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1دورية أكاديمية
المؤلفون: Stina Saunders, Joyce Gomes-Osman, Ali Jannati, Marissa Ciesla, Russell Banks, John Showalter, Graciela Muniz-Terrera, Saturnino Luz, Craig Ritchie, Álvaro Pascual-Leone
المصدر: Frontiers in Neurology, Vol 15 (2024)
مصطلحات موضوعية: patient reported outcomes, clinical meaningfulness, Alzheimer’s disease, mild cognitive impairment, outcome measures, Neurology. Diseases of the nervous system, RC346-429
الوصف: Incorporating person-centered outcomes into clinical trials for neurodegenerative diseases has been challenging due to a deficiency in quantitative measures. Meanwhile, the integration of personally meaningful treatment targets in clinical practice remains qualitative, failing to truly inform evaluations, therapeutic interventions and longitudinal monitoring and support. We discuss the current advances and future directions in capturing individualized brain health outcomes and present an approach to integrate person-centered outcome in a scalable manner. Our approach stems from the evidence-based electronic Person-Specific Outcome Measure (ePSOM) program which prompts an individual to define personally meaningful treatment priorities and report level of confidence in managing items that matter to the individual the most (e.g., “Do I feel confident in my ability to contribute to a conversation?”). Deployed either as a single version (person only) or a dyad version (person and care partner), our proposed tool could be used as an endpoint in clinical trials, offering proof of meaningful intervention benefits and in clinical practice, by establishing an anchor for the therapeutic objectives sought by the individual.
وصف الملف: electronic resource
العلاقة: https://www.frontiersin.org/articles/10.3389/fneur.2024.1387206/fullTest; https://doaj.org/toc/1664-2295Test
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2دورية أكاديمية
المؤلفون: Saturnino Luz, Fasih Haider, Davida Fromm, Ioulietta Lazarou, Ioannis Kompatsiaris, Brian MacWhinney
المصدر: IEEE Open Journal of Signal Processing, Vol 5, Pp 738-749 (2024)
مصطلحات موضوعية: Biomedical signal processing, medical conditions, Alzheimer's disease, human disease biomarkers, speech processing, natural language processing, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
الوصف: The ADReSS-M Signal Processing Grand Challenge was held at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023. The challenge targeted difficult automatic prediction problems of great societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD) and the estimation of cognitive test scoress. Participants were invited to create models for the assessment of cognitive function based on spontaneous speech data. Most of these models employed signal processing and machine learning methods. The ADReSS-M challenge was designed to assess the extent to which predictive models built based on speech in one language generalise to another language. The language data compiled and made available for ADReSS-M comprised English, for model training, and Greek, for model testing and validation. To the best of our knowledge no previous shared research task investigated acoustic features of the speech signal or linguistic characteristics in the context of multilingual AD detection. This paper describes the context of the ADReSS-M challenge, its data sets, its predictive tasks, the evaluation methodology we employed, our baseline models and results, and the top five submissions. The paper concludes with a summary discussion of the ADReSS-M results, and our critical assessment of the future outlook in this field.
وصف الملف: electronic resource
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3دورية أكاديميةEnhancing volleyball training: empowering athletes and coaches through advanced sensing and analysis
المؤلفون: Fahim A. Salim, Dees B. W. Postma, Fasih Haider, Saturnino Luz, Bert-Jan F. van Beijnum, Dennis Reidsma
المصدر: Frontiers in Sports and Active Living, Vol 6 (2024)
مصطلحات موضوعية: smart sports, digital sports technologies, sports telematics, telematics applications, training methods, action recognition, Sports, GV557-1198.995
الوصف: Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial Measurement Units, a pressure sensitive display floor, and machine learning techniques to automatically detect relevant behaviours and provides the user with the appropriate information. While working with trainers and amateur athletes, we also explore potential applications that are driven by automatic action recognition, that contribute various requirements to the platform. The first application is an automatic video-tagging protocol that marks key events (captured on video) based on the automatic recognition of volleyball-specific actions with an unweighted average recall of 78.71% in the 10-fold cross-validation setting with convolution neural network and 73.84% in leave-one-subject-out cross-validation setting with active data representation method using wearable sensors, as an exemplification of how dashboard and retrieval systems would work with the platform. In the context of action recognition, we have evaluated statistical functions and their transformation using active data representation besides raw signal of IMUs sensor. The second application is the “bump-set-spike” trainer, which uses automatic action recognition to provide real-time feedback about performance to steer player behaviour in volleyball, as an example of rich learning environments enabled by live action detection. In addition to describing these applications, we detail the system components and architecture and discuss the implications that our system might have for sports in general and for volleyball in particular.
