Uasisi: a modular and adaptable wearable system to assist the visually impaired

García-Macías, J. Antonio; Ramos, Alberto G.; Hasimoto-Beltrán, Rogelio; Hernández, Saúl E. Pomares

doi:10.1016/j.procs.2019.04.058

Cited by 18 publications

(14 citation statements)

References 8 publications

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“…Typically, short distances allow high interactions, while long distances allow little to no interaction. For example, in [9,10,13,16,21,23,27,34], the distance is used as a parameter to assign a proxemic zone that allows the users to interact with the display or devices in different proximities. The interaction zones are also used for adapting visualizations on displays based on the users' distance relative to the screen, such as the studies presented in [4,19,32].…”

Section: Applications Based On Proxemic Interactionsmentioning

confidence: 99%

“…Location describes qualitative aspects of the space, where there is interaction among fixed entities (e.g., room layout, doors) or semi-fixed entities (e.g., furniture positions) [13,22]. Researchers have also considered the location for obtaining the user's current positions.…”

Section: Applications Based On Proxemic Interactionsmentioning

confidence: 99%

“…We have briefly described studies that have implemented multiple proxemic applications based on DILMO. From this review, we want to highlight that the majority of these proposals manage only partial proxemic dimensions; few of them have used the five DILMO proxemic dimensions [6,13,14,20,22,23]. In all of these applications, the interaction objects (i.e., people and devices) are considered as entities; when the Identity dimension is implemented, these entities are explicitly identified in a particular role.…”

Section: Applications Based On Proxemic Interactionsmentioning

confidence: 99%

“…The work presented in [11] detects the Distance and Orientation of a user with respect to a single display to generate multiple views of the information displayed. Similarly, the applications described in [13,21] help visually impaired people to explore paintings based on Distance, Movement, and Location. Thus, we conclude that depending on the application, all DILMO dimensions are not required and specific combination of them can determine different proxemic environments.…”

Section: Applications Based On Proxemic Interactionsmentioning

confidence: 99%

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Mobile proxemic application development for smart environments

Pérez

Roose

Cardinale

et al. 2020

Proceedings of the 18th International Conference on Advances in Mobile Computing &Amp; Multimedia

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Currently, mobile technologies are present in our daily day in different activities and their use keeps increasing. In this context, proxemic interaction, derived from proxemic theory, is becoming an influential approach to implement Mobile Human-Computer Interaction (MobileHCI) in smart environments, based on five proxemic dimensions: Distance, Identity, Location, Movement, and Orientation (DILMO). The existing tools for implementing proxemic applications require fixed devices that make it difficult to built mobile proxemic apps. This work aims to propose a framework for the development of proxemic applications for smart environments comprised by entities, whose interactions are supported by MobileHCI defined in terms of DILMO dimensions. Our proposed framework allows defining and managing all components in a smart proxemic environment. The framework also provides an API, that allows developers to simplify the process of proxemic information sensing (i.e., detection of DILMO dimensions) from mobile phones and wearable sensors. We demonstrate and evaluate the effectiveness and suitability of our framework, through the proof-of-concept which describes the implementation of two proxemic mobile applications built in a context-based infrastructure for smart proxemic environments based on mobile devices.

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Section: Applications Based On Proxemic Interactionsmentioning

confidence: 99%

Section: Applications Based On Proxemic Interactionsmentioning

confidence: 99%

Section: Applications Based On Proxemic Interactionsmentioning

confidence: 99%

Section: Applications Based On Proxemic Interactionsmentioning

confidence: 99%

See 2 more Smart Citations

Mobile proxemic application development for smart environments

Pérez

Roose

Cardinale

et al. 2020

Proceedings of the 18th International Conference on Advances in Mobile Computing &Amp; Multimedia

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show abstract

“…Up to now, there exists some vision-based assistive systems, which aim to improve perception experience of VI [10,12,5]. In particular, OrCAM [22] equipped with monocular camera is used to provide some special object information to VI, such as reading text and face recognition, etc.…”

Section: Visual Assistive Systemsmentioning

confidence: 99%

Deep Learning Based Wearable Assistive System for Visually Impaired People

Lin¹,

Wang²,

Yi³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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In this paper, we propose a deep learning based assistive system to improve the environment perception experience of visually impaired (VI). The system is composed of a wearable terminal equipped with an RGBD camera and an earphone, a powerful processor mainly for deep learning inferences and a smart phone for touch-based interaction. A data-driven learning approach is proposed to predict safe and reliable walkable instructions using RGBD data and the established semantic map. This map is also used to help VI understand their 3D surrounding objects and layout through well-designed touchscreen interactions. The quantitative and qualitative experimental results show that our learning based obstacle avoidance approach achieves excellent results in both indoor and outdoor datasets with lowlying obstacles. Meanwhile, user studies have also been carried out in various scenarios and showed the improvement of VI's environment perception experience with our system.

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