Automated drowsiness detection for improved driving safety

Several approaches were proposed for the detection and prediction of drowsiness. The approaches can be categorized as estimating the fitness of duty, modeling the sleep-wake rhythms, measuring the vehicle based performance and online operator monitoring. Computer vision based online operator monitoring approach has become prominent due to its predictive ability of detecting drowsiness. Previous studies with this approach detect driver drowsiness primarily by making preassumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers for 30 facial actions from the Facial Action Coding system were developed using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. In addition, head motion was collected through automatic eye tracking and an accelerometer. These measures were passed to learning-based classifiers such as Adaboost and multinomial ridge regression. The system was able to predict sleep and crash episodes during a driving computer game with 96% accuracy within subjects and above 90% accuracy across subjects. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis revealed new information about human behavior during drowsy drivin

Real-time image and video processing for advanced services on-board vehicles for passenger transport

The paper exploits the video camera available on-board vehicles for public transport, such as trains, coaches, ferryboats, and so on, to implement advanced services for the passengers. The idea is implementing not only surveillance systems, but also passenger services such as: people counting, smoke and/or fire alarm, automatic climate control, e-ticketing. For each wagon, an embedded acquisition and processing unit is used, which is composed by a video multiplexer, and by an image/video signal processor that implements in real-time algorithms for advanced services such as: smoke detection, to give an early alarm in case of a fire, or people detection for people counting, or fatigue detection for the driver. The alarm is then transmitted to the train information system, to be displayed for passengers or the crew staff

HW/SW Co-design and Prototyping Approach for Embedded Smart Camera: ADAS Case Study

In 1968, Volkswagen integrated an electronic circuit as a new control fuel injection system, called the “Little Black Box”, it is considered as the first embedded system in the automotive industry. Currently, automobile constructors integrate several embedded systems into any of their new model vehicles. Behind these automobile’s electronics systems, a sophisticated Hardware/Software (HW/SW) architecture, which is based on heterogeneous components, and multiple CPUs is built. At present, they are more oriented toward visionbased systems using tiny embedded smart camera. This visionbased system in real time aspects represents one of the most challenging issues, especially in the domain of automobile’s applications. On the design side, one of the optimal solutions adopted by embedded systems designer for system performance, is to associate CPUs and hardware accelerators in the same design, in order to reduce the computational burden on the CPU and to speed-up the data processing. In this paper, we present a hardware platform-based design approach for fast embedded smart Advanced Driver Assistant System (ADAS) design and prototyping, as an alternative for the pure time-consuming simulation technique. Based on a Multi-CPU/FPGA platform, we introduced a new methodology/flow to design the different HW and SW parts of the ADAS system. Then, we shared our experience in designing and prototyping a HW/SW vision based on smart embedded system as an ADAS that helps to increase the safety of car’s drivers. We presented a real HW/SW prototype of the vision ADAS based on a Zynq FPGA. The system detects the fatigue/drowsiness state of the driver by monitoring the eyes closure and generates a real time alert. A new HW Skin Segmentation step to locate the eyes/face is proposed. Our new approach migrates the skin segmentation step from processing system (SW) to programmable logic (HW) taking the advantage of High-Level Synthesis (HLS) tool flow to accelerate the implementation, and the prototyping of the Vision based ADAS on a hardware platform

Platform-based design, test and fast verification flow for mixed-signal systems on chip