وصف الملف: electronic resource
العلاقة: https://www.frontiersin.org/articles/10.3389/fspor.2024.1326807/fullTest; https://doaj.org/toc/2624-9367Test
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4دورية أكاديمية
المصدر: BMJ Open, Vol 14, Iss 3 (2024)
مصطلحات موضوعية: Medicine
الوصف: Introduction There is emerging evidence that speech may be a potential indicator and manifestation of early Alzheimer’s disease (AD) pathology. Therefore, the University of Edinburgh and Sony Research have partnered to create the Speech for Intelligent cognition change tracking and DEtection of Alzheimer’s Disease (SIDE-AD) study, which aims to develop digital speech-based biomarkers for use in neurodegenerative disease.Methods and analysis SIDE-AD is an observational longitudinal study, collecting samples of spontaneous speech. Participants are recruited from existing cohort studies as well as from the National Health Service (NHS)memory clinics in Scotland. Using an online platform, participants record a voice sample talking about their brain health and rate their mood, anxiety and apathy. The speech biomarkers will be analysed longitudinally, and we will use machine learning and natural language processing technology to automate the assessment of the respondents’ speech patterns.Ethics and dissemination The SIDE-AD study has been approved by the NHS Research Ethics Committee (REC reference: 23/WM/0153, protocol number AC23046, IRAS Project ID 323311) and received NHS management approvals from Lothian, Fife and Forth Valley NHS boards. Our main ethical considerations pertain to the remote administration of the study, such as taking remote consent. To address this, we implemented a consent process, whereby the first step of the consent is done entirely remotely but a member of the research team contacts the participant over the phone to consent participants to the optional, most sensitive, elements of the study. Results will be presented at conferences, published in peer-reviewed journals and communicated to study participants.
وصف الملف: electronic resource
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5دورية أكاديمية
المؤلفون: Tjeerd Van Staa, Rosanna W Peeling, Saturnino Luz, Herman Goossens, Gunnar Skov Simonsen, Rangarajan Sampath, Jacob Moran-Gilad, Valentina Di Gregori, Alex van Belkum, Jordi Vila, Till T Bachmann, Konstantinos Mitsakakis, John P Hays, Aman Russom, Gerd Luedke, Gyorgy Abel, Harald Peter, Karsten Becker, Pieter Moons
المصدر: BMJ Global Health, Vol 8, Iss 12 (2023)
مصطلحات موضوعية: Medicine (General), R5-920, Infectious and parasitic diseases, RC109-216
الوصف: Diagnostics are widely considered crucial in the fight against antimicrobial resistance (AMR), which is expected to kill 10 million people annually by 2030. Nevertheless, there remains a substantial gap between the need for AMR diagnostics versus their development and implementation. To help address this problem, target product profiles (TPP) have been developed to focus developers’ attention on the key aspects of AMR diagnostic tests. However, during discussion between a multisectoral working group of 51 international experts from industry, academia and healthcare, it was noted that specific AMR-related TPPs could be extended by incorporating the interdependencies between the key characteristics associated with the development of such TPPs. Subsequently, the working group identified 46 characteristics associated with six main categories (ie, Intended Use, Diagnostic Question, Test Description, Assay Protocol, Performance and Commercial). The interdependencies of these characteristics were then identified and mapped against each other to generate new insights for use by stakeholders. Specifically, it may not be possible for diagnostics developers to achieve all of the recommendations in every category of a TPP and this publication indicates how prioritising specific TPP characteristics during diagnostics development may influence (or not) a range of other TPP characteristics associated with the diagnostic. The use of such guidance, in conjunction with specific TPPs, could lead to more efficient AMR diagnostics development.
وصف الملف: electronic resource
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6دورية أكاديمية
المؤلفون: Pierre Albert, Fasih Haider, Saturnino Luz
المصدر: Sensors, Vol 24, Iss 5, p 1506 (2024)
مصطلحات موضوعية: ambient intelligence, human-behaviour tracking, multimodal recording devices, privacy preservation, cooperative work, healthcare, Chemical technology, TP1-1185
الوصف: The ubiquity of digital technology has facilitated detailed recording of human behaviour. Ambient technology has been used to capture behaviours in a broad range of applications ranging from healthcare and monitoring to assessment of cooperative work. However, existing systems often face challenges in terms of autonomy, usability, and privacy. This paper presents a portable, easy-to-use and privacy-preserving system for capturing behavioural signals unobtrusively in home or in office settings. The system focuses on the capture of audio, video, and depth imaging. It is based on a device built on a small-factor platform that incorporates ambient sensors which can be integrated with the audio and depth video hardware for multimodal behaviour tracking. The system can be accessed remotely and integrated into a network of sensors. Data are encrypted in real time to ensure safety and privacy. We illustrate uses of the device in two different settings, namely, a healthy-ageing IoT application, where the device is used in conjunction with a range of IoT sensors to monitor an older person’s mental well-being at home, and a healthcare communication quality assessment application, where the device is used to capture a patient–clinician interaction for consultation quality appraisal. CUSCO can automatically detect active speakers, extract acoustic features, record video and depth streams, and recognise emotions and cognitive impairment with promising accuracy.
