Drowsiness is a critical factor impairing drivers’ performance in driving safely. There are several approaches in dealing with this issue based on human-machine interaction to detect drivers’ dozing off state, and then alert them to keep awake by sound or visual. These techniques fundamentally measure driver’s physical changes such as head angle, fatigue level and eyes states which are the indicators of drowsy state. However, they are limited in providing accurate and reliable results. Therefore, the project aims to achieve higher accuracy rate of drowsiness detection by using a very potential technology, electroencephalography (EEG) which is used widely in medical areas. Other than providing reliable result, the final product would bring more conveniences for customers with portability, easy-to-deploy and multi-device compatibility feature. In this project, its methodology first shows the strong correlation between drowsy state with brainwave frequency. Then a proposed system and testing plan are suggested based on the project objectives and available technologies. The final product is simply comprised of a hat with attached small electronic package used to record brainwave and a handheld device placing on dashboard of the car with an installed app. Finally, project management section will present in detail the human resources, scheduling, budget plan and risk analysis to show how it will be going to complete the project in six months.
The purpose of this project is to control robot with an interface board of the Raspberry Pi, sensors and software to full fill real time requirement.
Controlling DC motors, different sensors, camera interfacing with raspberry Pi using GPIO pin.
Live streaming, Command the robot easily, sends data of different sensors which works automatically or control from anywhere at any time.
Design of the website and control page of robot is done using Java tools and HTML. This system works on IOT concept.
This will enable Raspberry Pi to be used for more robotic applications and cut down the cost for building an IOT Robot.
The main objective of our project is to provide an optimum solution to the traffic hazards and the road accidents. According to this project when a vehicle meets with an accident, immediately vibration sensor will detect the signal and sends it to ARM controller. Microcontroller sends the alert message through the GSM MODEM including the location to police control room or a rescue team. So the police can immediately trace the location through the GPS MODEM after receiving the information.
This is a presentation of OBSTACLE AVOIDANCE ROBOT. which has the details on making an obstacle avoider using arduino uno, ultrasonic sensor. This presentation has the detailed description of all the components that are being used in making. And also circuit diagram and flow chart of the robot.
The purpose of this project is to control robot with an interface board of the Raspberry Pi, sensors and software to full fill real time requirement.
Controlling DC motors, different sensors, camera interfacing with raspberry Pi using GPIO pin.
Live streaming, Command the robot easily, sends data of different sensors which works automatically or control from anywhere at any time.
Design of the website and control page of robot is done using Java tools and HTML. This system works on IOT concept.
This will enable Raspberry Pi to be used for more robotic applications and cut down the cost for building an IOT Robot.
The main objective of our project is to provide an optimum solution to the traffic hazards and the road accidents. According to this project when a vehicle meets with an accident, immediately vibration sensor will detect the signal and sends it to ARM controller. Microcontroller sends the alert message through the GSM MODEM including the location to police control room or a rescue team. So the police can immediately trace the location through the GPS MODEM after receiving the information.
This is a presentation of OBSTACLE AVOIDANCE ROBOT. which has the details on making an obstacle avoider using arduino uno, ultrasonic sensor. This presentation has the detailed description of all the components that are being used in making. And also circuit diagram and flow chart of the robot.
This is a presentation that focuses on autonomous vehicles technology. The presentation describes key sensor technologies integrated under the bonnet of a driverless car. After a brief introduction, the presentation dwells deeper into each sensor technology demonstrating examples of self driving cars such as Google's self driving car, DARPA URBAN challenge etc., along the way. It also introduces the concept of electronic control units which is responsible for collecting data from different sensors and respond to other units accordingly. The slides also build a platform for vehicle to vehicle communication technology, types and its application areas.
A self-driving car is a vehicle that is capable of sensing its environment and navigating without human input. Self-driving cars can detect surroundings, using a variety of techniques such as radar, GPS, and computer vision.
<<instagram>> https://www.instagram.com/mike_sarafoglou/
<<youtube>> https://www.youtube.com/channel/UCZQDCo6W-3wTkbrkHbgjs2w
Seminar on night vision technology pptdeepakmarndi
ppt of night vission technology. this is made under the guidance of teacher. withe this report also given in theis side. main things report is given according to the ppt...........
