8,276 research outputs found
A review of abnormal behavior detection in activities of daily living
Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend
Rehabilitation Exercise Repetition Segmentation and Counting using Skeletal Body Joints
Physical exercise is an essential component of rehabilitation programs that
improve quality of life and reduce mortality and re-hospitalization rates. In
AI-driven virtual rehabilitation programs, patients complete their exercises
independently at home, while AI algorithms analyze the exercise data to provide
feedback to patients and report their progress to clinicians. To analyze
exercise data, the first step is to segment it into consecutive repetitions.
There has been a significant amount of research performed on segmenting and
counting the repetitive activities of healthy individuals using raw video data,
which raises concerns regarding privacy and is computationally intensive.
Previous research on patients' rehabilitation exercise segmentation relied on
data collected by multiple wearable sensors, which are difficult to use at home
by rehabilitation patients. Compared to healthy individuals, segmenting and
counting exercise repetitions in patients is more challenging because of the
irregular repetition duration and the variation between repetitions. This paper
presents a novel approach for segmenting and counting the repetitions of
rehabilitation exercises performed by patients, based on their skeletal body
joints. Skeletal body joints can be acquired through depth cameras or computer
vision techniques applied to RGB videos of patients. Various sequential neural
networks are designed to analyze the sequences of skeletal body joints and
perform repetition segmentation and counting. Extensive experiments on three
publicly available rehabilitation exercise datasets, KIMORE, UI-PRMD, and
IntelliRehabDS, demonstrate the superiority of the proposed method compared to
previous methods. The proposed method enables accurate exercise analysis while
preserving privacy, facilitating the effective delivery of virtual
rehabilitation programs.Comment: 8 pages, 1 figure, 2 table
Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review
In this paper, a critical bibliometric analysis study is conducted, coupled
with an extensive literature survey on recent developments and associated
applications in machine learning research with a perspective on Africa. The
presented bibliometric analysis study consists of 2761 machine learning-related
documents, of which 98% were articles with at least 482 citations published in
903 journals during the past 30 years. Furthermore, the collated documents were
retrieved from the Science Citation Index EXPANDED, comprising research
publications from 54 African countries between 1993 and 2021. The bibliometric
study shows the visualization of the current landscape and future trends in
machine learning research and its application to facilitate future
collaborative research and knowledge exchange among authors from different
research institutions scattered across the African continent
Evaluation of image quality and reconstruction parameters in recent PET-CT and PET-MR systems
In this PhD dissertation, we propose to evaluate the impact of using different PET isotopes for
the National Electrical Manufacturers Association (NEMA) tests performance evaluation of the
GE Signa integrated PET/MR. The methods were divided into three closely related categories:
NEMA performance measurements, system modelling and evaluation of the image quality of
the state-of-the-art of clinical PET scanners. NEMA performance measurements for
characterizing spatial resolution, sensitivity, image quality, the accuracy of attenuation and
scatter corrections, and noise equivalent count rate (NECR) were performed using clinically
relevant and commercially available radioisotopes. Then we modelled the GE Signa integrated
PET/MR system using a realistic GATE Monte Carlo simulation and validated it with the result of
the NEMA measurements (sensitivity and NECR). Next, the effect of the 3T MR field on the
positron range was evaluated for F-18, C-11, O-15, N-13, Ga-68 and Rb-82. Finally, to evaluate the image
quality of the state-of-the-art clinical PET scanners, a noise reduction study was performed
using a Bayesian Penalized-Likelihood reconstruction algorithm on a time-of-flight PET/CT
scanner to investigate whether and to what extent noise can be reduced. The outcome of this
thesis will allow clinicians to reduce the PET dose which is especially relevant for young
patients. Besides, the Monte Carlo simulation platform for PET/MR developed for this thesis will
allow physicists and engineers to better understand and design integrated PET/MR systems
Targeting Fusion Proteins of HIV-1 and SARS-CoV-2
Viruses are disease-causing pathogenic agents that require host cells to replicate. Fusion of host and viral membranes is critical for the lifecycle of enveloped viruses. Studying viral fusion proteins can allow us to better understand how they shape immune responses and inform the design of therapeutics such as drugs, monoclonal antibodies, and vaccines. This thesis discusses two approaches to targeting two fusion proteins: Env from HIV-1 and S from SARS-CoV-2. The first chapter of this thesis is an introduction to viruses with a specific focus on HIV-1 CD4 mimetic drugs and antibodies against SARS-CoV-2. It discusses the architecture of these viruses and fusion proteins and how small molecules, peptides, and antibodies can target these proteins successfully to treat and prevent disease. In addition, a brief overview is included of the techniques involved in structural biology and how it has informed the study of viruses. For the interested reader, chapter 2 contains a review article that serves as a more in-depth introduction for both viruses as well as how the use of structural biology has informed the study of viral surface proteins and neutralizing antibody responses to them. The subsequent chapters provide a body of work divided into two parts. The first part in chapter 3 involves a study on conformational changes induced in the HIV-1 Env protein by CD4-mimemtic drugs using single particle cryo-EM. The second part encompassing chapters 4 and 5 includes two studies on antibodies isolated from convalescent COVID-19 donors. The former involves classification of antibody responses to the SARS-CoV-2 S receptor-binding domain (RBD). The latter discusses an anti-RBD antibody class that binds to a conserved epitope on the RBD and shows cross-binding and cross-neutralization to other coronaviruses in the sarbecovirus subgenus.</p
