Advanced driver monitoring for assistance system (ADMAS)

Izquierdo-Reyes, Javier; Ramírez-Mendoza, Ricardo A.; Bustamante-Bello, Rogelio; Navarro-Tuch, Sergio A.; Avila-Vázquez, Roberto

doi:10.1007/s12008-016-0349-9

Cited by 28 publications

(10 citation statements)

References 28 publications

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“…The company iMotions for example has successfully developed a commercial platform for monitoring physiological and psychological parameters of users while experiencing VR. This is a great example of how affect recognition can be used to add value to VR applications [18,20].…”

Section: Eeg As An Input Modality For Emotion Recognitionmentioning

confidence: 99%

Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset

Menezes

Samara

Galway

et al. 2017

Pers Ubiquit Comput

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One of the challenges in virtual environments is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the user's emotions. This paper investigates features extracted from electroencephalogram signals for the purpose of affective state modelling based on Russell's Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect to enhance interaction experience in virtual environments. The DEAP dataset was used within this work, along with a Support Vector Machine and Random Forest, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements and band power from the α, β, δ, and θ waves and High Order Crossing of the EEG signal.

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Section: Eeg As An Input Modality For Emotion Recognitionmentioning

confidence: 99%

Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset

Menezes

Samara

Galway

et al. 2017

Pers Ubiquit Comput

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“…In medicine, it can be used as heart rate monitoring [19], glucose levels [20], and patient data [21]. In the automotive field, it is used to learn the driver behavior pattern [22]. Some cases in agriculture also utilizes monitoring, such as to monitor microclimate [23], monitor soil moisture [24], and plant growth monitoring [25].…”

Section: A Monitoringmentioning

confidence: 99%

Greenhouse Monitoring and Automation Using Arduino: a Review on Precision Farming and Internet of Things (IoT)

Ardiansah¹,

Bafdal²,

Suryadi³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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The 21st century became the beginning of the development of information technology, where one of the revolutions was the presence of the Internet of Things. Internet of Things or abbreviated as IoT is a technology that combines electronic devices, sensors, and the internet to manage data and applications. The Internet of Things can be adopted in agriculture for crop management as a media for monitoring and controlling, especially in greenhouses and is called Precision Farming. The application of precision farming will be more effective in a greenhouse because it is easier to engineer similar environmental conditions. IoT development in greenhouses is using Arduino Microcontroller or Raspberry Pi Microcomputer. These devices are used because the price is low and easy to get on the market and can be designed so that technicians who have limited information technology knowledge can run it. To be able to manage greenhouses with IoT requires sensors as five senses that can detect changes that occur in the greenhouse. By using sensors, the hardware can detect what is happening in the greenhouse and make decisions based on the data acquired. Some sensors that are often used in Precision Farming are temperature and humidity sensors, soil moisture sensors, and light sensors. In the Internet of Things, the data that has been acquired by the hardware will then be transmitted wirelessly. The wireless connections used are Bluetooth, ZigBee Protocol, and Wi-Fi, where Bluetooth and Zigbee connections have a short distance between 10-100 meters, while Wi-Fi has a longer distance especially when connected to the Internet. The purpose of this paper is to understand the advantages and challenges of adopting IoT-based Precision Farming for monitoring and automation.

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“…After analysing the state of the art of the alerts, the variety of them and the experience obtained in the experiment developed in [3] and [12], we propose the use of a surround system 360° inside the vehicle as is shown in Figure 4, to give the user the ability to know the existence of hazards around him even without the need of see a monitor or infotainment screen. Despite it is a good concept, it has not been installed in any car yet, so, it is not clear how the system will be connected to ADAS and if necessary a major hardware and software integration.…”

Section: ° Adas Alert Proposalmentioning

confidence: 99%

A Logic Architecture for 360 ADAS-Alerts for Hazards Detection Based in Driver Actions

et al. 2017

Self Cite

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Abstract. In this work is presented a novel approach for passive safety in vehicles by Advanced Driver Assistance Systems (ADAS) alert emission in 360° around driver to notify about hazards nearby the vehicle depending on the actions taken by driver per the context. This proposal would create a more robust system compared to current passive ADAS systems since the feedback to driver is in the same direction that hazard is detected (Punctual Sound Source Alert), compared with most assistance systems that emits sounds from the monitor or from the dashboard provoking distractions when emits alerts unnecessarily. The increase of security by this method will allow the driver to be aware of their surroundings even in a very quiet cabin or in a noisy environment. Also, it would detect the steering wheel angle, speed of movement and the activation of turning lights among other alerts, which would allow us to define a critical action during driving; apart from using sensors and cameras aimed at the driver to detect patterns of movement during these critical actions and have a prediction of a possible turn or manoeuvre when driving, refer to Figure 1. It will be necessary a reconfiguration of the alert in frequency, time of action depending upon the level of risk to prevent an accident or to reduce the consequences in an imminent accident.

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Advanced driver monitoring for assistance system (ADMAS)

Cited by 28 publications

References 28 publications

Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset

Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset

Greenhouse Monitoring and Automation Using Arduino: a Review on Precision Farming and Internet of Things (IoT)

A Logic Architecture for 360 ADAS-Alerts for Hazards Detection Based in Driver Actions

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