Navigating Virtual Environments Using Leg Poses and Smartphone Sensors

Tsaramirsis, Georgios; Buhari, Seyed M.; Basheri, Mohammed; Stojmenović, Miloš

doi:10.3390/s19020299

Cited by 19 publications

(10 citation statements)

References 23 publications

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“…We applied our framework to our remote lab, the design of which is further described (Randhawa et al, 2017;; Randhawa and Shanthagiri, 2015;Braiek et al, 2008;Randhawa et al, 2020 (b); Tsaramirsis et al, 2019).…”

Section: Our Remote Labmentioning

confidence: 99%

A Robust Risk Model to Identify Factors that Affect Students’ Critical Achievement in Remote Lab Courses

Georgakopoulos¹,

Piromalis²,

Panagiotis³

et al. 2022

IJEBA

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Purpose:The research objective is to address the problem of students' critical achievement in remote lab courses aiming at identifying the risk factors of students' failure by exploiting e-learning learning analytics. A robust risk model is developed to serve this purpose. The research is orented into NI-Elvis remote lab courses given that they offer a stable environment in the context of which automation lab courses could be effectively delivered. Design/methodology/approach: A robust risk model was developed by analyzing students' behavioral engagement data. The e-learning part of the remote lab course was used to provide the requisite dataset. In detail, a proper binary logistics regression scheme was employed to come up with the aforementioned risk model. The e-learning part was implemented by a specific e-learning platform that is suitable for NI-Elvis remote lab courses. The data was collected after the course completion. Findings: Factors that are related to students' engagement (number of theory exercises completed and number of messages sent) appeared to be decisive. Originality/value: The originality of our research lies in the fact that the issue of students' critical achievement in remote lab courses is not addressed in a fragmentary way by just carrying out a specific analysis and coming up with results, like many similar studies in the literature. Thereby, a concrete methodology was developed on the basis of an established generic risk management framework. The added value of our research is centered on the fact that our risk model could potentially be applied to any remote lab course to come up with the respective risk factors.

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Section: Our Remote Labmentioning

confidence: 99%

A Robust Risk Model to Identify Factors that Affect Students’ Critical Achievement in Remote Lab Courses

Georgakopoulos¹,

Piromalis²,

Panagiotis³

et al. 2022

IJEBA

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show abstract

“…Virtual reality (VR) is an artificially generated alternative reality, which primarily utilizes 3D images and sound and may use additional input and output devices. [112] VR attempts to convince the person the artificial reality is the actual reality with the user interacting with elements in a "virtual world" which is invisible to others except to the user herself.…”

Section: Robots and Dronesmentioning

confidence: 99%

“…The idea is to simulate the actual vehicle or equipment usage in a virtual world, allowing users to experience nearly exact situations without its dangers. These types of VR simulations require specialized equipment that sometimes have comparable size to the actual vehicles they mimic [112].…”

Section: Robots and Dronesmentioning

confidence: 99%

A Modern Approach towards an Industry 4.0 Model: From Driving Technologies to Management

et al. 2022

Self Cite

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Every so often, a confluence of novel technologies emerges that radically transforms every aspect of the industry, the global economy, and finally, the way we live. These sharp leaps of human ingenuity are known as industrial revolutions, and we are currently in the midst of the fourth such revolution, coined Industry 4.0 by the World Economic Forum. Building on their guideline set of technologies that encompass Industry 4.0, we present a full set of pillar technologies on which Industry 4.0 project portfolio management rests as well as the foundation technologies that support these pillars. A complete model of an Industry 4.0 factory which relies on these pillar technologies is presented. The full set of pillars encompasses cyberphysical systems and Internet of Things (IoT), artificial intelligence (AI), machine learning (ML) and big data, robots and drones, cloud computing, 5G and 6G networks, 3D printing, virtual and augmented reality, and blockchain technology. These technologies are based on a set of foundation technologies which include advances in computing, nanotechnology, biotechnology, materials, energy, and finally cube satellites. We illustrate the confluence of all these technologies in a single model factory. This new factory model succinctly demonstrates the advancements in manufacturing introduced by these modern technologies, which qualifies this as a seminal industrial revolutionary event in human history.

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“…Moreover, many state-of-the-art smartphones also have the proximity sensor, the ambient light sensor, and the temperature sensor. 16 In MobiScan, the orientation sensor and the accelerator sensor are utilized to estimate the gesture of the smartphone while shooting the video. MobiScan can correct the video quickly and accurately if the system knows the camera's shooting gesture.…”

Section: Sensors In Smartphonementioning

confidence: 99%

“…The orientation sensor can be aware of smartphone's gestures. Moreover, many state‐of‐the‐art smartphones also have the proximity sensor, the ambient light sensor, and the temperature sensor 16 . In MobiScan, the orientation sensor and the accelerator sensor are utilized to estimate the gesture of the smartphone while shooting the video.…”

Section: Introductionmentioning

confidence: 99%

MobiScan: An enhanced invisible screen‐camera communication system for IoT applications

Xiao

Liu

et al. 2020

Trans Emerging Tel Tech

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In recent years, dynamic and invisible screen‐camera short‐range communication for IoT devices have become a popular research field due to its superior user experience and no‐extra hardware required. However, most existing screen‐camera communication systems suffer from poor data security, high computational overhead, and limited capture angle, which make them infeasible in practice. In this work, we propose MobiScan, a dynamic and invisible screen‐to‐camera communication system that is able to ensure data security, real‐time communication, and flexible capture angle. MobiScan is composed of two parts: a novel fast frame correction scheme and a multilevel data pattern scheme to address the above problems. The fast frame correction scheme proposes a novel frame correct approach that utilizes the sensor data on the smartphone to correct the captured frame by reproducing the relationship between positions of the screen and the smartphone. Through this method, the scheme can save frame correction time without calculating frame content to identify the degree of deformation. The multilevel data pattern scheme protects the data privacy by sending the classified information to targeted users, which can reduce the time overhead and extend the application scenario by redesigning the data structure. The experimental results show that for flexible capture angle problem, the maximum capture angle is 180° in the rotate view and 60° in the side view, bottom view, and overlook view. For real‐time communication problem, the time overhead is reduced by 90% in the procedure of the frame correction. In the data decoding process, the time overhead is reduced by 10%. MobiScan enables a flexible capture angle on the PC platform for secure and real‐time communication.

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Navigating Virtual Environments Using Leg Poses and Smartphone Sensors

Cited by 19 publications

References 23 publications

A Robust Risk Model to Identify Factors that Affect Students’ Critical Achievement in Remote Lab Courses

A Robust Risk Model to Identify Factors that Affect Students’ Critical Achievement in Remote Lab Courses

A Modern Approach towards an Industry 4.0 Model: From Driving Technologies to Management

MobiScan: An enhanced invisible screen‐camera communication system for IoT applications

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