268 research outputs found
Examining the Relationships Between Distance Education Students’ Self-Efficacy and Their Achievement
This study aimed to examine the relationships between students’ self-efficacy (SSE) and students’ achievement (SA) in distance education. The instruments were administered to 100 undergraduate students in a distance university who work as migrant workers in Taiwan to gather data, while their SA scores were obtained from the university. The semi-structured interviews for 8 participants consisted of questions that showed the specific conditions of SSE and SA. The findings of this study were reported as follows: There was a significantly positive correlation between targeted SSE (overall scales and general self-efficacy) and SA. Targeted students' self-efficacy effectively predicted their achievement; besides, general self- efficacy had the most significant influence. In the qualitative findings, four themes were extracted for those students with lower self-efficacy but higher achievement—physical and emotional condition, teaching and learning strategy, positive social interaction, and intrinsic motivation. Moreover, three themes were extracted for those students with moderate or higher self-efficacy but lower achievement—more time for leisure (not hard-working), less social interaction, and external excuses. Providing effective learning environments, social interactions, and teaching and learning strategies are suggested in distance education
New techniques for neuro-rehabilitation: Transcranial Electric Stimulation and Virtual Reality
Recovery of motor and cognitive performances after a neurological illness remains a significant challenge for rehabilitation specialists. The traditional rehabilitative interventions are usually delivered using a multidisciplinary
approach, whose results are not always satisfactory. These limitations in functional recovery have led researchers
to consider alternative approaches. The hypothesis of providing new therapeutic possibilities in
the different patients treated is, as a rehabilitator, very rewarding and represents a challenge for the future. The application of simple and low-cost techniques, defined by the literature as "unconventional" or “novel”, can provide new ideas not only in the field of research but above all of application in clinical reality.A suitable approach to improve the rehabilitation outcome is to utilize these novel rehabilitation techniques that act as a substitute or an addition to the traditional ones. In this context, some recent approaches have been proposed that might increase the effectiveness of a traditional treatment. Among them, two techniques have been demonstrated to be very promising, namely non-invasive brain stimulation (NIBS) and Virtual Reality (VR).In light of the foregoing, my thesis has been divided into two main lines of research, namely: a) the study of the effects of transcranial direct current stimulation (tDCS) in different neurological conditions; b) the application of VR (used alone or combined with tDCS) in the treatment of some neurocognitive disorders. A semi-immersive VR tool (ReMOVES system) has been used as
a user-friendly platform providing activities based on exergames
Managing healthcare transformation towards P5 medicine (Published in Frontiers in Medicine)
Health and social care systems around the world are facing radical organizational, methodological and technological paradigm changes to meet the requirements for improving quality and safety of care as well as efficiency and efficacy of care processes. In this they’re trying to manage the challenges of ongoing demographic changes towards aging, multi-diseased societies, development of human resources, a health and social services consumerism, medical and biomedical progress, and exploding costs for health-related R&D as well as health services delivery. Furthermore, they intend to achieve sustainability of global health systems by transforming them towards intelligent, adaptive and proactive systems focusing on health and wellness with optimized quality and safety outcomes.
The outcome is a transformed health and wellness ecosystem combining the approaches of translational medicine, 5P medicine (personalized, preventive, predictive, participative precision medicine) and digital health towards ubiquitous personalized health services realized independent of time and location. It considers individual health status, conditions, genetic and genomic dispositions in personal social, occupational, environmental and behavioural context, thus turning health and social care from reactive to proactive. This requires the advancement communication and cooperation among the business actors from different domains (disciplines) with different methodologies, terminologies/ontologies, education, skills and experiences from data level (data sharing) to concept/knowledge level (knowledge sharing). The challenge here is the understanding and the formal as well as consistent representation of the world of sciences and practices, i.e. of multidisciplinary and dynamic systems in variable context, for enabling mapping between the different disciplines, methodologies, perspectives, intentions, languages, etc. Based on a framework for dynamically, use-case-specifically and context aware representing multi-domain ecosystems including their development process, systems, models and artefacts can be consistently represented, harmonized and integrated. The response to that problem is the formal representation of health and social care ecosystems through an system-oriented, architecture-centric, ontology-based and policy-driven model and framework, addressing all domains and development process views contributing to the system and context in question.
