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Over the past decade, enhanced computing capabilities and mobile technologies have begotten the upsurge of innovative mobile health (mHealth) solutions, and many research efforts have occurred recently in the area of technology-based interventions (TBI) for autism spectrum disorders (ASD). Mobile augmented reality (MAR) refers to AR systems that use the handheld mobile device medium (mobile phones, tablets or smart glasses). This article reports the results of a systematic review undertaken on the use of MAR for ASD-related skills learning from the year 2010. It aims to provide an insight into the current state of research on MAR interventions and to provide guidance to relevant designers and researchers. We searched seven databases and retrieved 625 articles initially. After exclusion and screening, 36 articles were reviewed reporting on using MAR to improve various skills of children and adolescents with ASD, and 10 research questions related to PICO (P: Population, I: Intervention, C: Comparison, O: Outcomes) were addressed. This study identifies challenges that still exist in the research efforts towards the development of applications exploiting the MAR for ASD interventions: technology issues, research design consideration, subjective assessment etc. The studies examined suggest researchers should focus on users and improve the quality of the MAR app. In addition, more effective research methods and evaluation methods could be involved in future studies to facilitate the development of MAR intervention applications. [ABSTRACT FROM AUTHOR]

In marker-based augmented reality (AR) systems, markers are usually relatively independent and predefined by the system creator in advance. Users can only use these predefined markers to complete the construction of certain specified content. Such systems usually lack flexibility and cannot allow users to create content freely. In this paper, we propose a virtual marker technique to build a marker-based AR system framework, where multiple AR markers including virtual and physical markers work together. Information from multiple markers can be merged, and virtual markers are used to provide user-defined information. We conducted a pilot study to understand the multi-marker cooperation framework based on virtual markers. The pilot study shows that the virtual marker technique will not significantly increase the user's time and operational burdens, while actively improving the user's cognitive experience. [ABSTRACT FROM AUTHOR]

Since electronic components are constantly getting smaller and smaller, sensors and logic boards can be fitted into smaller enclosures. This miniaturization lead to the development of smart rings containing motion sensors. These sensors of smart rings can be used to recognize hand/finger gestures enabling natural interaction. Unlike vision-based systems, wearable systems do not require a special infrastructure to operate in. Smart rings are highly mobile and are able to communicate wirelessly with various devices. They could potentially be used as a touchless user interface for countless applications, possibly leading to new developments in many areas of computer science and human–computer interaction. Specifically, the accelerometer and gyroscope sensors of a custom-built smart ring and of a smartwatch are used to train multiple machine learning models. The accuracy of the models is compared to evaluate whether smart rings or smartwatches are better suited for gesture recognition tasks. All the real-time data processing to predict 12 different gesture classes is done on a smartphone, which communicates wirelessly with the smart ring and the smartwatch. The system achieves accuracy scores of up to 98.8%, utilizing different machine learning models. Each machine learning model is trained with multiple different feature vectors in order to find optimal features for the gesture recognition task. A minimum accuracy threshold of 92% was derived from related research, to prove that the proposed system is able to compete with state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

Augmented reality (AR)-based surgical navigation may offer new possibilities for safe and accurate surgical execution of complex osteotomies. In this study we investigated the feasibility of navigating the periacetabular osteotomy of Ganz (PAO), known as one of the most complex orthopedic interventions, on two cadaveric pelves under realistic operating room conditions. Preoperative planning was conducted on computed tomography (CT)-reconstructed 3D models using an in-house developed software, which allowed creating cutting plane objects for planning of the osteotomies and reorientation of the acetabular fragment. An AR application was developed comprising point-based registration, motion compensation and guidance for osteotomies as well as fragment reorientation. Navigation accuracy was evaluated on CT-reconstructed 3D models, resulting in an error of 10.8 mm for osteotomy starting points and 5.4° for osteotomy directions. The reorientation errors were 6.7°, 7.0° and 0.9° for the x-, y- and z-axis, respectively. Average postoperative error of LCE angle was 4.5°. Our study demonstrated that the AR-based execution of complex osteotomies is feasible. Fragment realignment navigation needs further improvement, although it is more accurate than the state of the art in PAO surgery. [ABSTRACT FROM AUTHOR]

Despite the convenience offered by e-commerce, online apparel shopping presents various product-related risks, as consumers can neither physically see nor try products on themselves. Augmented reality (AR) and virtual reality (VR) technologies have been used to improve the shopping online experience. Therefore, we propose an AR- and VR-based try-on system that provides users a novel shopping experience where they can view garments fitted onto their personalized virtual body. Recorded personalized motions are used to allow users to dynamically interact with their dressed virtual body in AR. We conducted two user studies to compare the different roles of VR- and AR-based try-ons and validate the impact of personalized motions on the virtual try-on experience. In the first user study, the mobile application with the AR- and VR-based try-on is compared to a traditional e-commerce interface. In the second user study, personalized avatars with pre-defined motion and personalized motion is compared to a personalized no-motion avatar with AR-based try-on. The result shows that AR- and VR-based try-ons can positively influence the shopping experience, compared with the traditional e-commerce interface. Overall, AR-based try-on provides a better and more realistic garment visualization than VR-based try-on. In addition, we found that personalized motions do not directly affect the user's shopping experience. [ABSTRACT FROM AUTHOR]

Biometric authentication is popular in authentication systems, and gesture as a carrier of behavior characteristics has the advantages of being difficult to imitate and containing abundant information. This research aims to use three-dimensional (3D) depth information of gesture movement to perform authentication with less user effort. We propose an approach based on depth cameras, which satisfies three requirements: Can authenticate from a single, customized gesture; achieves high accuracy without an excessive number of gestures for training; and continues learning the gesture during use of the system. To satisfy these requirements respectively: We use a sparse autoencoder to memorize the single gesture; we employ data augmentation technology to solve the problem of insufficient data; and we use incremental learning technology for allowing the system to memorize the gesture incrementally over time. An experiment has been performed on different gestures in different user situations that demonstrates the accuracy of one-class classification (OCC), and proves the effectiveness and reliability of the approach. Gesture authentication based on 3D depth cameras could be achieved with reduced user effort. [ABSTRACT FROM AUTHOR]