Systematic literature review of hand gestures used in human computer interaction interfaces

Gestures, widely accepted as a humans' natural mode of interaction with their surroundings, have been considered for use in human-computer based interfaces since the early 1980s. They have been explored and implemented, with a range of success and maturity levels, in a variety of fields, facilitated by a multitude of technologies. Underpinning gesture theory however focuses on gestures performed simultaneously with speech, and majority of gesture based interfaces are supported by other modes of interaction. This article reports the results of a systematic review undertaken to identify characteristics of touchless/in-air hand gestures used in interaction interfaces. 148 articles were reviewed reporting on gesture-based interaction interfaces, identified through searching engineering and science databases (Engineering Village, Pro Quest, Science Direct, Scopus and Web of Science). The goal of the review was to map the field of gesture-based interfaces, investigate the patterns in gesture use, and identify common combinations of gestures for different combinations of applications and technologies. From the review, the community seems disparate with little evidence of building upon prior work and a fundamental framework of gesture-based interaction is not evident. However, the findings can help inform future developments and provide valuable information about the benefits and drawbacks of different approaches. It was further found that the nature and appropriateness of gestures used was not a primary factor in gesture elicitation when designing gesture based systems, and that ease of technology implementation often took precedence

Driver lane change intention inference using machine learning methods.

Lane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways. This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ. Finally, discussions and conclusions are made in Part Ⅵ. A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour.PhD in Transpor

Applied Cognitive Sciences

Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

On Motion Analysis in Computer Vision with Deep Learning: Selected Case Studies

Motion analysis is one of the essential enabling technologies in computer vision. Despite recent significant advances, image-based motion analysis remains a very challenging problem. This challenge arises because the motion features are extracted directory from a sequence of images without any other meta data information. Extracting motion information (features) is inherently more difficult than in other computer vision disciplines. In a traditional approach, the motion analysis is often formulated as an optimisation problem, with the motion model being hand-crafted to reflect our understanding of the problem domain. The critical element of these traditional methods is a prior assumption about the model of motion believed to represent a specific problem. Data analytics’ recent trend is to replace hand-crafted prior assumptions with a model learned directly from observational data with no, or very limited, prior assumptions about that model. Although known for a long time, these approaches, based on machine learning, have been shown competitive only very recently due to advances in the so-called deep learning methodologies. This work's key aim has been to investigate novel approaches, utilising the deep learning methodologies, for motion analysis where the motion model is learned directly from observed data. These new approaches have focused on investigating the deep network architectures suitable for the effective extraction of spatiotemporal information. Due to the estimated motion parameters' volume and structure, it is frequently difficult or even impossible to obtain relevant ground truth data. Missing ground truth leads to choose the unsupervised learning methodologies which is usually represents challenging choice to utilize in already challenging high dimensional motion representation of the image sequence. The main challenge with unsupervised learning is to evaluate if the algorithm can learn the data model directly from the data only without any prior knowledge presented to the deep learning model during In this project, an emphasis has been put on the unsupervised learning approaches. Owning to a broad spectrum of computer vision problems and applications related to motion analysis, the research reported in the thesis has focused on three specific motion analysis challenges and corresponding practical case studies. These include motion detection and recognition, as well as 2D and 3D motion field estimation. Eyeblinks quantification has been used as a case study for the motion detection and recognition problem. The approach proposed for this problem consists of a novel network architecture processing weakly corresponded images in an action completion regime with learned spatiotemporal image features fused using cascaded recurrent networks. The stereo-vision disparity estimation task has been selected as a case study for the 2D motion field estimation problem. The proposed method directly estimates occlusion maps using novel convolutional neural network architecture that is trained with a custom-designed loss function in an unsupervised manner. The volumetric data registration task has been chosen as a case study for the 3D motion field estimation problem. The proposed solution is based on the 3D CNN, with a novel architecture featuring a Generative Adversarial Network used during training to improve network performance for unseen data. All the proposed networks demonstrated a state-of-the-art performance compared to other corresponding methods reported in the literature on a number of assessment metrics. In particular, the proposed architecture for 3D motion field estimation has shown to outperform the previously reported manual expert-guided registration methodology

Digital twin of construction crane and realization of the physical to virtual connection

Digital twin is an integrated multi-physics representation of a complex physical entity. This article constructs the digital twin of the construction crane, proposes a framework for the construction of the tower crane digital twin, and realizes the connection from physical to virtual in the concept of digital twin. The main contributions are divided into three parts: development of tower crane monitoring dataset, tower crane detection and tower crane operation mode recognition. By using labellmg to annotate more than 20,000 tower crane images in 583 tower crane videos, a tower crane image recognition dataset and a tower crane operating mode dataset are established. Yolov5x algorithm is selected in the tower crane detection. Edge extraction is used to improve the quality of the raw dataset and distance-intersection-over union non-maximum suppression is used to replace the traditional non-maximum suppression part in the Yolov5x algorithm to improve the detect accuracy when some tower cranes are overlapping. The final test set detection accuracy rate is 93.85%. After comparing the LSTM and CNN algorithms, 3DResNet algorithm is selected for tower crane operational mode recognition. The raw dataset is augmented by rotating the image by ±10° and ±20°, and the augmented dataset enlarges five times. Using these methods, the final recognition accuracy of tower crane operation mode reaches 87%. These models can be installed on the cctv to monitor the running status of the tower crane in real time and transfer relevant information to the virtual model. The tower crane in the virtual space completes the action of the physical tower crane, thereby realizing the physical-to-virtual mapping in the digital twin

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions