Recent augmented reality (AR) advancements have enabled the development of effective training systems, especially in the medical, rehabilitation, and industrial fields. However, it is unclear from the literature what the intrinsic value of AR to training is and how it differs across multiple application fields. In this work, we gathered and reviewed the prototypes and applications geared towards training the intended user's knowledge, skills, and abilities. Specifically, from IEEE Xplore plus other digital libraries, we collected 64 research papers present in high-impact publications about augmented reality training systems (ARTS). All 64 papers were then categorized according to the training method used, and each paper's evaluations were identified by validity. The summary of the results shows trends in the training methods and evaluations that incorporate ARTS in each field. The narrative synthesis illustrates the different implementations of AR for each of the training methods. In addition, examples of the different evaluation types of the current ARTS are described for each of the aforementioned training methods. We also investigated the different training strategies used by the prevailing ARTS. The insights gleaned from this review can suggest standards for designing ARTS regarding training strategy, and recommendations are provided for the implementation and evaluation of future ARTS.

Abnormal behaviour can be an indicator for a medical condition in older adults. Our novel unsupervised statistical concept drift detection approach uses variational autoencoders for estimating the parameters for a statistical hypothesis test for abnormal days. As feature, the Kullback–Leibler divergence of activity probability maps derived from power and motion sensors were used. We showed the general feasibility (min. F₁-Score of 91 %) on an artificial dataset of four concept drift types. Then we applied our new method to our real–world dataset collected from the homes of 20 (pre–)frail older adults (avg. age 84.75 y). Our method was able to find abnormal days when a participant suffered from severe medical condition.

A gentle touch is an essential part of human interaction that produces a positive care effect. Previously, robotics studies have shown that robots can reproduce a gentle touch that elicits similar, positive emotional responses in humans. However, whether the positive emotional effects of a robot’s touch combined with speech can be enhanced using a multimodal approach remains unclear. This study supports the hypothesis that a multimodal interaction combining gentle touch and speech by a robot enhances positive emotional responses. Here, we conducted an experiment using a robotic arm to perform a gentle touch combined with speech and compared three conditions: touch alone, speech alone, and touch with speech. We assessed participants’ subjective ratings of valence, arousal, and human likeliness using subjective emotional responses. Furthermore, we recorded facial electromyography (EMG) from the corrugator supercilii and zygomaticus major muscles and measured skin conductance levels (SCLs) as physiological emotional responses. Our results show that touch combined with speech elicited higher subjective valence and arousal ratings, stronger zygomaticus major EMG and SCL activities than touch alone. The results suggest that the positive emotional effects of robotic touch can be boosted by combining elements of speech.

Lenslet array (LA) near-eye displays (NEDs) are a recent technical development that creates a virtual image in the field of view of one or both eyes. A problem occurs when the user’s pupil moves out of the LA-NED eye box (i.e., cross-talk) making the image look doubled or ghosted. It negatively impacts the user experience. Although eye-gaze tracking can mitigate this problem, the effect of the solution has not been studied to understand the impact of pupil size and human perception. In this paper, we redefine the cross-talk region as the practical pupil movable region (PPMR₅₀), which differs from eye box size because it considers pupil size and human visual perception. To evaluate the effect of eye-gaze tracking on subjective image quality, three user studies were conducted. From the results, PPMR₅₀ was found to be consistent with human perception, and cross-talk elimination via eye-gaze tracking was better understood in a static gaze scenario. Although the system latency prevented the complete elimination of cross-talk for fast movements or large pupil changes, the problem was greatly alleviated. We also analyzed system delays based on PPMR₅₀, which we newly defined in this paper and provided an optimization scheme to meet the maximum eyeball rotation speed.

Individuals with social anxiety often experience heightened anxiety during face-to-face conversations due to their fear of negative judgments when perceiving neutral facial expressions from others. This research aims to alleviate this anxiety by introducing a novel approach that involves overlaying visual effects onto the conversation partner via an augmented reality head-mounted display. We developed an AR application for HoloLens 2, allowing users to overlay either an anime-style avatar or a smiling face photo during in-person interactions. We conducted a user study where participants engaged in dyadic conversations using our AR application. 29 participants compared three conditions: control, anime-style avatar, and smiling face photo. The findings reveal two significant outcomes: (1) overlaying an anime-style avatar onto the conversation partner enhances conversational comfort, and (2) individuals with pronounced social interaction anxiety and intense fear of negative evaluation benefit from our AR-based system. This research presents possibilities for practical solutions that could improve the well-being of individuals with social anxiety during in-person conversations.