Smart advisor: Real-time information provider with mobile augmented reality

Chae, Seungho; Yang, Yoonsik; Choi, Heeseung; Kim, Ig-Jae; Byun, Jung-Hyun; Jo, Jiyoon; Han, Tack-Don

doi:10.1109/icce.2016.7430535

Cited by 5 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…List of Papers Total AR [16], [17], [19], [22], [23], [26], [27], [38], [41], [48], [49], [53] 12 IoT [12], [40] 2 AR + IoT [11], [13], [14], [15], [18], [20], [24], [25], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [39], [42], [43], [44], [45], [46], [47], [50], [51], [52] 28…”

Section: Use Of Titlementioning

confidence: 99%

“…List of Papers Total IEEE Xplore Digital Library [11], [13], [14], [15], [17], [19], [21], [22], [25], [28], [29], [32], [33], [34], [35], [38], [39], [40], [41], [44], [45], [46], [48], [49], [50], [51], [52] Publication Database List of Papers Total ACM Digital Library [16], [18], [20], [23], [26], [27], [42], [53] 8 SpringerLink [12], [24], [30], [31], [36], [37] 6 MDPI [43], [47] 2…”

Section: Publication Databasementioning

confidence: 99%

“…[18] 1 Social Life [14], [16], [17], [19], [20], [22], [23], [24], [26], [28], [29], [30], [31], [33], [35], [37], [39], [40], [42], [46], [48], [50], [51], [53] 24…”

Section: Fieldsunclassified

See 2 more Smart Citations

Augmented Reality in the Internet of Things (AR + IoT): A Review

Syahidi

Arai

Tolle

et al. 2021

ijics

View full text Add to dashboard Cite

Augmented Reality (AR) and Internet of Things (IoT) devices can converge, we call it AR + IoT. The integration of AR and IoT has promising opportunities for future product research and development. IoT is a tool for communicating, monitoring, and remotely controlling with connected products, while AR is a means to see and find out what is happening with connected products based on visualization/interface based on additional information. The mapping review method was used to carry out this research. The result is that as many as 43 papers from 2014-2020 are the main targets of review. The largest publication database is in the IEEE Xplore Digital Library, the application dominates in the field of social life, the research objectives are varied, and the recommended future work is to maximize the potential for integration between AR and IoT or even with other promising devices. Then comfort, convenience, and user experience are also recommended in implementing AR + IoT

show abstract

Section: Use Of Titlementioning

confidence: 99%

Section: Publication Databasementioning

confidence: 99%

See 1 more Smart Citation

Augmented Reality in the Internet of Things (AR + IoT): A Review

Syahidi

Arai

Tolle

et al. 2021

ijics

View full text Add to dashboard Cite

show abstract

“…A mobile AR system is relatively easy to develop and can provide a friendly user interface (UI) because it uses mobile devices. For example, applications have been developed that employ a mobile device's camera and provide related information through the device's display when the user uses a medicine case [17,18]. Developed for mobile AR games for health promotion through the user-friendly mobile devices [19].…”

Section: Related Workmentioning

confidence: 99%

Deep-cARe: Projection-Based Home Care Augmented Reality System with Deep Learning for Elderly

Park

Lee

et al. 2019

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Developing innovative and pervasive smart technologies that provide medical support and improve the welfare of the elderly has become increasingly important as populations age. Elderly people frequently experience incidents of discomfort in their daily lives, including the deterioration of cognitive and memory abilities. To provide auxiliary functions and ensure the safety of the elderly in daily living situations, we propose a projection-based augmented reality (PAR) system equipped with a deep-learning module. In this study, we propose three-dimensional space reconstruction of a pervasive PAR space for the elderly. In addition, we propose the application of a deep-learning module to lay the foundation for contextual awareness. Performance experiments were conducted for grafting the deep-learning framework (pose estimation, face recognition, and object detection) onto the PAR technology through the proposed hardware for verification of execution possibility, real-time execution, and applicability. The precision of the face pose is particularly high by pose estimation; it is used to determine an abnormal user state. For face recognition results of whole class, the average detection rate (DR) was 74.84% and the precision was 78.72%. However, for face occlusions, the average DR was 46.83%. It was confirmed that the face recognition can be performed properly if the face occlusion situation is not frequent. By object detection experiment results, the DR increased as the distance from the system decreased for a small object. For a large object, the miss rate increased when the distance between the object and the system decreased. Scenarios for supporting the elderly, who experience degradation in movement and cognitive functions, were designed and realized, constructed using the proposed platform. In addition, several user interfaces (UI) were implemented according to the scenarios regardless of distance between users and the proposed system. In this study, we developed a bidirectional PAR system that provides the relevant information by understanding the user environment and action intentions instead of a unidirectional PAR system for simple information provision. We present a discussion of the possibility of care systems for the elderly through the fusion of PAR and deep-learning frameworks. Appl. Sci. 2019, 9, 3897 2 of 22 were usually attended to by their families; however, they are currently forced to lead independent lives [2]. There has been increasing interest in social welfare and medical support owing to the increasing aging rate. The social government is increasing social welfare and medical benefits to support elderly self-sustenance; however, most elderly people attend to various problems in their daily life on them own. Moreover, various diseases as well as physical and mental deterioration can occur with aging [3]. Muscle weakness is a representative example of physical deterioration. Muscle strength of people in their 60's decreases to approximately one half of that in their 20s, resulting in sign...

show abstract

“…In this way, the user can efficiently validate the CAP with highly customized test cases. Moreover, inspired by the prior works that immerse users in Extended Reality (XR) to facilitate the interpretation of spacesensitive contexts [16,35,92], Fast-Forward Reality adopts an XR interface that delivers each test case by overlaying (e.g., smart object states), augmenting (e.g., time), and placing (e.g., human action) corresponding context visualizations in-situ in either the physical environment or the virtual replica. The user can intuitively evaluate whether the CAP performs correctly as if experiencing it in real life.…”

Section: Introductionmentioning

confidence: 99%

Fast-Forward Reality: Authoring Error-Free Context-Aware Policies with Real-Time Unit Tests in Extended Reality

Qian,

Wang,

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Meta Reality Labs Research (a) (b) (c) (d) Figure 1: Overview of the Fast-Forward Reality workflow: (a) An end-user initiates an authoring session of a context-aware policy (CAP) while being immersed in an Extended Reality (XR) authoring environment. The user includes context instances into the CAP by selecting the corresponding XR icons. (b) The system adaptively generates multiple unit test cases based on the context history of the user's everyday routines and the CAP, and highlights them with XR visualizations. (c) The user acts out the suggested test cases by in-situ actions and selections of the involved context instances to intuitively validate whether the CAP reacts as expected under each test case. (d) The user refines the CAP after noticing potential improvements and repeats the validation until the CAP is error-free under all test cases.

show abstract

Smart advisor: Real-time information provider with mobile augmented reality

Cited by 5 publications

References 7 publications

Augmented Reality in the Internet of Things (AR + IoT): A Review

Augmented Reality in the Internet of Things (AR + IoT): A Review

Deep-cARe: Projection-Based Home Care Augmented Reality System with Deep Learning for Elderly

Fast-Forward Reality: Authoring Error-Free Context-Aware Policies with Real-Time Unit Tests in Extended Reality

Contact Info

Product

Resources

About