The development of novel computer interfaces has led to the possibility of integrating inputs from multiple individuals into a single avatar, fostering collaboration by combining skills and sharing the cognitive load. However, the collaboration dynamic and its effectiveness may vary depending on the individuals involved. Particularly in scenarios where two individuals remotely control a robotic avatar without the possibility of direct communication, understanding each other’s characteristics can result in enhanced performance. To achieve this, it is essential to ascertain if individuals can discern their partner’s characteristics within the merged embodiment. This paper investigates the accuracy with which participants can distinguish between two different collaborating partners (one attempting to lead and one attempting to follow) when sharing control of a robot arm during a block pick-and-place task. The results suggested that participants who changed their roles according to the different roles of the two partners achieved the highest discrimination rates. Furthermore, participants changed their movements through the trials, adapting their actions to their preferred approach. This research provides insights into the factors determining individuals’ ability to understand partner characteristics during control of collaborative avatars.

Dementia describes a syndrome of cognitive degeneration, and Behavioural and Psychological Symptoms of Dementia (BPSD) is the non-cognitive symptom. BPSD can be improved by care services. To aid better care service, we explore the potential of using Augmented Reality (AR) to support dementia education for medical workers in three steps: (1) We explore medical workers’ perspective on dementia care lived experience and XR, (2) we co-design an educational experience containing an AR-based application and a 5-min activity with medical workers, (3) we evaluate the effectiveness of the system through a mixed method study. Our result shows that the AR experience successfully touches participants, and motivates them to reflect on the provision of care service. On this basis, we discuss the elements and challenges of designing XR-enabled dementia education for users unfamiliar with novel technology, and the potential of using XR in clinical education.

This paper presents an approach for coupling robotic capability with human ability in dynamic motor skills, called "Human-Machine Mutual Actuation (HMMA)." We focus specifically on throwing motions and propose a method to control the release timing computationally. A system we developed achieves our concept, HMMA, by a robotic handheld device that acts as a release controller. We conducted user studies to validate the feasibility of the concept and clarify related technical issues to be tackled. We recognized that the system successfully performs on throwing according to the target while it exploits human ability. These empirical experiments suggest that robotic capability can be embedded into the users' motions without losing their senses of control. Throughout the user study, we also revealed several issues to be tackled in further research contributing to HMMA.

To efficiently edit first-person videos, manually highlighting important scenes while recording is helpful. However, little study has been performed on how such annotation contributes to video editing and affects user behavior during recording. To elicit fundamental requirements for designing useful record-time annotation techniques, we conducted a study using a set of prototype wearable camera system and a video editing interface that enables users to annotate scenes during recording. We asked participants to perform video recording and editing tasks with two different interface settings. We observed that the participants edited videos more efficiently with detailed annotation techniques, whereas focussing on annotating scenes affected their record-time behavior. We conclude the paper with the design guidelines developed from the findings.

We present Reality Jockey, a system that confuses the participant's perception of the reality by mixing in a recorded past-reality. The participant will be immersed in a spatialized 3D sound environment that is a mix of sounds from the reality and from the past. The sound environment from the past is augmented with haptic feedback in cross-modality. The haptic feedback is associated with certain sounds such as the vibration in the table when stuff is placed on the table to make the illusion of it happening in live. The seamless transition between live and past creates immersive experience of past events. The blending of live and past allows interactivity. To validate our system, we conducted user studies on 1) does blending live sensations improve such experiences, and 2) how beneficial is it to provide haptic feedbacks in recorded pasts. Potential applications are suggested to illustrate the significance of Reality Jockey.