Communication in collaboration, especially synchronous, remote communication, is crucial to the success of task-specific goals. Insufficient or excessive forms of communication may lead to detrimental effects on task performance while increasing mental fatigue. However, identifying which combinations of communication modalities provide the most efficient transfer of information in collaborative settings will greatly improve collaboration. To investigate this, we developed a remote, synchronous, asymmetric VR collaborative assembly task application, where users play the role of either mentor or mentee, and were exposed to different combinations of three communication modalities: voice, gestures, and gaze. Through task-based experiments with 25 pairs of participants (50 individuals), we evaluated quantitative and qualitative data and found that gaze did not differ significantly from multiple combinations of communication modalities. Our qualitative results indicate that mentees experienced more difficulty and frustration in completing tasks than mentors, with both types of users preferring all three modalities to be present.

Chronic lower back pain due to improper lifting techniques poses a major workplace safety hazard. The major risk factors for improper loading posture (ILP) include overloading, and improper loading of the lumbar muscles, ligaments, and vertebrae due to repetitive mechanical stresses exerted upon them. The current intervention technology relies on the users’ intent and willingness to self-correct ILP through alert-based feedback or involves wearing bulky lift assist devices to prevent ILP. We address these issues with a physiological feedback system that utilizes IMU sensors for ILP detection and Electrical Muscle Stimulation (EMS) for automatic dynamic ILP correction for restoring ideal lifting angles for torso inclination and knee bend. In a user study involving 36 participants, our automatic approach delivered significantly faster correction and outperformed alternative feedback mechanisms (Audio and Vibro-tactile) and was perceived to be interesting, comfortable and a potential commercial product.

We present a taxonomy of human-centered collaborative XR tasks. XR technologies have extended into the realm of collaboration, improving the quality and accessibility of teamwork. However, after a comprehensive assessment of the literature on the interaction between XR technologies and collaboration, no comprehensive method that emphasizes task actions and properties exists to classify collaborative tasks. Thus, our suggested taxonomy represents a classification system for collaborative tasks. After conducting a thorough literature review across different research venues, we conducted several exhaustive classification and review cycles for over 800 papers collected, which resulted in 148 papers retained to create the taxonomy. We dissected the actions and properties that the collaborative endeavors and tasks of these papers encompass as well as the types of categorizations and relations these papers illustrate. We expand on the design choices and usage of our taxonomy, followed by its limitations and future work. We built this taxonomy in order to reduce ambiguities and confusion regarding the design and comprehension of human-based collaborative tasks that use XR technology, which could prove useful in aiding the development and understanding of these tasks. Our taxonomy reveals a framework for understanding how collaborative tasks are designed and a systematic way of classifying different methods by which people can collaborate and interact in environments that involve XR, while still promoting efficient communication, teamwork, goal achievement and productivity.

Standard selection techniques such as ray casting fail when virtual objects are partially or fully occluded. In this paper, we present two novel approaches that combine cone-casting, world-in-miniature, and grasping metaphors to disocclude objects in the representation local to the user. Through a within-subject study where we compared 4 selection techniques across 3 levels of object occlusion, we found that our techniques outperformed an alternative one that also focuses on maintaining the spatial relationships between objects. We discuss application scenarios and future research directions for these types of selection techniques.