Accordingly, this Research Topic would like to address this change towards 5P medicine. Specifically, areas of interest include, but are not limited:
• A multidisciplinary approach to the transformation of health and social systems
• Success factors for sustainable P5 ecosystems
• AI and robotics in transformed health ecosystems
• Transformed health ecosystems challenges for security, privacy and trust
• Modelling digital health systems
• Ethical challenges of personalized digital health
• Knowledge representation and management of transformed health ecosystems
Table of Contents:
04 Editorial: Managing healthcare transformation towards P5
medicine
Bernd Blobel and Dipak Kalra
06 Transformation of Health and Social Care Systems—An
Interdisciplinary Approach Toward a Foundational
Architecture
Bernd Blobel, Frank Oemig, Pekka Ruotsalainen and Diego M. Lopez
26 Transformed Health Ecosystems—Challenges for Security,
Privacy, and Trust
Pekka Ruotsalainen and Bernd Blobel
36 Success Factors for Scaling Up the Adoption of Digital
Therapeutics Towards the Realization of P5 Medicine
Alexandra Prodan, Lucas Deimel, Johannes Ahlqvist, Strahil Birov,
Rainer Thiel, Meeri Toivanen, Zoi Kolitsi and Dipak Kalra
49 EU-Funded Telemedicine Projects – Assessment of, and
Lessons Learned From, in the Light of the SARS-CoV-2
Pandemic
Laura Paleari, Virginia Malini, Gabriella Paoli, Stefano Scillieri,
Claudia Bighin, Bernd Blobel and Mauro Giacomini
60 A Review of Artificial Intelligence and Robotics in
Transformed Health Ecosystems
Kerstin Denecke and Claude R. Baudoin
73 Modeling digital health systems to foster interoperability
Frank Oemig and Bernd Blobel
89 Challenges and solutions for transforming health ecosystems
in low- and middle-income countries through artificial
intelligence
Diego M. LĂłpez, Carolina Rico-Olarte, Bernd Blobel and Carol Hullin
111 Linguistic and ontological challenges of multiple domains
contributing to transformed health ecosystems
Markus Kreuzthaler, Mathias Brochhausen, Cilia Zayas, Bernd Blobel
and Stefan Schulz
126 The ethical challenges of personalized digital health
Els Maeckelberghe, Kinga Zdunek, Sara Marceglia, Bobbie Farsides
and Michael Rigb
Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design
Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data
Queensland University of Technology: Handbook 2023
The Queensland University of Technology handbook gives an outline of the faculties and subject offerings available that were offered by QUT
Optimizing AI at the Edge: from network topology design to MCU deployment
The first topic analyzed in the thesis will be Neural Architecture Search (NAS).
I will focus on two different tools that I developed, one to optimize the architecture of Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged, and one to optimize the data precision of tensors inside CNNs.
The first NAS proposed explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive field, and the number of features in each layer. Note that this is the first NAS that explicitly targets these networks.
The second NAS proposed instead focuses on finding the most efficient data format for a target CNN, with the granularity of the layer filter. Note that applying these two NASes in sequence allows an "application designer" to minimize the structure of the neural network employed, minimizing the number of operations or the memory usage of the network.
After that, the second topic described is the optimization of neural network deployment on edge devices. Importantly, exploiting edge platforms' scarce resources is critical for NN efficient execution on MCUs.
To do so, I will introduce DORY (Deployment Oriented to memoRY) -- an automatic tool to deploy CNNs on low-cost MCUs.
DORY, in different steps, can manage different levels of memory inside the MCU automatically, offload the computation workload (i.e., the different layers of a neural network) to dedicated hardware accelerators, and automatically generates ANSI C code that orchestrates off- and on-chip transfers with the computation phases.
On top of this, I will introduce two optimized computation libraries that DORY can exploit to deploy TCNs and Transformers on edge efficiently.