وصف الملف: electronic resource
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7دورية أكاديمية
المؤلفون: Fasih Haider, Pierre Albert, Saturnino Luz
المصدر: AI, Vol 2, Iss 4, Pp 636-649 (2021)
مصطلحات موضوعية: privacy preservation, affect recognition, health technologies, emotion recognition, Ambient Assisted Living, social signal processing, Electronic computers. Computer science, QA75.5-76.95
الوصف: Ambient Assisted Living (AAL) technologies are being developed which could assist elderly people to live healthy and active lives. These technologies have been used to monitor people’s daily exercises, consumption of calories and sleep patterns, and to provide coaching interventions to foster positive behaviour. Speech and audio processing can be used to complement such AAL technologies to inform interventions for healthy ageing by analyzing speech data captured in the user’s home. However, collection of data in home settings presents challenges. One of the most pressing challenges concerns how to manage privacy and data protection. To address this issue, we proposed a low cost system for recording disguised speech signals which can protect user identity by using pitch shifting. The disguised speech so recorded can then be used for training machine learning models for affective behaviour monitoring. Affective behaviour could provide an indicator of the onset of mental health issues such as depression and cognitive impairment, and help develop clinical tools for automatically detecting and monitoring disease progression. In this article, acoustic features extracted from the non-disguised and disguised speech are evaluated in an affect recognition task using six different machine learning classification methods. The results of transfer learning from non-disguised to disguised speech are also demonstrated. We have identified sets of acoustic features which are not affected by the pitch shifting algorithm and also evaluated them in affect recognition. We found that, while the non-disguised speech signal gives the best Unweighted Average Recall (UAR) of 80.01%, the disguised speech signal only causes a slight degradation of performance, reaching 76.29%. The transfer learning from non-disguised to disguised speech results in a reduction of UAR (65.13%). However, feature selection improves the UAR (68.32%). This approach forms part of a large project which includes health and wellbeing monitoring and coaching.
وصف الملف: electronic resource
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8دورية أكاديمية
المؤلفون: Craig W Ritchie, Saturnino Luz, Sarah Gregory, John Harrison, Alexandra König, Nicklas Linz, Kai Langel, Hannah Pullen
المصدر: BMJ Open, Vol 12, Iss 3 (2022)
مصطلحات موضوعية: Medicine
وصف الملف: electronic resource
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9دورية أكاديمية
المصدر: Frontiers in Computer Science, Vol 3 (2021)
مصطلحات موضوعية: Alzheimer’s disease, signal processing (SP), machine learning, speech processing and recognition, natural language processing (NLP), computational paralinguistics, Electronic computers. Computer science, QA75.5-76.95
وصف الملف: electronic resource
العلاقة: https://www.frontiersin.org/articles/10.3389/fcomp.2021.780169/fullTest; https://doaj.org/toc/2624-9898Test
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10دورية أكاديمية
المؤلفون: Matej Martinc, Fasih Haider, Senja Pollak, Saturnino Luz
المصدر: Frontiers in Aging Neuroscience, Vol 13 (2021)
مصطلحات موضوعية: Alzheimer's dementia detection, speech, language, acoustic features, lexical features, natural language processing, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
الوصف: Background: Advances in machine learning (ML) technology have opened new avenues for detection and monitoring of cognitive decline. In this study, a multimodal approach to Alzheimer's dementia detection based on the patient's spontaneous speech is presented. This approach was tested on a standard, publicly available Alzheimer's speech dataset for comparability. The data comprise voice samples from 156 participants (1:1 ratio of Alzheimer's to control), matched by age and gender.Materials and Methods: A recently developed Active Data Representation (ADR) technique for voice processing was employed as a framework for fusion of acoustic and textual features at sentence and word level. Temporal aspects of textual features were investigated in conjunction with acoustic features in order to shed light on the temporal interplay between paralinguistic (acoustic) and linguistic (textual) aspects of Alzheimer's speech. Combinations between several configurations of ADR features and more traditional bag-of-n-grams approaches were used in an ensemble of classifiers built and evaluated on a standardised dataset containing recorded speech of scene descriptions and textual transcripts.Results: Employing only semantic bag-of-n-grams features, an accuracy of 89.58% was achieved in distinguishing between Alzheimer's patients and healthy controls. Adding temporal and structural information by combining bag-of-n-grams features with ADR audio/textual features, the accuracy could be improved to 91.67% on the test set. An accuracy of 93.75% was achieved through late fusion of the three best feature configurations, which corresponds to a 4.7% improvement over the best result reported in the literature for this dataset.Conclusion: The proposed combination of ADR audio and textual features is capable of successfully modelling temporal aspects of the data. The machine learning approach toward dementia detection achieves best performance when ADR features are combined with strong semantic bag-of-n-grams features. This combination leads to state-of-the-art performance on the AD classification task.
وصف الملف: electronic resource
العلاقة: https://www.frontiersin.org/articles/10.3389/fnagi.2021.642647/fullTest; https://doaj.org/toc/1663-4365Test
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