Due to the perceptual characteristics of the head, vibrotactile Head-mounted Displays are built with low actuator density. Therefore, vibrotactile guidance is mostly assessed by pointing towards objects in the azimuthal plane. When it comes to multisensory interaction in 3D environments, it is also important to convey information about objects in the elevation plane. In this paper, we design and assess a haptic guidance technique for 3D environments. First, we explore the modulation of vibration frequency to indicate the position of objects in the elevation plane. Then, we assessed a vibrotactile HMD made to render the position of objects in a 3D space around the subject by varying both stimulus loci and vibration frequency. Results have shown that frequencies modulated with a quadratic growth function allowed a more accurate, precise, and faster target localization in an active head pointing task. The technique presented high usability and a strong learning effect for a haptic search across different scenarios in an immersive VR setup.

Several studies evaluated vibrotactile stimuli on the head to aid orientation and communication. However, the acuity for vibration of the head's skin still needs to be explored. In this paper, we report the assessment of the spatial resolution on the head. We performed a 2AFC psychophysical experiment systematically varying the distance between pairs of stimuli in a standard-comparison approach. We took into consideration not only the perceptual thresholds but also the reaction times and subjective factors, like workload and vibration pleasantness. Results show that the region around the forehead is not only the most sensitive, with thresholds under 5mm, but it is also the region wherein the spatial discrimination was felt to be easier to perform. We also have found that it is possible to describe acuity on the head for vibrating stimulus as a function of skin type (hairy or glabrous) and of the distance of the stimulated loci from the head midline.

Vibrotactile feedback is broadly used to support different tasks in virtual and augmented reality applications, such as navigation, communication, attentional redirection, or to enhance the sense of presence in virtual environments. Thus, we aim to include the haptic component to the most popular wearable used in VR applications: the VR headset. After studying the acuity around the head for vibrating stimuli, and trying different parameters, actuators, and configurations, we developed a haptic guidance technique to be used in a vibrotactile Head-mounted Display (HMD). Our vi-brotactile HMD was made to render the position of objects in a 3D space around the subject by varying both stimulus loci and vibration frequency. In this demonstration, the participants will interact with different scenarios where the mission is to select a number of predefined objects. However, instead of displaying occlusive graphical information to point to these objects, vibrotactile cues will provide guidance in the VR setup. Participants will see that our haptic guidance technique can be both easy to use and entertaining. (See Video: https://youtu.be/_H0MQy6QD7M).

In this paper, we describe a proof-of-concept for the implementation of a wearable auditory biofeedback system based on a sensor-instrumented insole. Such a system aims to assist everyday users with static and dynamic exercises for gait rehabilitation interventions by providing auditory feedback based on plantar pressure distribution and automated classification of functional gait disorders. As ground reaction force (GRF) data are frequently used in clinical practice to quantitatively describe human motion and have been successfully used for the classification of gait patterns into clinically relevant classes, a feed-forward neural network was implemented on the firmware of the insoles to estimate the GRFs using pressure and acceleration data. The estimated GRFs approximated well the GRF measurements obtained from force plates. To distinguish between physiological gait and gait disorders, we trained and evaluated a support vector machine with labeled data from a publicly accessible dataset. The automated gait classification was then sonified for auditory feedback. The potential of the implemented auditory feedback for preventive and supportive applications in physical therapy was finally assessed with both expert and non-expert participants. A focus group revealed experts’ expectations for the proposed system, while a usability study assessed the clarity of the auditory feedback to everyday users. The evaluation shows promising results regarding the usefulness of our system in this application area.