ORCID Profile
0000-0003-1892-831X
Current Organisation
Curtin University Computing Discipline
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 05-2013
Publisher: Science Publications
Date: 10-2013
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 02-2019
Publisher: Springer International Publishing
Date: 2020
Publisher: ACM
Date: 28-11-2017
Publisher: The Australian National University
Date: 2018
Publisher: Springer International Publishing
Date: 2021
Publisher: Cold Spring Harbor Laboratory
Date: 20-04-2021
DOI: 10.1101/2021.04.16.21255630
Abstract: The COVID-19 pandemic has a devastating impact on the health and well-being of global population. Cough audio signals classification showed potential as a screening approach for diagnosing people, infected with COVID-19. Recent approaches need costly deep learning algorithms or sophisticated methods to extract informative features from cough audio signals. In this paper, we propose a low-cost envelope approach, called CovidEnvelope, which can classify COVID-19 positive and negative cases from raw data by avoiding above disadvantages. This automated approach can pre-process cough audio signals by filter-out back-ground noises, generate an envelope around the audio signal, and finally provide outcomes by computing area enclosed by the envelope. It has been seen that reliable datasets are also important for achieving high performance. Our approach proves that human verbal confirmation is not a reliable source of information. Finally, the approach reaches highest sensitivity, specificity, accuracy, and AUC of 0.92, 0.87, 0.89, and 0.89 respectively. The automatic approach only takes 1.8 to 3.9 minutes to compute these performances. Overall, this approach is fast and sensitive to diagnose the people living with COVID-19, regardless of having COVID-19 related symptoms or not, and thus have vast applicability in human well-being by designing HCI devices incorporating this approach.
Publisher: MDPI AG
Date: 28-12-2022
DOI: 10.3390/APP13010387
Abstract: Emotion monitoring can play a vital role in investigating mental health disorders that contribute to 14% of global diseases. Currently, the mental healthcare system is struggling to cope with the increasing demand. Robot-assisted mental health monitoring tools can take the enormous strain off the system. The current study explored existing state-of-art machine learning (ML) models and signal data from different bio-sensors assessed the suitability of robotic devices for surveilling different physiological and physical traits related to human emotions and discussed their potential applicability for mental health monitoring. Among the selected 80 articles, we sub ided our findings in terms of two different emotional categories, namely—discrete and valence-arousal (VA). By examining two different types of signals (physical and physiological) from 10 different signal sources, we found that RGB images and CNN models outperformed all other data sources and models, respectively, in both categories. Out of the 27 investigated discrete imaging signals, 25 reached higher than 80% accuracy, while the highest accuracy was observed from facial imaging signals (99.90%). Besides imaging signals, brain signals showed better potentiality than other data sources in both emotional categories, with accuracies of 99.40% and 96.88%. For both discrete and valence-arousal categories, neural network-based models illustrated superior performances. The majority of the neural network models achieved accuracies of over 80%, ranging from 80.14% to 99.90% in discrete, 83.79% to 96.88% in arousal, and 83.79% to 99.40% in valence. We also found that the performances of fusion signals (a combination of two or more signals) surpassed that of the in idual ones in most cases, showing the importance of combining different signals for future model development. Overall, the potential implications of the survey are discussed, considering both human computing and mental health monitoring. The current study will definitely serve as the base for research in the field of human emotion recognition, with a particular focus on developing different robotic tools for mental health monitoring.
Publisher: JMIR Publications Inc.
Date: 08-04-2022
DOI: 10.2196/28901
Abstract: Monitoring glucose and other parameters in persons with type 1 diabetes (T1D) can enhance acute glycemic management and the diagnosis of long-term complications of the disease. For most persons living with T1D, the determination of insulin delivery is based on a single measured parameter—glucose. To date, wearable sensors exist that enable the seamless, noninvasive, and low-cost monitoring of multiple physiological parameters. The objective of this literature survey is to explore whether some of the physiological parameters that can be monitored with noninvasive, wearable sensors may be used to enhance T1D management. A list of physiological parameters, which can be monitored by using wearable sensors available in 2020, was compiled by a thorough review of the devices available in the market. A literature survey was performed using search terms related to T1D combined with the identified physiological parameters. The selected publications were restricted to human studies, which had at least their abstracts available. The PubMed and Scopus databases were interrogated. In total, 77 articles were retained and analyzed based on the following two axes: the reported relations between these parameters and T1D, which were found by comparing persons with T1D and healthy control participants, and the potential areas for T1D enhancement via the further analysis of the found relationships in studies working within T1D cohorts. On the basis of our search methodology, 626 articles were returned, and after applying our exclusion criteria, 77 (12.3%) articles were retained. Physiological parameters with potential for monitoring by using noninvasive wearable devices in persons with T1D included those related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature. Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, have been shown to be valuable in diagnosing and monitoring cardiac autonomic neuropathy and, potentially, predicting and detecting hypoglycemia. All identified physiological parameters were shown to be associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with capacity for early risk prediction. However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices. Wearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges associated with measurement accuracy, removal of noise and motion artifacts, and smart decision-making exist. Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions.
Publisher: IEEE
Date: 02-2014
Publisher: ACM
Date: 04-12-2018
Publisher: IEEE
Date: 05-2013
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Nature Singapore
Date: 2022
Publisher: ACM
Date: 02-12-2019
Publisher: IEEE
Date: 02-2014
Publisher: ACM
Date: 29-01-2018
Publisher: Springer International Publishing
Date: 2021
Publisher: ACM
Date: 23-05-2017
Publisher: Springer International Publishing
Date: 2017
Publisher: Canadian Center of Science and Education
Date: 11-03-2014
DOI: 10.5539/MAS.V8N2P69
Publisher: Institution of Engineering and Technology (IET)
Date: 06-10-2021
DOI: 10.1049/TJE2.12082
Publisher: Elsevier BV
Date: 2016
Publisher: Springer International Publishing
Date: 2018
Publisher: Canadian Center of Science and Education
Date: 25-01-2014
DOI: 10.5539/MAS.V8N1P164
Publisher: Wiley
Date: 20-07-2022
DOI: 10.1111/EXSY.13045
Abstract: Although speech recognition has achieved significant success using integrated and efficient models, still some series of challenges remain as linguistic‐acoustic patterns are perturbed by speakers' in idual articulation gestures and environmental noises. Due to dynamic changes in the vocal tract cavity, word utterances yield temporal and perturbed linguistic‐acoustic features, whereas vowel utterances yield less‐perturbed quasi‐stationary features. To recognize patterns as in vowels and words, the basic feedforward neural network (NN), among other methods, responds to these vocal tract‐induced variabilities and has shown promising results because of its simple yet effective modelling of nonlinear data. We, therefore, present a comprehensive study on how these variabilities of acoustical features affect the speech token classification performances using NNs. We chose vocal tract resonance (formant frequency) as linguistic‐acoustic feature. Our statistical evaluation of vocal tract‐induced variabilities in seven Bengali vowels and words revealed that words have more variations than vowels. We used four‐fold cross‐validation in an NN with Adam optimizer to compute classification performances using five different metrics. Our experiments found that formant transitions and dispersions do not contribute to classification, and five‐hidden‐layered NN is optimum. In all different test cases, we justified our hypothesis—word classification falls behind vowel classification due to the variability induced by vocal tract dynamics. The optimum NN with 28,263 trainable parameters achieved the highest accuracy and AUC scores: 0.89 and 0.99 in vowels, and 0.64 and 0.91 in words.
Location: Bangladesh
No related grants have been discovered for Md Zakir Hossain.