I conclude the thesis with two different applications on bio-signal analysis, i.e., heart rate tracking and sEMG-based gesture recognition
StairNetV3: Depth-aware Stair Modeling using Deep Learning
Vision-based stair perception can help autonomous mobile robots deal with the
challenge of climbing stairs, especially in unfamiliar environments. To address
the problem that current monocular vision methods are difficult to model stairs
accurately without depth information, this paper proposes a depth-aware stair
modeling method for monocular vision. Specifically, we take the extraction of
stair geometric features and the prediction of depth images as joint tasks in a
convolutional neural network (CNN), with the designed information propagation
architecture, we can achieve effective supervision for stair geometric feature
learning by depth information. In addition, to complete the stair modeling, we
take the convex lines, concave lines, tread surfaces and riser surfaces as
stair geometric features and apply Gaussian kernels to enable the network to
predict contextual information within the stair lines. Combined with the depth
information obtained by depth sensors, we propose a stair point cloud
reconstruction method that can quickly get point clouds belonging to the stair
step surfaces. Experiments on our dataset show that our method has a
significant improvement over the previous best monocular vision method, with an
intersection over union (IOU) increase of 3.4 %, and the lightweight version
has a fast detection speed and can meet the requirements of most real-time
applications. Our dataset is available at
https://data.mendeley.com/datasets/6kffmjt7g2/1
Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities
Robotics and Artificial Intelligence (AI) have been inextricably intertwined
since their inception. Today, AI-Robotics systems have become an integral part
of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These
systems are built upon three fundamental architectural elements: perception,
navigation and planning, and control. However, while the integration of
AI-Robotics systems has enhanced the quality our lives, it has also presented a
serious problem - these systems are vulnerable to security attacks. The
physical components, algorithms, and data that make up AI-Robotics systems can
be exploited by malicious actors, potentially leading to dire consequences.
Motivated by the need to address the security concerns in AI-Robotics systems,
this paper presents a comprehensive survey and taxonomy across three
dimensions: attack surfaces, ethical and legal concerns, and Human-Robot
Interaction (HRI) security. Our goal is to provide users, developers and other
stakeholders with a holistic understanding of these areas to enhance the
overall AI-Robotics system security. We begin by surveying potential attack
surfaces and provide mitigating defensive strategies. We then delve into
ethical issues, such as dependency and psychological impact, as well as the
legal concerns regarding accountability for these systems. Besides, emerging
trends such as HRI are discussed, considering privacy, integrity, safety,
trustworthiness, and explainability concerns. Finally, we present our vision
for future research directions in this dynamic and promising field
Networking Architecture and Key Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey
Digital twin (DT), refers to a promising technique to digitally and
accurately represent actual physical entities. One typical advantage of DT is
that it can be used to not only virtually replicate a system's detailed
operations but also analyze the current condition, predict future behaviour,
and refine the control optimization. Although DT has been widely implemented in
various fields, such as smart manufacturing and transportation, its
conventional paradigm is limited to embody non-living entities, e.g., robots
and vehicles. When adopted in human-centric systems, a novel concept, called
human digital twin (HDT) has thus been proposed. Particularly, HDT allows in
silico representation of individual human body with the ability to dynamically
reflect molecular status, physiological status, emotional and psychological
status, as well as lifestyle evolutions. These prompt the expected application
of HDT in personalized healthcare (PH), which can facilitate remote monitoring,
diagnosis, prescription, surgery and rehabilitation. However, despite the large
potential, HDT faces substantial research challenges in different aspects, and
becomes an increasingly popular topic recently. In this survey, with a specific
focus on the networking architecture and key technologies for HDT in PH
applications, we first discuss the differences between HDT and conventional
DTs, followed by the universal framework and essential functions of HDT. We
then analyze its design requirements and challenges in PH applications. After
that, we provide an overview of the networking architecture of HDT, including
data acquisition layer, data communication layer, computation layer, data
management layer and data analysis and decision making layer. Besides reviewing
the key technologies for implementing such networking architecture in detail,
we conclude this survey by presenting future research directions of HDT
- …