment is highly valuable for effective health management of people with Parkinson's disease (PwP). Here, we propose a method for monitoring PwP by stochastically modeling the relationships between their wrist movements during unscripted daily activities and corresponding annotations about clinical displays of movement abnormalities. We approach the estimation of PD motor signs by independently modeling and hierarchically stacking Gaussian process models for three classes of commonly observed movement abnormalities in PwP including tremor, (non-tremulous) bradykinesia, and (non-tremulous) dyskinesia. We use clinically adopted severity measures as annotations for training the models, thus allowing our multi-layer Gaussian process prediction models to estimate not only their presence but also their severities. The experimental validation of our approach demonstrates strong agreement of the model predictions with these PD annotations. Our results show the proposed method produces promising results in objective monitoring of movement abnormalities of PD in the presence of arbitrary and unknown voluntary motions, and makes an important step towards continuous monitoring of PD in the home environment.