3,119 research outputs found
Marvin: an Innovative Omni-Directional Robotic Assistant for Domestic Environments
Population ageing and pandemics recently demonstrate to cause isolation of
elderly people in their houses, generating the need for a reliable assistive
figure. Robotic assistants are the new frontier of innovation for domestic
welfare, and elderly monitoring is one of the services a robot can handle for
collective well-being. Despite these emerging needs, in the actual landscape of
robotic assistants there are no platform which successfully combines a reliable
mobility in cluttered domestic spaces, with lightweight and offline Artificial
Intelligence (AI) solutions for perception and interaction. In this work, we
present Marvin, a novel assistive robotic platform we developed with a modular
layer-based architecture, merging a flexible mechanical design with
cutting-edge AI for perception and vocal control. We focus the design of Marvin
on three target service functions: monitoring of elderly and reduced-mobility
subjects, remote presence and connectivity, and night assistance. Compared to
previous works, we propose a tiny omnidirectional platform, which enables agile
mobility and effective obstacle avoidance. Moreover, we design a controllable
positioning device, which easily allows the user to access the interface for
connectivity and extends the visual range of the camera sensor. Nonetheless, we
delicately consider the privacy issues arising from private data collection on
cloud services, a critical aspect of commercial AI-based assistants. To this
end, we demonstrate how lightweight deep learning solutions for visual
perception and vocal command can be adopted, completely running offline on the
embedded hardware of the robot.Comment: 20 pages, 9 figures, 3 tabl
Identifying attack surfaces in the evolving space industry using reference architectures
The space environment is currently undergoing a substantial change and many new entrants to the market are deploying devices, satellites and systems in space; this evolution has been termed as NewSpace. The change is complicated by technological developments such as deploying machine learning based autonomous space systems and the Internet of Space Things (IoST). In the IoST, space systems will rely on satellite-to-x communication and interactions with wider aspects of the ground segment to a greater degree than existing systems. Such developments will inevitably lead to a change in the cyber security threat landscape of space systems. Inevitably, there will be a greater number of attack vectors for adversaries to exploit, and previously infeasible threats can be realised, and thus require mitigation. In this paper, we present a reference architecture (RA) that can be used to abstractly model in situ applications of this new space landscape. The RA specifies high-level system components and their interactions. By instantiating the RA for two scenarios we demonstrate how to analyse the attack surface using attack trees
Tracking and modeling focus of attention in meetings [online]
Abstract
This thesis addresses the problem of tracking the focus of
attention of people. In particular, a system to track the focus
of attention of participants in meetings is developed. Obtaining
knowledge about a person\u27s focus of attention is an important
step towards a better understanding of what people do, how and
with what or whom they interact or to what they refer. In
meetings, focus of attention can be used to disambiguate the
addressees of speech acts, to analyze interaction and for
indexing of meeting transcripts. Tracking a user\u27s focus of
attention also greatly contributes to the improvement of
humanÂcomputer interfaces since it can be used to build interfaces
and environments that become aware of what the user is paying
attention to or with what or whom he is interacting.
The direction in which people look; i.e., their gaze, is closely
related to their focus of attention. In this thesis, we estimate
a subject\u27s focus of attention based on his or her head
orientation. While the direction in which someone looks is
determined by head orientation and eye gaze, relevant literature
suggests that head orientation alone is a su#cient cue for the
detection of someone\u27s direction of attention during social
interaction. We present experimental results from a user study
and from several recorded meetings that support this hypothesis.
We have developed a Bayesian approach to model at whom or what
someone is look ing based on his or her head orientation. To
estimate head orientations in meetings, the participants\u27 faces
are automatically tracked in the view of a panoramic camera and
neural networks are used to estimate their head orientations
from preÂprocessed images of their faces. Using this approach,
the focus of attention target of subjects could be correctly
identified during 73% of the time in a number of evaluation meetÂ
ings with four participants.
In addition, we have investigated whether a person\u27s focus of
attention can be preÂdicted from other cues. Our results show
that focus of attention is correlated to who is speaking in a
meeting and that it is possible to predict a person\u27s focus of
attention
based on the information of who is talking or was talking before
a given moment.
We have trained neural networks to predict at whom a person is
looking, based on information about who was speaking. Using this
approach we were able to predict who is looking at whom with 63%
accuracy on the evaluation meetings using only information about
who was speaking. We show that by using both head orientation
and speaker information to estimate a person\u27s focus, the
accuracy of focus detection can be improved compared to just
using one of the modalities for focus estimation.
