A systematic review of sarcopenia prevalence and associated factors in people living with human immunodeficiency virus

People living with human immunodeficiency virus (HIV) (PLWH) appear to be at an increased risk of sarcopenia, which can have a devastating effect on their life due to consequences such as physical disability, poor quality of life, and finally death. This systematic review examined sarcopenia prevalence and its associated factors in PLWH. A systematic search was conducted using the keywords in the online databases including Scopus, PubMed, Web of Science, Embase and Cochrane databases from the dates of inception up to May 2022. The retrieved articles underwent a two-step title/abstract and full-text review process, and the eligible papers were selected and included in the qualitative synthesis. Data relating to the study population, purpose of study, gender, age, race, body mass index, medical history, paraclinical results and antiretroviral therapy as associated factors of sarcopenia were extracted. In addition, the prevalence of sarcopenia in PLWH and its promoting and reducing factors were also extracted. We reviewed the 14 related studies for identifying of sarcopenia prevalence and its associated factors in PLWH. The total number of PLWH in all the reviewed studies was 2592. There was no criterion for the minimum number of people with HIV and the lowest number of PLWH was 27, and the highest number was 860. Some studies reported a significantly higher prevalence of sarcopenia in HIV-infected individuals compared with HIV-negative controls as follows: 24.2–6.7%, 15–4% and 10–6%, respectively. We showed that, age (30–50 years), being female, >5 years post-HIV diagnosis, multiple vertebral fractures, cocaine/heroin use and lower gamma-glutamyl transferase level were the main promoting factors of sarcopenia. Higher educational level, employment, physical exercise, calf circumference >31 cm, and gait speed >0.8 m/s were also factors to reduce sarcopenia. Sarcopenia prevalence in PLWH is higher than HIV-negative population. Given the importance and prevalence of sarcopenia among PLWH and its associated consequences (i.e., mortality and disability), determining its risk factors is of great importance. © 2023 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders

The application of adaptive neuro-fuzzy classifier using linguistic hedges in emotion recognition system

In human communication, expression and understanding of emotions facilitate the mutual sympathy. To approach this level of understanding in human-machine interaction, we need to equip machines with the means to interpret and understand human emotions without the input of the user’s translated intention. There are a variety of emerging applications that track physiological data associated with emotional states over periods of time using biosensors. Physiological signals have been largely neglected for emotion recognition as compared with audio-visual emotion sensors such as facial expression or speech. Classifiers are important for emotion recognition regardless of the type of signal. This paper presents an effective adaptive neuro-fuzzy classifier using the linguistic hedges (ANFC-LH) in human emotion classification and investigates the potential of physiological signals as reliable channels for this purpose

A Robust Real-time Stress Detection System Using ECG and Neuro-Fuzzy Classification Method

A reliable solution for mutual sympathy in human computer interaction (HCI) has recently become a major issue in human life. • Machines disregard human emotion in the human computer interaction (no sympathy). • Stress in some social situations such as job interview can be tough even if one has gone through it many times. • Bio-signals are one of the most reliable input parameters for assessing human emotion. • The electrocardiogram signal (ECG) is one of the biosensors used in this study for stress detection

Stress detection during job interview using physiological signal

A job interview can be challenging and stressful even when one has gone through it many times. Failure to handle the stress may lead to unsuccessful delivery of their best throughout the interview session. Therefore, an alternative method which is preparing a video resume and interview before the actual interview could reduce the level of stress. An intelligent stress detection is proposed to classify individuals with different stress levels by understanding the physiological signal through electrocardiogram (ECG) signals. The Augsburg biosignal toolbox (AUBT) dataset was used to obtain the state-of-art results. Only five selected features are significant to the stress level were fed into neural network multi-layer perceptron (MLP) as the optimum classifier. This stress detection achieved an accuracy of 92.93% when tested over the video interview dataset of 10 male subjects who were recording the video resume for the analysis purposes

Stress detection during job interview using physiological signal

A job interview can be challenging and stressful even when one has gone through it many times. Failure to handle the stress may lead to unsuccessful delivery of their best throughout the interview session. Therefore, an alternative method which is preparing a video resume and interview before the actual interview could reduce the level of stress. An intelligent stress detection is proposed to classify individuals with different stress levels by understanding the physiological signal through electrocardiogram (ECG) signals. The Augsburg biosignal toolbox (AUBT) dataset was used to obtain the state-of-art results. Only five selected features are significant to the stress level were fed into neural network multi-layer perceptron (MLP) as the optimum classifier. This stress detection achieved an accuracy of 92.93% when tested over the video interview dataset of 10 male subjects who were recording the video resume for the analysis purposes

Robust stress classifier using adaptive neuro-fuzzy classifier-linguistic hedges

Recent studies show that chronic stress exposure can induce a long list of diseases that are prevalent in human body. In this paper, researchers work on measuring and analyzing stress level using human biosignal, electrocardiogram (ECG). First, a few preprocessing steps and different analysis domains is done onto the raw data signals to clean and extract any and every relevant features found in ECG signal. A Linguistic Hedges concept on fuzzy feature selection method is then proposed to select unique patterns from the listed heart rate variability features. From the extracted list of features, a neurofuzzy classifier (ANFC-LH) is used to classify the data points into 2 classes, high arousal and low arousal, high arousal indicating stress feature. Then a comparative study using different classification methods, including Multilayer Perceptron, kNearest Neighbor, and Linear Discriminant Analysis are used to determine the most relevant feature specifying high stress level. Comparing to MLP, kNN, and LDA, ANFC-LH achieved the highest recognition rate. This research paper also shows the effects of using dimension reduction methods on classification algorithms where the result of kNN and LDA improved about 20% when applied with dimension reduction method, however, MLP recognition rate deteriorates about 50% when classifying data point after dimension reduction