4,682 research outputs found

    Speaker verification with long-term ageing data

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    Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

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    Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

    Factors Affecting the Performance of Automated Speaker Verification in Alzheimer's Disease Clinical Trials

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    Detecting duplicate patient participation in clinical trials is a major challenge because repeated patients can undermine the credibility and accuracy of the trial's findings and result in significant health and financial risks. Developing accurate automated speaker verification (ASV) models is crucial to verify the identity of enrolled individuals and remove duplicates, but the size and quality of data influence ASV performance. However, there has been limited investigation into the factors that can affect ASV capabilities in clinical environments. In this paper, we bridge the gap by conducting analysis of how participant demographic characteristics, audio quality criteria, and severity level of Alzheimer's disease (AD) impact the performance of ASV utilizing a dataset of speech recordings from 659 participants with varying levels of AD, obtained through multiple speech tasks. Our results indicate that ASV performance: 1) is slightly better on male speakers than on female speakers; 2) degrades for individuals who are above 70 years old; 3) is comparatively better for non-native English speakers than for native English speakers; 4) is negatively affected by clinician interference, noisy background, and unclear participant speech; 5) tends to decrease with an increase in the severity level of AD. Our study finds that voice biometrics raise fairness concerns as certain subgroups exhibit different ASV performances owing to their inherent voice characteristics. Moreover, the performance of ASV is influenced by the quality of speech recordings, which underscores the importance of improving the data collection settings in clinical trials.Comment: Accepted to the 5th Clinical Natural Language Processing Workshop (ClinicalNLP) at ACL 202

    Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

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    Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

    The Use of EEG Signals For Biometric Person Recognition

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    This work is devoted to investigating EEG-based biometric recognition systems. One potential advantage of using EEG signals for person recognition is the difficulty in generating artificial signals with biometric characteristics, thus making the spoofing of EEG-based biometric systems a challenging task. However, more works needs to be done to overcome certain drawbacks that currently prevent the adoption of EEG biometrics in real-life scenarios: 1) usually large number of employed sensors, 2) still relatively low recognition rates (compared with some other biometric modalities), 3) the template ageing effect. The existing shortcomings of EEG biometrics and their possible solutions are addressed from three main perspectives in the thesis: pre-processing, feature extraction and pattern classification. In pre-processing, task (stimuli) sensitivity and noise removal are investigated and discussed in separated chapters. For feature extraction, four novel features are proposed; for pattern classification, a new quality filtering method, and a novel instance-based learning algorithm are described in respective chapters. A self-collected database (Mobile Sensor Database) is employed to investigate some important biometric specified effects (e.g. the template ageing effect; using low-cost sensor for recognition). In the research for pre-processing, a training data accumulation scheme is developed, which improves the recognition performance by combining the data of different mental tasks for training; a new wavelet-based de-noising method is developed, its effectiveness in person identification is found to be considerable. Two novel features based on Empirical Mode Decomposition and Hilbert Transform are developed, which provided the best biometric performance amongst all the newly proposed features and other state-of-the-art features reported in the thesis; the other two newly developed wavelet-based features, while having slightly lower recognition accuracies, were computationally more efficient. The quality filtering algorithm is designed to employ the most informative EEG signal segments: experimental results indicate using a small subset of the available data for feature training could receive reasonable improvement in identification rate. The proposed instance-based template reconstruction learning algorithm has shown significant effectiveness when tested using both the publicly available and self-collected databases

    How to Prepare for the Auditor

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    Unmet needs and current and future approaches for osteoporotic patients at high risk of hip fracture.

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    Summary: This review provides a critical analysis of currently available approaches to increase bone mass, structure and strength through drug therapy and of possible direct intraosseous interventions for the management of patients at imminent risk of hip fracture. Purpose : Osteoporotic hip fractures represent a particularly high burden in morbidity-, mortality- and health care-related costs. There are challenges and unmet needs in the early prevention of hip fractures, opening the perspective of new developments for the management of osteoporotic patients at imminent and/or at very high risk of hip fracture. Amongst them, preventive surgical intervention needs to be considered. Methods: A European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO)/International Osteoporosis Foundation (IOF) working group reviewed the presently available intervention modalities including preventive surgical options for hip fragility. This paper represents a summary of the discussions. Results: Prevention of hip fracture is currently based on regular physical activity; prevention of falls; correction of nutritional deficiencies, including vitamin D repletion; and pharmacological intervention. However, efficacy of these various measures to reduce hip fractures is at most 50% and may need months or years before becoming effective. To face the challenges of early prevention of hip fractures for osteoporotic patients at imminent and/or at very high risk of hip fracture, preventive surgical intervention needs further investigation. Conclusion: Preventive surgical intervention needs to be appraised for osteoporotic patients at imminent and/or at very high risk of hip fracture
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