This research is providing methodologies to enhance the design phase from architectural space exploration and system study to verification of the whole mixed-signal system. At the beginning of the work, some innovative digital IPs have been designed to develop efficient signal conditioning for sensor systems on-chip that has been included in commercial products. After this phase, the main focus has been addressed to the creation of a re-usable and versatile test of the device after the tape-out which is close to become one of the major cost factor for ICs companies, strongly linking it to model’s test-benches to avoid re-design phases and multi-environment scenarios, producing a very effective approach to a single, fast and reliable multi-level verification environment. All these works generated different publications in scientific literature. The compound scenario concerning the development of sensor systems is presented in Chapter 1, together with an overview of the related market with a particular focus on the latest MEMS and MOEMS technology devices, and their applications in various segments. Chapter 2 introduces the state of the art for sensor interfaces: the generic sensor interface concept (based on sharing the same electronics among similar applications achieving cost saving at the expense of area and performance loss) versus the Platform Based Design methodology, which overcomes the drawbacks of the classic solution by keeping the generality at the highest design layers and customizing the platform for a target sensor achieving optimized performances. An evolution of Platform Based Design achieved by implementation into silicon of the ISIF (Intelligent Sensor InterFace) platform is therefore presented. ISIF is a highly configurable mixed-signal chip which allows designers to perform an effective design space exploration and to evaluate directly on silicon the system performances avoiding the critical and time consuming analysis required by standard platform based approach. In chapter 3 we describe the design of a smart sensor interface for conditioning next generation MOEMS. The adoption of a new, high performance and high integrated technology allow us to integrate not only a versatile platform but also a powerful ARM processor and various IPs providing the possibility to use the platform not only as a conditioning platform but also as a processing unit for the application. In this chapter a description of the various blocks is given, with a particular emphasis on the IP developed in order to grant the highest grade of flexibility with the minimum area occupation. The architectural space evaluation and the application prototyping with ISIF has enabled an effective, rapid and low risk development of a new high performance platform achieving a flexible sensor system for MEMS and MOEMS monitoring and conditioning. The platform has been design to cover very challenging test-benches, like a laser-based projector device. In this way the platform will not only be able to effectively handle the sensor but also all the system that can be built around it, reducing the needed for further electronics and resulting in an efficient test bench for the algorithm developed to drive the system. The high costs in ASIC development are mainly related to re-design phases because of missing complete top-level tests. Analog and digital parts design flows are separately verified. Starting from these considerations, in the last chapter a complete test environment for complex mixed-signal chips is presented. A semi-automatic VHDL-AMS flow to provide totally matching top-level is described and then, an evolution for fast self-checking test development for both model and real chip verification is proposed. By the introduction of a Python interface, the designer can easily perform interactive tests to cover all the features verification (e.g. calibration and trimming) into the design phase and check them all with the same environment on the real chip after the tape-out. This strategy has been tested on a consumer 3D-gyro for consumer application, in collaboration with SensorDynamics AG

Single Channel Wireless EEG: Proposed Application in Train Drivers

Electroencephalography (EEG) can be used as an indicator of fatigue. Several studies have shown that slow wave brain activities, delta (0-4 Hz) and theta (4- 8 Hz), increase as an individual becomes fatigued, while the fast brain activities, alpha (8-13 Hz) and beta (13-35 Hz), decrease. However, an EEG is a complex piece of equipment that is generally used in laboratory based studies. In order to develop a fatigue countermeasure device for train drivers using EEG, there is a need for a simple and wireless EEG monitor. This paper explains the development of a single channel wireless EEG device

Online Alpha Wave detector: an Embedded hardware-software implementation

The recent trend on embedded system development opens a new prospect for applications that in the past were not possible. The eye tracking for sleep and fatigue detection has become an important and useful application in industrial and automotive scenarios since fatigue is one of the most prevalent causes of earth-moving equipment accidents. Typical applications such as cameras, accelerometers and dermal analyzers are present on the market but have some inconvenient. This thesis project has used EEG signal, particularly, alpha waves, to overcome them by using an embedded software-hardware implementation to detect these signals in real tim

Development and Evaluation of a Real-Time Framework for a Portable Assistive Hearing Device

Testing and verification of digital hearing aid devices, and the embedded software and algorithms can prove to be a challenging task especially taking into account time-to-market considerations. This thesis describes a PC based, real-time, highly configurable framework for the evaluation of audio algorithms. Implementation of audio processing algorithms on such a platform can provide hearing aid designers and manufacturers the ability to test new and existing processing techniques and collect data about their performance in real-life situations, and without the need to develop a prototype device. The platform is based on the Eurotech Catalyst development kit and the Fedora Linux OS, and it utilizes the JACK audio engine to facilitate reliable real-time performance Additionally, we demonstrate the capabilities of this platform by implementing an audio processing chain targeted at improving speech intelligibility for people suffering from auditory neuropathy. Evaluation is performed for both noisy and noise-free environments. Subjective evaluation of the results, using normal hearing listeners and an auditory neuropathy simulator, demonstrates improvement in some conditions