Proof of Concept of Therapeutic Gene Modulation of MBNL1/2 in Myotonic Dystrophy
La distrofia miotónica tipo 1 es una enfermedad genética rara multisistémica que afecta a 1 de cada 3000-8000 personas. La causa molecular de la enfermedad proviene de repeticiones tóxicas “CTG” en el gen DMPK (DM Protein Kinase). Tras la transcripción, estas repeticiones forman una estructura de horquilla que se une con alta afinidad a la familia de proteínas MBNL (Muscleblind-like) que agota su función de regulación de la poliadenilación y el splicing alternativo postranscripcional en numerosos transcritos. La pérdida de función de MBNL provoca una cascada de efectos posteriores, que eventualmente conducen a síntomas clínicos que incluyen miotonía, debilidad y atrofia muscular, cataratas, disfunción cardíaca y trastorno cognitivo. La restauración de la función de la proteína MBNL es clave para aliviar los síntomas debilitantes de esta enfermedad. Se han utilizado oligonucleótidos antisentido (AON) para apuntar a las repeticiones de DMPK y liberar MBNL del secuestro, lo que da como resultado resultados terapéuticos prometedores en modelos celulares y animales de la enfermedad. Otro factor que interviene en la pérdida de función de las proteínas MBNL son los miRNAs que regulan su traducción. Aquí se muestra el uso de AON dirigidos a la actividad de miR-23b y miR-218, que se ha demostrado previamente que regulan directamente MBNL1 y MBNL2. Estos antimiRs recibieron modificaciones FANA para aumentar su entrega en las células y reducir la toxicidad. También se probaron los AON, denominados blockmiRs, que se unen de manera complementaria a los sitios de unión confirmados de miR-23b y miR-218 en los 3'-UTR de las transcripciones de MBNL1 y MBNL2. De esta manera, los miRNAs no pueden unirse y regular la traducción de MBNL, lo que aumenta la cantidad de proteína MBNL producida en una célula deficiente. Aquí se propone el uso de AON de nuevo diseño dirigidos a la actividad de miR-23b y miR-218 para regular MBNL1 y MBNL2 a través de (1) exploración del bloqueo de miRNA a través de FANA-antimiR AON in vitro, (2) exploración del bloqueo del sitio de unión de miRNA a través de la estrategia blockmiR in vitro e in vivo con el uso de modificaciones químicas de LNA, y (3) mejora de la química de la estrategia blockmiR mediante el uso de tecnología de péptidos de penetración celular in vitro e in vivo.Myotonic Dystrophy Type 1 is a multi-systemic rare genetic disease affecting 1 in 3000-8000 people. The molecular cause of the disease stems from toxic “CTG” repetitions in the DMPK (DM Protein Kinase) gene. Upon transcription, these repetitions form a hairpin structure that binds with high affinity to the MBNL (Muscleblind-like) family of proteins depleting their function of post-transcriptional alternative splicing and polyadenylation regulation on numerous transcripts. MBNL loss-of-function causes a cascade of downstream effects, which eventually lead to clinical symptoms including myotonia, muscle weakness and atrophy, cataracts, cardiac dysfunction, and cognitive disorder. The restoration of MBNL protein function is key to relieving the debilitating symptoms of this disease. Antisense oligonucleotides (AONs) have been used to target the DMPK repeats and release MBNL from sequestration resulting in promising therapeutic results in cellular and animal models of the disease. Another factor playing a role in the loss-of-function of MBNL proteins are the miRNAs that regulate their translation. Here is shown the use of AONs targeting miR-23b and miR-218 activity, which have been previously shown to directly regulate MBNL1 and MBNL2. These antimiRs were given FANA modifications to increase their delivery in cells and lower toxicity. Also tested are AONs, termed blockmiRs, that complementary bind to the confirmed binding sites of miR-23b and miR-218 in the 3’-UTRs of MBNL1 and MBNL2 transcripts. In this way, the miRNAs are unable to bind and regulate the translation of MBNL thereby augmenting the amount of MBNL protein made in an otherwise deficient cell. Proposed here is the use of newly designed AONs targeting miR-23b and miR-218 activity in order to regulate MBNL1 and MBNL2 through (1) exploration of miRNA blocking through FANA-antimiR AONs in vitro, (2) exploration of miRNA binding site blocking through blockmiR strategy in vitro and in vivo with the use of LNA chemical modifications, and (3) improvement of the chemistry of the blockmiR strategy through the use of cell penetrating peptide technology in vitro and in vivo
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Brain signal recognition using deep learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityBrain Computer Interface (BCI) has the potential to offer a new generation of applications independent of
muscular activity and controlled by the human brain. Brain imaging technologies are used to transfer the
cognitive tasks into control commands for a BCI system. The electroencephalography (EEG) technology
serves as the best available non-invasive solution for extracting signals from the brain. On the other hand,
speech is the primary means of communication, but for patients suffering from locked-in syndrome, there
is no easy way to communicate. Therefore, an ideal communication system for locked-in patients is a
thought-to-speech BCI system.
