1,720,973 research outputs found
Ultrasound measurements to monitor the specific gravity of food batters
No full textThis paper describes the design and application of a low cost ultrasound system to monitor the specific gravity of batter as it is mixed. The quantity of air is believed to be the main factor in determining the quality of the finished product and specific gravity measurement is common for assessing the quality and progress of the mixing process. A probe is designed to allow measurements in reflection and the relationship of ultrasound gain to specific gravity determined theoretically and justified experimentally. Operation was conducted in pulsed mode using a nominally 2.25 MHz, 15 mm diameter transducer. The work may have application to measurements on industrial sludges in which similar material properties are observed
Measuring Arm Reach-and-Grasp Movements with Accelerometers in Rehabilitation: A Kalman Filter to Stabilise Velocity Estimate
No full textThe reality of homes fit for heroes: design challenges for rehabilitation technology at home.
No full textIt is widely accepted that rigorous rehabilitation exercises after a stroke can help restore some functionality. However for many patients, this means exercises at home with minimal, if any, clinician support. Technologies that help motivate and promote good exercises offer significant potential but need to be designed to realistically take account of real homes and real lives of the people who have had a stroke. As part of the Motivating Mobility project, we carried out a series of visits to homes of people living with stroke and photographed their homes. In contrast to many utopian smart home scenarios, the elderly of today live in homes that were built as homes fit for heroes' but have been evolved and adapted over time and present significant challenges for the design of in-home rehabilitation technologies. These challenges include the uses and repurposing of use of rooms, attitudes to and uses of existing technologies, space available in the home, feelings about different spaces within homes and individual preferences and interests. The findings provide a set of sensitivities that will help shape and frame ongoing design work for the successful deployment of rehabilitation technologies in real homes
Learning from sonar data for the classification of underwater seabeds
Full text linkThe increased use of sonar surveys for both industrial and leisure activities has motivated the research for cost effective, automated processed for seabed classification. Seabed classification is essential for many fields including dredging, environmental studies, fisheries research, pipeline and cable route surveys, marine archaeology and automated underwater vehicles. The advancement in both sonar technology and sonar data storage has led to large quantities of sonar data being collected per survey. The challenge, however, is to derive relevant features that can summarise these large amounts of data and provide discrimination between several seabed types present in each survey.The main aim of this work is to classify sidescan bathymetric datasets. However, in most sidescan bathymetric surveys, only a few ground-truthed areas (if any) are available. Since sidescan ‘ground-truthed’ areas were also provided for this work, they were used to test feature extraction, selection and classification algorithms. Backscattering amplitude, after using bathymetric data to correct for variations, did not provide enough discrimination between sediment classes in this work which lead to the investigation of other features. The variation of backscattering amplitude at different scales corresponds to variations in both micro bathymetry and large scale bathymetry. A method that can derive multiscale features from signals was needed, and the wavelet method proved to be an efficient method of doing so. Wavelets are used for feature extraction in 1D sidescan bathymetry survey data and both the feature selection and classification stages are automated. The method is tested on areas of known types and in general, the features show good correlation with sediment types in both types of survey.The main disadvantage of this method, however, is that signal futures are calculated per swathe (or received signal). Thus, sediment boundaries within the same swathe are not detected. To solve this problem, information present in consecutive pings of data can be used, leading to 2-D feature extraction.Several textural classification methods are investigated for the segmentation of sidescan sonar images. The method includes 2D wavelets and Gabor filters. Effects of filter orientation filter scale and window size are observed in both cases, and validated on given sonar images.For sidescan bathymetric datasets, a novel method of classification using both sidescan images and depth maps is investigated. Backscattering amplitude and bathymetry images are both used for feature extraction. Features include amplitude-dependent features, textural features and bathymetric variation features. The method makes use of grab samples available in given areas of the survey for training the classifiers. Alternatively, clustering techniques are used to group the data. The results of applying the method on sidescan bathymetric surveys correlate with the grab samples available as well as the user-classified areas.An automatic method for sidescan bathymetric classification offers a cost effective approach to classify large areas of seabed with a fewer number of grab samples. This work sheds light on areas of feature extraction, selection and classification of sonar data
Use of inertial sensors to measure upper limb motion: application in stroke rehabilitation
Full text linkStroke is the largest cause of severe adult complex disability, caused when the blood supply to the brain is interrupted, either by a clot or a burst blood vessel. It is characterised by deficiencies in movement and balance, changes in sensation, impaired motor control and muscle tone, and bone deformity. Clinically applied stroke management relies heavily on the observational opinion of healthcare workers. Despite the proven validity of a few clinical outcome measures, they remain subjective and inconsistent, and suffer from a lack of standardisation. Motion capture of the upper limb has also been used in specialised laboratories to obtain accurate and objective information, and monitor progress in rehabilitation. However, it is unsuitable in environments that are accessible to stroke patients (for example at patients’ homes or stroke clubs), due to the high cost, special set-up and calibration requirements.The aim of this research project was to validate and assess the sensitivity of a relatively low cost, wearable, compact and easy-to-use monitoring system, which uses inertial sensors in order to obtain detailed analysis of the forearm during simple functional exercises, typically used in rehabilitation. Forearm linear and rotational motion were characterised for certain movements on four healthy subjects and a stroke patient using a motion capture system. This provided accuracy and sensitivity specifications for the wearable monitoring system. With basic signal pre-processing, the wearable system was found to report reliably on acceleration, angular velocity and orientation, with varying degrees of confidence. Integration drift errors in the estimation of linear velocity were unresolved. These errors were not straightforward to eliminate due to the varying position of the sensor accelerometer relative to gravity over time. The cyclic nature of rehabilitation exercises was exploited to improve the reliability of velocity estimation with model-based Kalman filtering, and least squares optimisation techniques. Both signal processing methods resulted in an encouraging reduction of the integration drift in velocity. Improved sensor information could provide a visual display of the movement, or determine kinematic quantities relevant to the exercise performance. Hence, the system could potentially be used to objectively inform patients and physiotherapists about progress, increasing patient motivation and improving consistency in assessment and reporting of outcomes
Temporal dynamics of resting state brain connectivity as revealed by magnetoencephalography
Full text linkExplorations into the organisation of spontaneous activity within the brain have demonstrated the existence of networks of temporally correlated activity, consisting of brain areas that share similar cognitive or sensory functions. These so-called resting state networks (RSNs) emerge spontaneously during rest and disappear in response to overt stimuli or cognitive demands. In recent years, the study of RSNs has emerged as a valuable tool for probing brain function, both in the healthy brain and in disorders such as schizophrenia, Alzheimer’s disease and Parkinson’s disease. However, analyses of these networks have so far been limited, in part due to assumptions that the patterns of neuronal activity that underlie these networks remain constant over time. Moreover, the majority of RSN studies have used functional magnetic resonance imaging (fMRI), in which slow fluctuations in the level of oxygen in the blood are used as a proxy for the activity within a given brain region. In this thesis we develop the use of magnetoencephalography (MEG) to study resting state functional connectivity. Unlike fMRI, MEG provides a direct measure of neuronal activity and can provide novel insights into the temporal dynamics that underlie resting state activity. In particular, we focus on the application of non- stationary analysis methods, which are able to capture fast temporal changes in activity. We first develop a framework for preprocessing MEG data and measuring interactions within different RSNs (Chapter 3). We then extend this framework to assess temporal variability in resting state functional connectivity by applying time- varying measures of interactions and show that within-network functional connectivity is underpinned by non-stationary temporal dynamics (Chapter 4). Finally we develop a data driven approach based on a hidden Markov model for inferring short lived connectivity states from resting state and task data (Chapter 5). By applying this approach to data from multiple subjects we reveal transient states that capture short lived patterns of neuronal activity (Chapter 6)
A toolkit to explore lived experience of motivation: When words are not enough
Full text linkDesigning to support motivation is an increasingly important issue, especially as pervasive technologies are used to facilitate various healthy behaviour changes. There are many motivation theories but these do not map specifically to inform design. In ‘Motivating Mobility’ we explore the lived experiences of motivation of people with stroke, in order to design rehabilitation technologies. Motivation varies between people, between contexts and over time and can be ‘difficult to express’, particularly for those with communication problems. We describe development of a theoretically based toolkit, principled in both content and form, and using multiple modes of communication, aimed at gathering motivational requirements in order to inspire design. We show use of the toolkit, discuss the rich data collected and reflect on how well the approach works and ties requirements, via their elicitation tool, back to theory. This toolkit has potential to inform design for motivational effect in similar pervasive health applications
Ultrasound to assess lipid content in salmon muscle
Full text linkIn this thesis, ultrasound pulse transit time measurement techniques are applied to aquaculture, specifically to measure the intramuscular fat in salmon muscle tissue. The main advantages of this technique are that it is noninvasive and that it uses low-cost components.Fat in salmon muscle exists as oil dispersed throughout the tissue. Therefore, a phantom was built to empirically model a dispersed fat system. The phantom was a mixture of low-fat milk and high-fat double cream. By varying the quantities of each component, the fat level of the phantom could be controlled. A trend of increasing speed of sound and attenuation with fat content was observed. Prom velocity measurements at a single temperature, it was possible to predict the fat content of the mixture to within ±1.5% fat. A measurement system was created to measure the sample thickness and the speed of sound through a sample at the same time. Velocity and attenuation measurements were made on fifty samples of salmon muscle tissue containing two distinct fat ranges. A trend of decreasing speed of sound with fat content was observed. Further measurements were taken on twelve more samples and compared to the results of chemical fat analysis to determine the strength of the correlation between fat content and speed of sound through the samples. Again, a trend of decreasing speed of sound with increasing fat content was observed (r=0.73, 71=12). This trend was not as strong as that observed for the phantom due to natural variation in the structure of the tissue. A conclusion drawn from this part of the research is that it may be possible to group the data into "high fat", "medium fat" and "low fat" categories. Attenuation measurements proved too dependent on muscle structure to yield a correlation between attenuation and fat content.Ray-tracing techniques were used to model the propagation velocity of a wavefront travelling through a single salmon sample. The model provided an insight into how variations in temperature, fat content, myoseptum thickness and myosepta configuration affect measured velocity.This thesis provides an insight into how ultrasound velocity measurement may be used to assess the fat content of salmon white muscle tissue. It also provides a starting point for future work in which these techniques may be combined with a vision system to enable similar measurements on live fish
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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