Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller

Mobile robots with manipulators have been more and more commonly applied in extreme and hostile environments to assist or even replace human operators for complex tasks. In addition to autonomous abilities, mobile robots need to facilitate the human–robot interaction control mode that enables human users to easily control or collaborate with robots. This paper proposes a system which uses human gestures to control an autonomous mobile robot integrating a manipulator and a video surveillance platform. A human user can control the mobile robot just as one drives an actual vehicle in the vehicle’s driving cab. The proposed system obtains human’s skeleton joints information using a motion sensing input device, which is then recognized and interpreted into a set of control commands. This is implemented, based on the availability of training data set and requirement of in-time performance, by an adaptive cerebellar model articulation controller neural network, a finite state machine, a fuzzy controller and purposely designed gesture recognition and control command generation systems. These algorithms work together implement the steering and velocity control of the mobile robot in real-time. The experimental results demonstrate that the proposed approach is able to conveniently control a mobile robot using virtual driving method, with smooth manoeuvring trajectories in various speeds

A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

open access articleDetecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics. Knowing the degree of driver distraction can aid in accident prevention techniques, including transitioning of control to a level 4 semi- autonomous vehicle, when a high distraction severity level is reached. Thus, enhancement of Advanced Driving Assistance Systems (ADAS) is a critical component in the safety of vehicle drivers and other road users. In this paper, a new methodology is introduced, using an expert knowledge rule system to predict the severity of distraction in a contiguous set of video frames using the Naturalistic Driving American University of Cairo (AUC) Distraction Dataset. A multi-class distraction system comprises the face orientation, drivers’ activities, hands and previous driver distraction, a severity classification model is developed as a discrete dynamic Bayesian (DDB). Furthermore, a Mamdani-based fuzzy system was implemented to detect multi- class of distractions into a severity level of safe, careless or dangerous driving. Thus, if a high level of severity is reached the semi-autonomous vehicle will take control. The result further shows that some instances of driver’s distraction may quickly transition from a careless to dangerous driving in a multi-class distraction context

Understanding face and eye visibility in front-facing cameras of smartphones used in the wild

Commodity mobile devices are now equipped with high-resolution front-facing cameras, allowing applications in biometrics (e.g., FaceID in the iPhone X), facial expression analysis, or gaze interaction. However, it is unknown how often users hold devices in a way that allows capturing their face or eyes, and how this impacts detection accuracy. We collected 25,726 in-the-wild photos, taken from the front-facing camera of smartphones as well as associated application usage logs. We found that the full face is visible about 29% of the time, and that in most cases the face is only partially visible. Furthermore, we identified an influence of users' current activity; for example, when watching videos, the eyes but not the entire face are visible 75% of the time in our dataset. We found that a state-of-the-art face detection algorithm performs poorly against photos taken from front-facing cameras. We discuss how these findings impact mobile applications that leverage face and eye detection, and derive practical implications to address state-of-the art's limitations

Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots

Central monitoring system for ambient assisted living

Smart homes for aged care enable the elderly to stay in their own homes longer. By means of various types of ambient and wearable sensors information is gathered on people living in smart homes for aged care. This information is then processed to determine the activities of daily living (ADL) and provide vital information to carers. Many examples of smart homes for aged care can be found in literature, however, little or no evidence can be found with respect to interoperability of various sensors and devices along with associated functions. One key element with respect to interoperability is the central monitoring system in a smart home. This thesis analyses and presents key functions and requirements of a central monitoring system. The outcomes of this thesis may benefit developers of smart homes for aged care

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Privacy-Protecting Techniques for Behavioral Data: A Survey

Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved