When we enter Virtual Reality (VR) in the first person, the avatar replaces our real body. Within a certain range of mismatches, we tend to identify with our avatar and perceive it as our own body. Previous research has shown that we can even believe that a deformed forearm is our own through haptic and visual feedback. However, the haptic devices used in earlier research were only explored using skin-stretching and weight-shifting. Vibrotactile feedback is the most common technique used to create a haptic VR experience in academia and industry because of its light weight and ease of application. Taking these advantages, we introduce VibroArm, a lightweight wearable haptic system with the vibrotactile funneling illusion that enhances the virtual forearm’s ownership in the forearm deformation illusion. In a perceptual study, we determined that users can make correct direction judgments about vibration on the forearm in different directions. The result shows that all vibration patterns can be sufficiently recognized, with an average recognition rate of $87.17 \%$. Based on our results, we designed 18 vibration patterns and compared their impact on the forearm deformation illusion. Finally, our study shows that using VibroArm in VR applications can significantly improve user realism and enjoyment compared to relying on visual feedback alone.

Compared to wearable devices, handheld haptic devices are promising for large scale virtual reality applications because of their portability and capability of supporting large workspace haptic interaction. However, it remains a challenge to render multimodal haptic stimuli in handheld devices due to space confinement. In this paper, we present a modular approach to build a Multimodal Handheld Haptic Controller called “Haptic Handshank” that includes a thumb feedback component, a palm feedback component, and a motion tracking component. In the thumb feedback component, a compact pneumatically-driven silicone airbag is utilized to simulate softness, and a flexible membrane based on the electro-vibration principle which covers the top portion of the airbag for rendering virtual textures. In the palm feedback component, vibrational motors and Peltier devices are embedded into the device's body for rendering vibrotactile flow and distributing thermal stimuli. In the motion tracking component, an HTC-Vive tracker is mounted on the bottom of the controller's handle to enable 6-DOF palm motion tracking. The performance of the handheld device is evaluated through quantitative experimental studies, which validate the ability of the device to simulate multimodal haptic sensations in accordance with diverse hand manipulation gestures such as enclosure, static contact, rubbing, squeezing and shaking of a cup of cold drink in 3D virtual space.

We propose a conceptually simple framework for fast COVID-19 screening in 3D chest CT images. The framework can efficiently predict whether or not a CT scan contains pneumonia while simultaneously identifying pneumonia types between COVID-19 and Interstitial Lung Disease (ILD) caused by other viruses. In the proposed method, two 3D-ResNets are coupled together into a single model for the two above-mentioned tasks via a novel prior-attention strategy. We extend residual learning with the proposed prior-attention mechanism and design a new so-called prior-attention residual learning (PARL) block. The model can be easily built by stacking the PARL blocks and trained end-to-end using multi-task losses. More specifically, one 3D-ResNet branch is trained as a binary classifier using lung images with and without pneumonia so that it can highlight the lesion areas within the lungs. Simultaneously, inside the PARL blocks, prior-attention maps are generated from this branch and used to guide another branch to learn more discriminative representations for the pneumonia-type classification. Experimental results demonstrate that the proposed framework can significantly improve the performance of COVID-19 screening. Compared to other methods, it achieves a state-of-the-art result. Moreover, the proposed method can be easily extended to other similar clinical applications such as computer-aided detection and diagnosis of pulmonary nodules in CT images, glaucoma lesions in Retina fundus images, etc.