To demonstrate the generality of our approach, we have built a
prototype system to demonstrate focusÂaware interaction with a
household robot and other smart appliances in a room using the
developed components for focus of attention tracking. In the
demonstration environment, a subject could interact with a
simulated household robot, a speechÂenabled VCR or with other
people in the room, and the recipient of the subject\u27s speech
was disambiguated based on the user\u27s direction of attention.
Zusammenfassung
Die vorliegende Arbeit beschäftigt sich mit der automatischen
Bestimmung und VerÂfolgung des Aufmerksamkeitsfokus von Personen
in Besprechungen.
Die Bestimmung des Aufmerksamkeitsfokus von Personen ist zum
Verständnis und zur automatischen Auswertung von
Besprechungsprotokollen sehr wichtig. So kann damit
beispielsweise herausgefunden werden, wer zu einem bestimmten
Zeitpunkt wen angesprochen hat beziehungsweise wer wem zugehört
hat. Die automatische BestimÂmung des Aufmerksamkeitsfokus kann
desweiteren zur Verbesserung von Mensch-MaschineÂSchnittstellen
benutzt werden.
Ein wichtiger Hinweis auf die Richtung, in welche eine Person
ihre Aufmerksamkeit richtet, ist die Kopfstellung der Person.
Daher wurde ein Verfahren zur Bestimmung der Kopfstellungen von
Personen entwickelt. Hierzu wurden kĂĽnstliche neuronale Netze
benutzt, welche als Eingaben vorverarbeitete Bilder des Kopfes
einer Person erhalten, und als Ausgabe eine Schätzung der
Kopfstellung berechnen. Mit den trainierten Netzen wurde auf
Bilddaten neuer Personen, also Personen, deren Bilder nicht in
der Trainingsmenge enthalten waren, ein mittlerer Fehler von
neun bis zehn Grad fĂĽr die Bestimmung der horizontalen und
vertikalen Kopfstellung erreicht.
Desweiteren wird ein probabilistischer Ansatz zur Bestimmung von
AufmerksamkeitsÂzielen vorgestellt. Es wird hierbei ein
Bayes\u27scher Ansatzes verwendet um die AÂposterior
iWahrscheinlichkeiten verschiedener Aufmerksamkteitsziele,
gegeben beobachteter Kopfstellungen einer Person, zu bestimmen.
Die entwickelten Ansätze wurden auf mehren Besprechungen mit
vier bis fĂĽnf Teilnehmern evaluiert.
Ein weiterer Beitrag dieser Arbeit ist die Untersuchung,
inwieweit sich die BlickrichÂtung der Besprechungsteilnehmer
basierend darauf, wer gerade spricht, vorhersagen läßt. Es wurde
ein Verfahren entwickelt um mit Hilfe von neuronalen Netzen den
Fokus einer Person basierend auf einer kurzen Historie der
Sprecherkonstellationen zu schätzen.
Wir zeigen, dass durch Kombination der bildbasierten und der
sprecherbasierten Schätzung des Aufmerksamkeitsfokus eine
deutliche verbesserte Schätzung erreicht werden kann.
Insgesamt wurde mit dieser Arbeit erstmals ein System
vorgestellt um automatisch die Aufmerksamkeit von Personen in
einem Besprechungsraum zu verfolgen.
Die entwickelten Ansätze und Methoden können auch zur Bestimmung
der AufmerkÂsamkeit von Personen in anderen Bereichen,
insbesondere zur Steuerung von computÂerisierten, interaktiven
Umgebungen, verwendet werden. Dies wird an einer
Beispielapplikation gezeigt
Development of artificial neural network-based object detection algorithms for low-cost hardware devices
Finally, the fourth work was published in the “WCCI” conference in 2020 and consisted of an individuals' position estimation algorithm based on a novel neural network model for environments with forbidden regions, named “Forbidden Regions Growing Neural Gas”.The human brain is the most complex, powerful and versatile learning machine ever known. Consequently, many scientists of various disciplines are fascinated by its structures and information processing methods. Due to the quality and quantity of the information extracted from the sense of sight, image is one of the main information channels used by humans. However, the massive amount of video footage generated nowadays makes it difficult to process those data fast enough manually. Thus, computer vision systems represent a fundamental tool in the extraction of information from digital images, as well as a major challenge for scientists and engineers.
This thesis' primary objective is automatic foreground object detection and classification through digital image analysis, using artificial neural network-based techniques, specifically designed and optimised to be deployed in low-cost hardware devices. This objective will be complemented by developing individuals' movement estimation methods by using unsupervised learning and artificial neural network-based models.
The cited objectives have been addressed through a research work illustrated in four publications supporting this thesis. The first one was published in the “ICAE” journal in 2018 and consists of a neural network-based movement detection system for Pan-Tilt-Zoom (PTZ) cameras deployed in a Raspberry Pi board. The second one was published in the “WCCI” conference in 2018 and consists of a deep learning-based automatic video surveillance system for PTZ cameras deployed in low-cost hardware. The third one was published in the “ICAE” journal in 2020 and consists of an anomalous foreground object detection and classification system for panoramic cameras, based on deep learning and supported by low-cost hardware
Hierarchical Bi-Directional Feature Perception Network for Person Re-Identification
Previous Person Re-Identification (Re-ID) models aim to focus on the most
discriminative region of an image, while its performance may be compromised
when that region is missing caused by camera viewpoint changes or occlusion. To
solve this issue, we propose a novel model named Hierarchical Bi-directional
Feature Perception Network (HBFP-Net) to correlate multi-level information and
reinforce each other. First, the correlation maps of cross-level feature-pairs
are modeled via low-rank bilinear pooling. Then, based on the correlation maps,
Bi-directional Feature Perception (BFP) module is employed to enrich the
attention regions of high-level feature, and to learn abstract and specific
information in low-level feature. And then, we propose a novel end-to-end
hierarchical network which integrates multi-level augmented features and inputs
the augmented low- and middle-level features to following layers to retrain a
new powerful network. What's more, we propose a novel trainable generalized
pooling, which can dynamically select any value of all locations in feature
maps to be activated. Extensive experiments implemented on the mainstream
evaluation datasets including Market-1501, CUHK03 and DukeMTMC-ReID show that
our method outperforms the recent SOTA Re-ID models.Comment: Accepted by ACM MM202
Development of new intelligent autonomous robotic assistant for hospitals
Continuous technological development in modern societies has increased the quality of life and average life-span of people. This imposes an extra burden on the current healthcare infrastructure, which also creates the opportunity for developing new, autonomous, assistive robots to help alleviate this extra workload.
The research question explored the extent to which a prototypical robotic platform can be created and how it may be implemented in a hospital environment with the aim to assist the hospital staff with daily tasks, such as guiding patients and visitors, following patients to ensure safety, and making deliveries to and from rooms and workstations.
In terms of major contributions, this thesis outlines five domains of the development of an actual robotic assistant prototype. Firstly, a comprehensive schematic design is presented in which mechanical, electrical, motor control and kinematics solutions have been examined in detail. Next, a new method has been proposed for assessing the intrinsic properties of different flooring-types using machine learning to classify mechanical vibrations. Thirdly, the technical challenge of enabling the robot to simultaneously map and localise itself in a dynamic environment has been addressed, whereby leg detection is introduced to ensure that, whilst mapping, the robot is able to distinguish between people and the background. The fourth contribution is geometric collision prediction into stabilised dynamic navigation methods, thus optimising the navigation ability to update real-time path planning in a dynamic environment. Lastly, the problem of detecting gaze at long distances has been addressed by means of a new eye-tracking hardware solution which combines infra-red eye tracking and depth sensing.
The research serves both to provide a template for the development of comprehensive mobile assistive-robot solutions, and to address some of the inherent challenges currently present in introducing autonomous assistive robots in hospital environments.Open Acces
Exploring the use of speech in audiology: A mixed methods study
This thesis aims to advance the understanding of how speech testing is, and can be, used for hearing device users within the audiological test battery. To address this, I engaged with clinicians and patients to understand the current role that speech testing plays in audiological testing in the UK, and developed a new listening test, which combined speech testing with localisation judgments in a dual task design. Normal hearing listeners and hearing aid users were tested, and a series of technical measurements were made to understand how advanced hearing aid settings might determine task performance.
A questionnaire was completed by public and private sector hearing healthcare professionals in the UK to explore the use of speech testing. Overall, results revealed this assessment tool was underutilised by UK clinicians, but there was a significantly greater use in the private sector. Through a focus group and semi structured interviews with hearing aid users I identified a mismatch between their common listening difficulties and the assessment tools used in audiology and highlighted a lack of deaf awareness in UK adult audiology.
The Spatial Speech in Noise Test (SSiN) is a dual task paradigm to simultaneously assess relative localisation and word identification performance. Testing on normal hearing listeners to investigate the impact of the dual task design found the SSiN to increase cognitive load and therefore better reflect challenging listening situations. A comparison of relative localisation and word identification performance showed that hearing aid users benefitted less from spatially separating speech and noise in the SSiN than normal hearing listeners. To investigate how the SSiN could be used to assess advanced hearing aid features, a subset of hearing aid users were fitted with the same hearing aid type and completed the SSiN once with adaptive directionality and once with omnidirectionality. The SSiN results differed between conditions but a larger sample size is needed to confirm these effects. Hearing aid technical measurements were used to quantify how hearing aid output changed in response to the SSiN paradigm
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