This research aims to investigate methods for the recognition of imagined speech from EEG signals
using deep learning techniques. In order to design an optimal imagined speech recognition BCI, variety
of issues have been solved. These include 1) proposing new feature extraction and classification
framework for recognition of imagined speech from EEG signals, 2) grammatical class recognition of
imagined words from EEG signals, 3) discriminating different cognitive tasks associated with speech in
the brain such as overt speech, covert speech, and visual imagery. In this work machine learning, deep
learning methods were used to analyze EEG signals.
For recognition of imagined speech from EEG signals, a new EEG database was collected while the
participants mentally spoke (imagined speech) the presented words. Along with imagined speech, EEG
data was recorded for visual imagery (imagining a scene or an image) and overt speech (verbal speech).
Spectro-temporal and spatio-temporal domain features were investigated for the classification of imagined
words from EEG signals. Further, a deep learning framework using the convolutional network
and attention mechanism was implemented for learning features in the spatial, temporal, and spectral
domains. The method achieved a recognition rate of 76.6% for three binary word pairs. These experiments
show that deep learning algorithms are ideal for imagined speech recognition from EEG signals
due to their ability to interpret features from non-linear and non-stationary signals. Grammatical classes
of imagined words from EEG signals were also recognized using a multi-channel convolution network
framework. This method was extended to a multi-level recognition system for multi-class classification
of imagined words which achieved an accuracy of 52.9% for 10 words, which is much better in
comparison to previous work.
In order to investigate the difference between imagined speech with verbal speech and visual imagery
from EEG signals, we used multivariate pattern analysis (MVPA). MVPA provided the time segments
when the neural oscillation for the different cognitive tasks was linearly separable. Further, frequencies
that result in most discrimination between the different cognitive tasks were also explored. A framework
was proposed to discriminate two cognitive tasks based on the spatio-temporal patterns in EEG signals.
The proposed method used the K-means clustering algorithm to find the best electrode combination and
convolutional-attention network for feature extraction and classification. The proposed method achieved
a high recognition rate of 82.9% and 77.7%.
The results in this research suggest that a communication based BCI system can be designed using
deep learning methods. Further, this work add knowledge to the existing work in the field of communication
based BCI system
Linguistic- and Acoustic-based Automatic Dementia Detection using Deep Learning Methods
Dementia can affect a person's speech and language abilities, even in the early stages. Dementia is incurable, but early detection can enable treatment that can slow down and maintain mental function. Therefore, early diagnosis of dementia is of great importance. However, current dementia detection procedures in clinical practice are expensive, invasive, and sometimes inaccurate. In comparison, computational tools based on the automatic analysis of spoken language have the potential to be applied as a cheap, easy-to-use, and objective clinical assistance tool for dementia detection.
In recent years, several studies have shown promise in this area. However, most studies focus heavily on the machine learning aspects and, as a consequence, often lack sufficient incorporation of clinical knowledge. Many studies also concentrate on clinically less relevant tasks such as the distinction between HC and people with AD which is relatively easy and therefore less interesting both in terms of the machine learning and the clinical application.
The studies in this thesis concentrate on automatically identifying signs of neurodegenerative dementia in the early stages and distinguishing them from other clinical, diagnostic categories related to memory problems: (FMD, MCI, and HC). A key focus, when designing the proposed systems has been to better consider (and incorporate) currently used clinical knowledge and also to bear in mind how these machine-learning based systems could be translated for use in real clinical settings.
Firstly, a state-of-the-art end-to-end system is constructed for extracting linguistic information from automatically transcribed spontaneous speech. The system's architecture is based on hierarchical principles thereby mimicking those used in clinical practice where information at both word-, sentence- and paragraph-level is used when extracting information to be used for diagnosis. Secondly, hand-crafted features are designed that are based on clinical knowledge of the importance of pausing and rhythm. These are successfully joined with features extracted from the end-to-end system. Thirdly, different classification tasks are explored, each set up so as to represent the types of diagnostic decision-making that is relevant in clinical practice. Finally, experiments are conducted to explore how to better deal with the known problem of confounding and overlapping symptoms on speech and language from age and cognitive decline. A multi-task system is constructed that takes age into account while predicting cognitive decline. The studies use the publicly available DementiaBank dataset as well as the IVA dataset, which has been collected by our collaborators at the Royal Hallamshire Hospital, UK. In conclusion, this thesis proposes multiple methods of using speech and language information for dementia detection with state-of-the-art deep learning technologies, confirming the automatic system's potential for dementia detection
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