5,886 research outputs found
Human engineering design criteria study Final report
Human engineering design criteria for use in designing earth launch vehicle systems and equipmen
Embodied Cognitive Science of Music. Modeling Experience and Behavior in Musical Contexts
Recently, the role of corporeal interaction has gained wide recognition within cognitive musicology. This thesis reviews evidence from different directions in music research supporting the importance of body-based processes for the understanding of music-related experience and behaviour. Stressing the synthetic focus of cognitive science, cognitive science of music is discussed as a modeling approach that takes these processes into account and may theoretically be embedded within the theory of dynamic systems. In particular, arguments are presented for the use of robotic devices as tools for the investigation of processes underlying human music-related capabilities (musical robotics)
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Fuzzy transfer learning in human activity recognition.
Assisted living environments are incorporated with diļ¬erent technological solutions to improve the quality of life and well-being. In recent years, there has been a growing interest in the research community on how to develop evolving solutions to aid assisted living. Diļ¬erent techniques have been studied to address the need for technological systems which are intelligent enough to evolve their knowledge to solve tasks which have not been previously encountered. One such approach is Transfer Learning (TL), for example, between humans and robots.
Humans excel at dealing with everyday activities, learning and adapting to diļ¬erent activities. This comprises diļ¬erent complex techniques which enable the lifelong learning process from observation of our environment. To obtain similar learning in assistive agents, TL is needed. The aim of the research reported in this thesis is to address the challenge associated with learning and reuse of knowledge by assistive agents in an Ambient Assisted Living (AAL) environment. In this thesis, a novel approach to transfer learning of human activities through the combination of three methods; TL, Fuzzy Systems (FS) and Human Activity Recognition (HAR) is presented. Through the incorporation of FS into the proposed approach, uncertainty that is evident in the dynamic nature of human activities are embedded into the learning model.
This research is focused on applications in assistive robotics. This is with a purpose of enabling assistive robots in AAL environments to acquire knowledge of such activities as are performed by humans. To achieve this, an extensive investigation into existing learning methods applied in human activities is conducted. The investigation encompasses current state-of-the-art of TL approaches employed in skill transfer across diļ¬erent but contextually related activities.
To address the research questions identiļ¬ed in the thesis, the contributions of the methodology employed are in three main categories; 1) Firstly, a novel framework for human activity learning from information observed. Experiments are conducted on selected human activities to acquire enough information for building the framework. From the acquired information, relevant features extracted are used in a learning model to recognise diļ¬erent activities. 2) Secondly, the sequence of occurrence(s) of tasks in an activity needs to be considered in the learning process. Therefore, in this research, a novel technique for adaptive learning of activity sequences from acquired information is developed. 3) Finally, from the sequence obtained, a novel technique for transfer of human activity across heterogeneous feature space existing between a human and an assistive robot is developed. These categories form the basis of the TL framework modelled in this research.
The framework proposed is applied to TL of human activity from data generated experimentally and benchmark datasets of various classes of human activities. The results presented in this thesis show that exploring the process of human activity learning is an important aspect in the TL framework. The features extracted suļ¬ciently distinguish relevant patterns for each activity. Also, the results demonstrate the ability of the methodology to learn and predict human actions with a high degree of certainty. This encourages the use of TL in assisted living environments and other applications. This and many more applications of TL in technology would be a potential driver of the next revolution in artiļ¬cial intelligence
Development of a text reading system on video images
Since the early days of computer science researchers sought to devise a machine which could automatically read text to help people with visual impairments. The problem of extracting and recognising text on document images has been largely resolved, but reading text from images of natural scenes remains a challenge. Scene text can present uneven lighting, complex backgrounds or perspective and lens distortion; it usually appears as short sentences or isolated words and shows a very diverse set of typefaces. However, video sequences of natural scenes provide a temporal redundancy that can be exploited to compensate for some of these deficiencies. Here we present a complete end-to-end, real-time scene text reading system on video images based on perspective aware text tracking.
The main contribution of this work is a system that automatically detects, recognises and tracks text in videos of natural scenes in real-time. The focus of our method is on large text found in outdoor environments, such as shop signs, street names and billboards. We introduce novel efficient techniques for text detection, text aggregation and text perspective estimation. Furthermore, we propose using a set of Unscented Kalman Filters (UKF) to maintain each text regionĀæs identity and to continuously track the homography transformation of the text into a fronto-parallel view, thereby being resilient to erratic camera motion and wide baseline changes in orientation. The orientation of each text line is estimated using a method that relies on the geometry of the characters themselves to estimate a rectifying homography. This is done irrespective of the view of the text over a large range of orientations. We also demonstrate a wearable head-mounted device for text reading that encases a camera for image acquisition and a pair of headphones for synthesized speech output.
Our system is designed for continuous and unsupervised operation over long periods of time. It is completely automatic and features quick failure recovery and interactive text reading. It is also highly parallelised in order to maximize the usage of available processing power and to achieve real-time operation. We show comparative results that improve the current state-of-the-art when correcting perspective deformation of scene text. The end-to-end system performance is demonstrated on sequences recorded in outdoor scenarios. Finally, we also release a dataset of text tracking videos along with the annotated ground-truth of text regions
Learning Transferable Push Manipulation Skills in Novel Contexts
This paper is concerned with learning transferable forward models for push
manipulation that can be applying to novel contexts and how to improve the
quality of prediction when critical information is available. We propose to
learn a parametric internal model for push interactions that, similar for
humans, enables a robot to predict the outcome of a physical interaction even
in novel contexts. Given a desired push action, humans are capable to identify
where to place their finger on a new object so to produce a predictable motion
of the object. We achieve the same behaviour by factorising the learning into
two parts. First, we learn a set of local contact models to represent the
geometrical relations between the robot pusher, the object, and the
environment. Then we learn a set of parametric local motion models to predict
how these contacts change throughout a push. The set of contact and motion
models represent our internal model. By adjusting the shapes of the
distributions over the physical parameters, we modify the internal model's
response. Uniform distributions yield to coarse estimates when no information
is available about the novel context (i.e. unbiased predictor). A more accurate
predictor can be learned for a specific environment/object pair (e.g. low
friction/high mass), i.e. biased predictor. The effectiveness of our approach
is shown in a simulated environment in which a Pioneer 3-DX robot needs to
predict a push outcome for a novel object, and we provide a proof of concept on
a real robot. We train on 2 objects (a cube and a cylinder) for a total of
24,000 pushes in various conditions, and test on 6 objects encompassing a
variety of shapes, sizes, and physical parameters for a total of 14,400
predicted push outcomes. Our results show that both biased and unbiased
predictors can reliably produce predictions in line with the outcomes of a
carefully tuned physics simulator.Comment: This work has been submitted to IEEE Transactions on Robotics journal
in July 202
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