The Smart Home Automation made by using Arduino and Cayenne as IoT middleware to control and monitor through a mobile app and the web from anywhere at anytime.
The system configured to send SMS and Email notification due to the reaction of smoke, temperature, magnetic door, PIR motion sensors.
automatic railway gate control system using arduinoantivirusspam
The objective of this project is to manage the control system of railway gate using the arduino. When train arrives at the sensing point alarm is triggered at the railway crossing point so that the people get intimation that gate is going to be closed. Then the control system activates and closes the gate on either side of the track once the train crosses the other end control system automatically lifts the gate.
Drowsiness State Detection of Driver using Eyelid Movement- IRE Journal Confe...Vignesh C
A presentation on Drowsiness State Detection of Driver using Eyelid Movement in IRE Journal publications in Volume 2 Issue 10 2019. In the field of automobile, drowsiness causes more setbacks, which this presentation initiate a step in finding the solution.
The Internet of Things (IoT) is the network of
physical objects devices, vehicles, buildings and
other items embedded with electronics, software,
sensors, and network connectivity that enables
these objects to collect and exchange data.
Starting from small houses to huge industries,
surveillance plays very vital role to fulfill our
safety aspects as Burglary and theft have always
been a problem. In big industries personal security
means monitoring the people’s changing
information like activities, behavior for the purpose
of protecting, managing and influencing
confidential details. Surveillance means watching
over from a distance by means of electronic
equipment such as CCTV cameras but it is costly
for normal residents to set up such kind of system
and also it does not inform the user immediately
when the burglary happens.
Today, every person facing the problem of traffic jam on the road, road accident is the measure problem for us. So this device “SMART VEHICLE SECURA”, which can be useful for stopping the accident and also useful for saving the life of human being. It is a device that works to provide the security of the vehicles.
Driver drowsiness detection is a car safety technology which helps prevent accidents caused by the driver getting drowsy. Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads
it is a presentation based on image processing used in the field of fatigue detection while driving which can save many life as well as prevent accident.
This is a presentation that focuses on autonomous vehicles technology. The presentation describes key sensor technologies integrated under the bonnet of a driverless car. After a brief introduction, the presentation dwells deeper into each sensor technology demonstrating examples of self driving cars such as Google's self driving car, DARPA URBAN challenge etc., along the way. It also introduces the concept of electronic control units which is responsible for collecting data from different sensors and respond to other units accordingly. The slides also build a platform for vehicle to vehicle communication technology, types and its application areas.
A self-driving car is a vehicle that is capable of sensing its environment and navigating without human input. Self-driving cars can detect surroundings, using a variety of techniques such as radar, GPS, and computer vision.
<<instagram>> https://www.instagram.com/mike_sarafoglou/
<<youtube>> https://www.youtube.com/channel/UCZQDCo6W-3wTkbrkHbgjs2w
Seminar on night vision technology pptdeepakmarndi
ppt of night vission technology. this is made under the guidance of teacher. withe this report also given in theis side. main things report is given according to the ppt...........
The Smart Home Automation made by using Arduino and Cayenne as IoT middleware to control and monitor through a mobile app and the web from anywhere at anytime.
The system configured to send SMS and Email notification due to the reaction of smoke, temperature, magnetic door, PIR motion sensors.
automatic railway gate control system using arduinoantivirusspam
The objective of this project is to manage the control system of railway gate using the arduino. When train arrives at the sensing point alarm is triggered at the railway crossing point so that the people get intimation that gate is going to be closed. Then the control system activates and closes the gate on either side of the track once the train crosses the other end control system automatically lifts the gate.
Drowsiness State Detection of Driver using Eyelid Movement- IRE Journal Confe...Vignesh C
A presentation on Drowsiness State Detection of Driver using Eyelid Movement in IRE Journal publications in Volume 2 Issue 10 2019. In the field of automobile, drowsiness causes more setbacks, which this presentation initiate a step in finding the solution.
The Internet of Things (IoT) is the network of
physical objects devices, vehicles, buildings and
other items embedded with electronics, software,
sensors, and network connectivity that enables
these objects to collect and exchange data.
Starting from small houses to huge industries,
surveillance plays very vital role to fulfill our
safety aspects as Burglary and theft have always
been a problem. In big industries personal security
means monitoring the people’s changing
information like activities, behavior for the purpose
of protecting, managing and influencing
confidential details. Surveillance means watching
over from a distance by means of electronic
equipment such as CCTV cameras but it is costly
for normal residents to set up such kind of system
and also it does not inform the user immediately
when the burglary happens.
Today, every person facing the problem of traffic jam on the road, road accident is the measure problem for us. So this device “SMART VEHICLE SECURA”, which can be useful for stopping the accident and also useful for saving the life of human being. It is a device that works to provide the security of the vehicles.
Driver drowsiness detection is a car safety technology which helps prevent accidents caused by the driver getting drowsy. Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads
it is a presentation based on image processing used in the field of fatigue detection while driving which can save many life as well as prevent accident.
This project represents a way of developing an
interface to detect driver drowsiness based on continuously
monitoring eyes and DIP algorithms. Micro sleeps that are short
period of sleeps lasting 2 to 3 seconds are good indicator of
fatigue state. Thus by continuously monitoring the eyes of the
driver by using camera one can detect the sleepy state of driver
and timely warning is issued.
Aim of the project is to develop the hardware which is very
advanced product related to driver safety on the roads using
controller and image processing. This product detects driver
drowsiness and gives warning in form of alarm and as well as
decreases the speed of vehicle.Along with the drowsiness
detection process there is continuous monitoring of the distance
done by the Ultrasonic sensor. The ultrasonic sensor detects the
obstacle and accordingly warns the driver as well as decreases
speed of vehicle.
Yawning analysis for driver drowsiness detectioneSAT Journals
Abstract Driver fatigue is the main reason for fatal road accidents around the world. In this paper, an efficient driver’s drowsiness detection system is designed using yawning detection.Here, we consider eye detection and mouth detection. So that road accidents can avoid successfully. Mouth features points are identified using the redness property. Firstly detecting the driver’s face using YCbCr method then face tracking will perform using canny edge detector. After that , eyes and mouth positions by using Haar features. Lastly yawning detection is perform by using mouth geometric features. This method is tested on images from videos. Also proposed system should then alert to the driver in case of inattention. Keywords: Face detection, Face tracking, Eye and Mouth detection, Yawn detection
Vision based system for monitoring the loss of attention in automotive driverVinay Diddi
This is a real time driver drowsiness detection system is used to alert the driver when he is drowsy. It consist of raspberry pi and OpenCV image processing library.
ACCIDENT PREVENTION AND SECURITY SYSTEM FOR AUTOMOBILESAdrija Chowdhury
This presentation is simply based on final year embedded systems based project entitled "ACCIDENT PREVENTION AND SECURITY SYSTEM FOR AUTOMOBILES". This project represents an automobile which has a security system of its own to prevent and detect accidents using GPS Module, GSM module and Alcohol sensor as well.
VMworld 2013: Quantifying the Business Value of VMware Horizon View VMworld
VMworld 2013
Aivars Apsite, Metro Health
Ridwan Huq, VMware
Learn more about VMworld and register at http://www.vmworld.com/index.jspa?src=socmed-vmworld-slideshare
Da Vinci - A scaleable architecture for neural network computing (updated v4)Heiko Joerg Schick
Introduction
- Computation in brains and machines
- The hype roller coaster of artificial intelligence | Neural networks beat human performance
- Two distinct eras of compute usage in training AI systems
- Microprocessor trends | Rich variety of computing architectures
- Comparison of processors for deep learning | Preferred architectures for compute are shifting
- Data structure of digital images | Kernel convolution example | - - - Architecture of LeNet-5
Applicability of artificial intelligence
- Ubiquitous and future AI computation requirements
- Artificial intelligence in modern medicine
Product realisation
- Scalable across devices
- Focus on innovation, continuous dedication and backward compatibility
- HiSilicon Ascend 310 | HiSilicon Ascend 910 | HiSilicon Kungpeng 920
Da Vinci architecture
- Building blocks and compute intensity
- Advantages of special compute units
- Da Vinci core architecture | Micro-architectural configurations
End-to-end lifecycle
- Implementation of end-to-end lifecycle in AI projects
- The Challenges to AI implementations
Software stack
- Ascend AI software stack | Logical architecture
- Software flow for model conversion and deployment | Framework manager | Digital vision pre-processing
- Mind Studio | Model Zoo (excerpt)
Gain more practical experiences
- Atlas 200 DK developer board | Application examples | Getting started | Environment deployment
- Ascend developer community
Getting started with Atlas 200 DK developer board
- Preparing the Ubuntu-based development environment
- Environment deployment
- Hardware and software requirements | About version 1.73.0.0
- Install environment dependencies
- Install the toolkit packages
- Install the media module device driver
- Install Mind Studio
Create and write SD card image
- Setting up the operating environment
Boot and connect to the Atlas 200 DK developer board
- Power on the Atlas 200 DK developer board
Install third-party packages
- Installation of additional packages (FFmpeg, OpenCV and Python)
NeuroVR: Open Source VR toolkit for Behavioral Healthcare and RehabilitationRiva Giuseppe
NeuroVR is a cost-free virtual reality platform based on open-source software. It allows non-expert users to easily modify a virtual world, to best suit the needs of a clinical setting.
Self-charging, Highly Accurate Insole-Based Health Trackers for Medical Grade...INVIZA® HEALTH
INVIZA® HEALTH generates power from piezoelectric, mechanical energy harvesting to enable its health and fitness sensor suite. In addition, by using multiple sensor's output data via software, i.e. "sensor fusion" INVIZA has learned to lower power overall sensor and electronics power consumption while simultaneously increasing health and fitness measured parameter's accuracy. This leads to the insole tracker's battery staying 100% full while the user obtains the most accurate data.
Presentation from SIEPON Seminar on 20 April in Czech Republic, sponsored by IEEE-SA & CAG. Opinions presented by the speakers in this presentation are their own, and not necessarily those of their employers or of IEEE.
The paper “A free, open-source virtual reality platform for the rehabilitation of cognitive and psychological disorders,” describes the main features of NeuroVR, a cost-free virtual reality platform used for therapeutic and research applications. It received the Best Paper Award at the "Virtual Rehabilitation" conference
Vietnam is a country with enormous economic potential. But at the same time, we have hundreds of thousands of disabled children. Unfortunately, many of them are from poor family, their parents cannot give them good condition to live, to study or to access medical services like other normal children. Moreover, there are not enough school for the disabled.
Our organization, Hands of Hope is trying to help these unlucky lives by providing medical services and supporting their families as well. Most funding comes from The Koop Family, so our efforts are not enough, we need your contribution, your help to give them a sense of self-respect to be able to live an independent live.
Let's contact us if you really want to help them.
----Presentation skill lesson ----- :D
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Anti drowsy alarm for drivers
1. Anti-drowsiness system for drivers
using EEG technology
December 2012
Nguyen Van Duc
Student ID : s3411503
Teacher : Dang Trong Trinh
2. CONTENTS
1 Introduction
2 Current approaches
3 Brainwave hat
4 Project management
5 Conclusion
3. Introduction Drowsy driving
The most danger to drivers & traffic participants [1]
10-20%
Road accidents in Europe [2]
http://www.end-your-sleep-deprivation.com/
100,000 road crashes 1,550 deaths
Result from drowsy driving in US [3]
4. Current approaches
Anti-Sleep Pilot Anti-Sleep Alarm Sleep Watcher-XR
http://www.antisleeppilot.com http://www.automotto.com http://www.exeros-technologies.com
Depending too much Not work when drivers Sensitive with
on the input data tilt the head backward, alternative illumination
right or left side[4] (~70% accurate rate) [1]
5. Brainwave hat Project objectives
Higher accurate rate detection
(>98%)
Portable & easy to deploy
Applicable to multiple devices
http://www.sydney-neurofeedback.com.au/
6. Brainwave hat Brainwave and cognitive states
Beta 13-30 Hz, 5-20 μV
Alert
8-13 Hz, 20-60 μV
Alpha
Relax
Theta 4-8 Hz, 20-100 μV
Drowsy
0.5 – 4 Hz, 20-200 μV
Delta
Sleep
Adapted from [5-10]
7. Brainwave hat Product concept
The product is mainly comprised of a hat and a dock for handheld device
8. Brainwave hat System description
Amplifier
Electrodes
Alarm system
Filter
Processor
ADC
Wireless Wireless
Sender Receiver
Sender module Receiver module
9. Brainwave hat Product cost
Price/Unit Cost
Items Units
(USD) (USD)
Electrodes 2 18 36
OPA333 1 1.15 1.15
MSP430 1 4.3 4.3
CC2500 1 1.7 1.7
PCB 1 4 5
Other
1 5 5
components
Dock 1 10 10
Raspberry Pi B
1 40 40
board
Total $103.15
10. Brainwave hat Testing plan
- The virtual reality of car driving model suggested by Yin Niandong [11]
- The experiment will collect three main information:
+ The fatigue level of participants
+ The response of the system on drowsiness detection
+ The estimated fatigue level of supervisor
13. Project management Budget plan
Initial fixed cost:
Price/Unit Total cost
Items Units
(USD) (USD)
Handheld devices
4 300 1200
(smartphones, tablets)
Sender’s components 4 60 240
Software license
(Cross-platform mobile 2 995 1990
development: Sencha Touch 2)
Furniture 1 500 500
Testing instruments 1 1000 1000
Total $4,930
14. Project management Budget plan
Monthly cost:
Cost for
Cost per
Items 6 months
month (USD)
(USD)
Nguyen Van Duc 1200 7200
Tran Manh Nguyen 700 4200
Salary
Ly Tue Hai 700 4200
Van Ngoc Mai 1000 6000
Electricity 50 300
Water 20 120
Utilities Internet 25 150
Telephone 25 150
Rental fee 250 1500
Total $23,820
Total cost: $4,930 + $23,820 = $28,550
15. Conclusion
Drowsy driving is a serious threat to drivers and traffic
participants
Non-reliable results of current solutions
New approach: “Brainwave Hat” is considered as a best
solution
“Brainwave Hat” project is feasible to implement within 6
months with available resources and technologies
16. REFERENCES
[1] W. H. Fei, Cheng and G. Xueming, "Real-Time Driver Drowsiness Tracking System " Nios II Embedded
Processor Design Contest - Outstanding Designs 2005, pp. 179-188, 2005.
[2] E. BEKIARIS, "System for effective Assessment of driver vigilance and Warning According to traffic risK
Estimation ", ed: CENTRE FOR RESEARCH AND TECHNOLOGY HELLAS, 2004.
[3] (18 November 2012). Research on Drowsy Driving Available:
http://www.nhtsa.gov/Driving+Safety/Distracted+Driving/Research+on+Drowsy+Driving
[4] (2007, 18 November 2012). Minister Issues Ban on Driving Anti-Sleep Alarm. Available:
http://www.legislation.vic.gov.au/domino/Web_Notes/newmedia.nsf/798c8b072d117a01ca256c8c0019bb01/5
1439959054b623bca2572b8007b358e!OpenDocument
[5] S. K. L. Lal and A. Craig, "A critical review of the psychophysiology of driver fatigue," Biological
Psychology, vol. 55, pp. 173-194, 2001.
[6] M. J. Wagner, "Effect of music and biofeedback on alpha brainwave rhythms and attentiveness," Journal of
Research in Music Education, vol. 23, pp. 3-13, 1975.
[7] L. Dailey, N. Krause, M. Roberts, K. Schuster, and C. Vang, "Effects of Physiological Relaxation on
Response Time," 2012.
[8] U. Svensson, "Blink behaviour based drowsiness detection," 0347-6049, 2004.
