Video recording is one of the most commonly used techniques for reflection, because video allows people to know what they look like to others and how they could improve their performance, but it is problematic because some people easily fall into negative emotions and worry about their performance, resulting in a low benefit. In this study, the possibility of applying a simple VR-based reflection method was explored. This method uses virtual reality (VR) and a head-mounted display (HMD) to allow presenters to watch their own presentations from the audience’s perspective and uses an avatar, which hides personal appearance, which has low relevance to the quality of presentation, to help reduce self-awareness during reflection. An experimental study was carried out, considering four personal characteristics—gender, personal anxiety, personal confidence and self-bias. The goal of this study is to discuss which populations can benefit more from this system and to assess the impact of the avatar and HMD-based VR. According to the results, the individuals with low self-confidence in their public speaking skills could benefit more on self-evaluation from VR reflection with HMD, while individuals with negative self-bias could reduce more anxiety by using an avatar.

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.

This paper describes a localization method with wearable electromagnetic sensor and orientation sensor for wearable computer users. Many user localization methods have been investigated to realize location-based services in a wide environment. The localization methods usually employ a hybrid approach in which user's position is estimated by using positioning infrastructures and dead reckoning such as a pedometer. However, the installation cost of infrastructures increases when the area expands, and the error of the pedometer is frequently caused by failures in walking locomotion detection and the difference between ideal and estimated step lengths. If the relative distance is accurately estimated by dead reckoning approach, the installation cost of infrastructures can be reduced. This paper proposes a new localization method that improves the estimation accuracy of step length in dead reckoning approach. The proposed method measures user's orientation and geometrical relationship between user's heel and waist with an orientation sensor and an electromagnetic tracker attached to user's body. When both feet come into contact with the ground, user's position is updated by adding the estimated step length which means the relationship between user's heels and the position estimated in the previous step. The proposed localization method has been experimented using a prototype system to evaluate the accuracy of the proposed method.