Head-Computer Interface: A Multimodal Approach to Navigate through Real and Virtual Worlds

Carrino, Francesco; Tscherrig, Julien; Mugellini, Elena; Khaled, Omar Abou; Ingold, Rolf

doi:10.1007/978-3-642-21605-3_25

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…Street-level images enable people to improve their spatial perception or awareness in urban areas [31,32]. The effects of the virtual reality interactive screen images available in widespread areas from Google Street View have affected virtual tourism and geo-gaming [33][34][35].…”

Section: Related Workmentioning

confidence: 99%

Developing a Mobile Mapping System for 3D GIS and Smart City Planning

Yang

2019

Sustainability

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The creation of augmented reality-related geographic information system (GIS) mapping applications has witnessed considerable advances in the technology of urban modeling; however, there are limitations to the technology that is currently used to create similar resources. The cost of the creation of the vehicle is an obstacle, and the rendering of textures of buildings is often lacking because of the distortion caused by the types of lenses that have been used. Generally, mobile mapping systems (MMSs) can extract detailed three-dimensional (3D) data with high quality texture information of the 3D building model. However, mapping urban areas by MMSs is expensive and requires advanced mathematical approaches with complicated steps. In particular, commercial MMS, which generally includes two GPS receivers, is an expensive device, costing ~$1 million. Thus, this research is aimed at developing a new MMS that semi-automatically produces high-quality texture information of 3D building models proposes a 3D urban model by hybrid approaches. Eventually, this study can support urban planners and people to improve their spatial perception and awareness of urban area for Smart City Planning.

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Section: Related Workmentioning

confidence: 99%

Developing a Mobile Mapping System for 3D GIS and Smart City Planning

Yang

2019

Sustainability

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“…The Emotiv EPOC headset was used in many systems to implement smart human-computer interaction systems [10,21,42]. For example, Gomez-Gil et al used the EPOC headset to recognize four types of muscular events: (1) eyes looking to the right while jaw opened, (2) eyes looking to the right while jaw closed, (3) eyes looking to the left while jaw opened, and (4) eyes looking to the left while jaw closed.…”

Section: Emotiv Epoc Eeg Headsetmentioning

confidence: 99%

“…They used these muscular events to steer an agricultural tractor. In [9,10], EPOC was used to develop a system called Virtual Move to navigate through Google Street View (GSV). Their proposed system exploited the EPOC capabilities to detect head movements, facial expressions, thoughts, and emotional states.…”

Section: Emotiv Epoc Eeg Headsetmentioning

confidence: 99%

A Wheelchair Control System Using Human-Machine Interaction: Single-Modal and Multimodal Approaches

Shahin

Tharwat

Gaber

et al. 2017

Journal of Intelligent Systems

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Recent research studies showed that brain-controlled systems/devices are breakthrough technology. Such devices can provide disabled people with the power to control the movement of the wheelchair using different signals (e.g. EEG signals, head movements, and facial expressions). With this technology, disabled people can remotely steer a wheelchair, a computer, or a tablet. This paper introduces a simple, low-cost human-machine interface system to help chaired people to control their wheelchair using several control sources. To achieve this paper’s aim, a laptop was installed on a wheelchair in front of the sitting person, and the 14-electrode Emotiv EPOC headset was used to collect the person’s head impressions from the skull surface. The superficially picked-up signals, containing the brain thoughts, head gestures, and facial emotions, were electrically encoded and then wirelessly sent to a personal computer to be interpreted and then translated into useful control instructions. Using these signals, two wheelchair control modes were proposed: automatic (using single-modal and multimodal approaches) and manual control. The automatic mode controller was accomplished using a software controller (Arduino), whereas a simple hardware controller was used for the manual mode. The proposed solution was designed using wheelchair, Emotiv EPOC EEG headset, Arduino microcontroller, and Processing language. It was then tested by totally chaired volunteers under different levels of trajectories. The results showed that the person’s thoughts can be used to seamlessly control his/her wheelchair and the proposed system can be configured to suit many levels and degrees of disability.

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“…However, its response time is slow. On the other hand, Gomez-Gil et al (2011) have used a sensor called Emotiv EPOC headset, i.e., an EEG sensor, to recognise four fixed trained muscular events to steer a tractor, while Carrino et al (2011) have developed a system, namely 'virtual move', which allows users to navigate through Google street view (GSV) using head movements, facial expressions, thoughts and emotional states detected with the Emotiv sensor.…”

Section: Introductionmentioning

confidence: 99%

Head movement and facial expression-based human-machine interface for controlling an intelligent wheelchair

Rechy-Ramirez

2014

IJBBR

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This paper presents a human machine interface (HMI) for hands-free control of an electric powered wheelchair (EPW) based on head movements and facial expressions detected by using the gyroscope and 'cognitiv suite' of an Emotiv EPOC device, respectively. The proposed HMI provides two control modes: 1) control mode 1 uses four head movements to display in its graphical user interface the control commands that the user wants to execute and one facial expression to confirm its execution; 2) control mode 2 employs two facial expressions for turning and forward motion, and one head movement for stopping the wheelchair. Therefore, both control modes offer hands-free control of the wheelchair. Two subjects have used the two control modes to operate a wheelchair in an indoor environment. Five facial expressions have been tested in order to determine if the users can employ different facial expressions for executing the commands. The experimental results show that the proposed HMI is reliable for operating the wheelchair safely.

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Head-Computer Interface: A Multimodal Approach to Navigate through Real and Virtual Worlds

Cited by 13 publications

References 14 publications

Developing a Mobile Mapping System for 3D GIS and Smart City Planning

Developing a Mobile Mapping System for 3D GIS and Smart City Planning

A Wheelchair Control System Using Human-Machine Interaction: Single-Modal and Multimodal Approaches

Head movement and facial expression-based human-machine interface for controlling an intelligent wheelchair

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