[9] J. L. Andreassi, Psychophysiology: Human behavior & physiological response: Lawrence Erlbaum, 2000.
[10] E. Grandjean and D. Barker, Fitting the Task to the Man, 1988.
[11] Y. Niandong, X. Ping, and T. Jing, "Construction of virtual experiment platform for research about vehicle
driving control at energy-saving," in Computer Supported Cooperative Work in Design (CSCWD), 2010 14th
International Conference on, 2010, pp. 427-431.
As you can see on the picture showing the accident results from drowsy driving in Los Angeles - Drowsy driving is one of the most dangers to drivers as well as to other traffic participants which could lead to the serious accident. Even drivers get into brief sleep in several seconds, it might cause fatal incident. And drowsy driving causes many accidents, there are 10-20% of road accidents in Europe caused by drowsiness when driving. In USA, it seem to be more serious with 100,000 crashes & 1,550 deaths resulting from drowsy driving
In adult human brainwave frequencies, from 0-30Hz, have been divided into 4 groups which are associated with different human states such as alertness, sleepiness, fatigue.*Beta: It is associated with state of alertness, concentration. It indicates that beta rhythm appears when people highly concentrate on doing something. It appears when you try to solve a touch math problem, a puzzle.For example: when I am standing here, my brain is generating beta wave because I have to think quickly and rapidly to come up with solutions to problems*Alpha:This brainwave indicates the state of relaxation and it takes place during wakefulness state. For example: in the lecture time, some students daydream about their girl friends.*Theta:This rhythm is associated with the light sleep state, usually appears at the beginning of deep sleep stage. Theta state have a strong link with drowsiness with low level of processing information. *Delta:Delta brainwaves are characterized as a transition from light sleep to deep sleep
The final product will be similar to the one showing in this figure. It is mainly comprised of a hat and a dock for your handheld device such as smartphone, table to place on. Note that, there is small electronic package attached inside the hat which is used to record the brain wave on the left hand side. For using it, driver simply wear the hat and launch the app on the phone,. Every time drivers feel sleepy, the speaker inside the dock will be triggered to warn them of focusing on the road.
Now we go deeply into the system:We have two modules in this system: the sender module and the receiver module. In the sender module, we first need electrodes to record brainwave, then it is amplified for further process because of its very small amplitude. Next, the interested frequency band which is brainwave band will be filtered before converting to digital signal by ADC block, and finally it is transmitted wirelessly to receiver module. In the receiver module, it is simply includes a processor which could be your smartphone, tablet or an embedded board in case you do not have those things. And the alarm.Here sender is build as small as a coin to attach inside a head, and a dock look like this to place handheld device.
Based on the correlation of particular brainwaves with sleep phases, emotional states . It is possible to estimate the level of attentiveness of particular driver by monitoring brainwave. Because the input voltage from electrodes is very small, about microvolts, so it need to be amplified for further processing. Then it is sent to filter & ADC blocks before transmitting to Receveiver module which contains wireless receiver, processor & the alarm system(usually sound). Note that: Processor here could be an embedded board or portable devices like tablets, smart phone.
Here is table showing the product cost which is approximately 100$. It is a reasonable price for drivers.
In order to verify the proposed project whether it attain the objectives with accurate rate up to more than 98%. Here I used a car driving model suggested by Yin.In the test, there are three information will be collected for experiment:+ The fatigue level of participant at the beginning and during the test+ The response of system on participant’s drowsiness state+ We also need a supervisor to collect fatigue level of driver
Our team doing this project includes 4 engineers: The team leader is me, and we have 3 main tasks which are assigned to different people as you can see on the or. ChartHD, SD and I&T
The project aims to be finished within 6 months, starting at 1/1/2013 and end on 30 JuneFour phases we would like to finish are Pr. Planning, Research, System design, integrating& testing phase and finally project evaluation.
In term of budget plan, I divide it into two parts which is Initial fixed cost and monthly cost:Here initial fixed cost is about 3.3 thounsand $s
Cost for 6 months of project progress is about 16000$The total cost is 19360