scholarly journals Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5022
Author(s):  
Francesco Asci ◽  
Giovanni Costantini ◽  
Pietro Di Leo ◽  
Alessandro Zampogna ◽  
Giovanni Ruoppolo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Experimental Studies ◽  
Combined Effect ◽  
Support Vector ◽  
Ecological Value ◽  
Age Related ◽  
And Gender ◽  
Age Range ◽  
Learning Analysis

Background: Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, however, have mostly focused on specific voice features without examining their dynamic interaction. To examine the complexity of age-related changes in voice, more advanced techniques based on machine learning have been recently applied to voice recordings but only in a laboratory setting. We here recorded voice samples in a large sample of healthy subjects. To improve the ecological value of our analysis, we collected voice samples directly at home using smartphones. Methods: 138 younger adults (65 males and 73 females, age range: 15–30) and 123 older adults (47 males and 76 females, age range: 40–85) produced a sustained emission of a vowel and a sentence. The recorded voice samples underwent a machine learning analysis through a support vector machine algorithm. Results: The machine learning analysis of voice samples from both speech tasks discriminated between younger and older adults, and between males and females, with high statistical accuracy. Conclusions: By recording voice samples through smartphones in an ecological setting, we demonstrated the combined effect of age and gender on voice. Our machine learning analysis demonstrates the effect of ageing on voice.

scholarly journals Decoding age-related changes in the spatiotemporal neural processing of speech using machine learning

2019 ◽  
Author(s):  
Md Sultan Mahmud ◽  
Faruk Ahmed ◽  
Rakib Al-Fahad ◽  
Kazi Ashraf Moinuddin ◽  
Mohammed Yeasin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Hearing Loss ◽  
Brain Regions ◽  
Evoked Response ◽  
Support Vector ◽  
Model Parameters ◽  
Stability Selection ◽  
Degraded Speech ◽  
Age Related

ABSTRACTSpeech comprehension in noisy environments depends on complex interactions between sensory and cognitive systems. In older adults, such interactions may be affected, especially in those individuals who have more severe age-related hearing loss. Using a data-driven approach, we assessed the temporal (when in time) and spatial (where in the brain) characteristics of the cortex’s speech-evoked response that distinguish older adults with or without mild hearing loss. We used source montage to model scalp-recorded during a phoneme discrimination task conducted under clear and noise-degraded conditions. We applied machine learning analyses (stability selection and control) to choose features of the speech-evoked response that are consistent over a range of model parameters and support vector machine (SVM) classification to investigate the time course and brain regions that segregate groups and speech clarity. Whole-brain data analysis revealed a classification accuracy of 82.03% [area under the curve (AUC)=81.18%; F1-score 82.00%], distinguishing groups within ∼50 ms after speech onset (i.e., as early as the P1 wave).We observed lower accuracy of 78.39% [AUC=78.74%; F1-score=79.00%] and delayed classification performance when the speech token were embedded in noise, with group segregation at 60 ms. Separate analysis using left (LH) and right hemisphere (RH) regions showed that LH speech activity was better at distinguishing hearing groups than activity measured over the RH. Moreover, stability selection analysis identified 13 brain regions (among 1428 total spatiotemporal features from 68 regions) where source activity segregated groups with >80% accuracy (clear speech); whereas 15 regions were critical for noise-degraded speech to achieve a comparable level of group segregation (76% accuracy). Our results identify two core neural networks associated with complex speech perception in older adults and confirm a larger number of neural regions, particularly in RH and frontal lobe, are active when processing degraded speech information.

scholarly journals Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246397
Author(s):  
Keisuke Hirata ◽  
Makoto Suzuki ◽  
Naoki Iso ◽  
Takuhiro Okabe ◽  
Hiroshi Goto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Knee Extension ◽  
The Body ◽  
Functional Mobility ◽  
Support Vector ◽  
Timed Up And Go ◽  
Explanatory Variables ◽  
Machine Learning Classification ◽  
Age Related

Previous studies have shown that functional mobility, along with other physical functions, decreases with advanced age. However, it is still unclear which domains of functioning (body structures, body functions, and activities) are most closely related to functional mobility. This study used machine learning classification to predict the rankings of Timed Up and Go tests based on the results of four assessments (soft lean mass, FEV1/FVC, knee extension torque, and one-leg standing time). We tested whether assessment results for each level could predict functional mobility assessments in older adults. Using support vector machines for machine learning classification, we verified that the four assessments of each level could classify functional mobility. Knee extension torque (from the body function domain) was the most closely related assessment. Naturally, the classification accuracy rate increased with a larger number of assessments as explanatory variables. However, knee extension torque remained the highest of all assessments. This extended to all combinations (of 2–3 assessments) that included knee extension torque. This suggests that resistance training may help protect individuals suffering from age-related declines in functional mobility.

scholarly journals Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 374 ◽  
Author(s):  
Sudhanshu Kumar ◽  
Monika Gahalawat ◽  
Partha Pratim Roy ◽  
Debi Prosad Dogra ◽  
Byung-Gyu Kim
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Age Groups ◽  
Modern World ◽  
Support Vector ◽  
Digital Information ◽  
Age And Gender ◽  
And Gender ◽  
The Impact

Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

Goal disengagement capacities and severity of disease across older adulthood

2015 ◽  
Vol 40 (2) ◽  
pp. 137-144 ◽  
Author(s):  
Joelle Jobin ◽  
Carsten Wrosch
Keyword(s):  
Older Adults ◽  
Depressive Symptoms ◽  
Emotional Distress ◽  
Well Being ◽  
Older Adulthood ◽  
Mediation Analyses ◽  
Age Related ◽  
Goal Disengagement ◽  
Age Range ◽  
Early Old Age

This study examined age-related associations between goal disengagement capacities, emotional distress, and disease severity across older adulthood. Given that an age-related increase in the experience of stressors might render important goals unattainable, it is expected that goal disengagement capacities would predict a decrease in the severity of experienced illness (i.e., the common cold) by preventing emotional distress (i.e., depressive symptoms), particularly so among individuals in advanced (as compared to early) old age. This hypothesis was tested in a 6-year longitudinal study of 131 older adults (age range = 64 to 90). Regression analyses showed that goal disengagement capacities buffered 6-year increases in older adults’ cold symptoms, and that this effect was significantly pronounced among older-old participants. Mediation analyses further indicated that changes in depressive symptoms exerted an indirect effect on the age-related association between goal disengagement and changes in cold symptoms. The study’s findings suggest that goal disengagement capacities become increasingly important for protecting emotional well-being and physical health as older adults advance in age.

scholarly journals Neurometabolic Correlates of Reactive and Proactive Motor Inhibition in Young and Older Adults: Evidence from Multiple Regional 1H-MR Spectroscopy

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Akila Weerasekera ◽  
Oron Levin ◽  
Amanda Clauwaert ◽  
Kirstin-Friederike Heise ◽  
Lize Hermans ◽  
...  
Keyword(s):  
Older Adults ◽  
Inhibitory Control ◽  
Inferior Frontal Gyrus ◽  
Proactive Inhibition ◽  
Older Age ◽  
Resonance Spectroscopy ◽  
Reactive Inhibition ◽  
Age Related ◽  
The Right ◽  
Age Range

Abstract Suboptimal inhibitory control is a major factor contributing to motor/cognitive deficits in older age and pathology. Here, we provide novel insights into the neurochemical biomarkers of inhibitory control in healthy young and older adults and highlight putative neurometabolic correlates of deficient inhibitory functions in normal aging. Age-related alterations in levels of glutamate–glutamine complex (Glx), N-acetylaspartate (NAA), choline (Cho), and myo-inositol (mIns) were assessed in the right inferior frontal gyrus (RIFG), pre-supplementary motor area (preSMA), bilateral sensorimotor cortex (SM1), bilateral striatum (STR), and occipital cortex (OCC) with proton magnetic resonance spectroscopy (1H-MRS). Data were collected from 30 young (age range 18–34 years) and 29 older (age range 60–74 years) adults. Associations between age-related changes in the levels of these metabolites and performance measures or reactive/proactive inhibition were examined for each age group. Glx levels in the right striatum and preSMA were associated with more efficient proactive inhibition in young adults but were not predictive for reactive inhibition performance. Higher NAA/mIns ratios in the preSMA and RIFG and lower mIns levels in the OCC were associated with better deployment of proactive and reactive inhibition in older adults. Overall, these findings suggest that altered regional concentrations of NAA and mIns constitute potential biomarkers of suboptimal inhibitory control in aging.

scholarly journals Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

2018 ◽  
Vol 12 (7) ◽  
pp. 49 ◽  
Author(s):  
Osama Harfoushi ◽  
Dana Hasan ◽  
Ruba Obiedat
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Language Processing ◽  
Data Analytics ◽  
Support Vector ◽  
Social Media Analytics ◽  
Depth Analysis ◽  
Series Of Experiments ◽  
Microsoft Azure ◽  
Learning Analysis

The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals’ opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.

The Effects of Age and Gender on Lipreading Abilities

2007 ◽  
Vol 18 (10) ◽  
pp. 883-892 ◽  
Author(s):  
Nancy Tye-Murray ◽  
Mitchell S. Sommers ◽  
Brent Spehar
Keyword(s):  
Older Adults ◽  
Cognitive Abilities ◽  
Younger Adults ◽  
Percent Correct ◽  
Age Related ◽  
Males And Females ◽  
The Difference ◽  
Las Mujeres ◽  
And Gender ◽  
Older Males

Age-related declines for many sensory and cognitive abilities are greater for males than for females. The primary purpose of the present investigation was to consider whether age-related changes in lipreading abilities are similar for men and women by comparing the lipreading abilities of separate groups of younger and older adults. Older females, older males, younger females and younger males completed vision-only speech recognition tests of: (1) 13 consonants in a vocalic /i/-C-/i/ environment; (2) words in a carrier phrase; and (3) meaningful sentences. In addition to percent correct performance, consonant data were analyzed for performance within viseme categories. The results suggest that while older adults do not lipread as well as younger adults, the difference between older and younger participants was comparable across gender. We also found no differences in the lipreading abilities of males and females, regardless of stimulus type (i.e., consonants, words, sentences), a finding that differs from some reports by previous investigators (e.g., Dancer, Krain, Thompson, Davis, & Glenn, 1994). El deterioro relacionado con la edad de muchas habilidades sensoriales y cognitivas es mayor para los hombres que para las mujeres. El propósito primario de la presente investigación fue considerar si los cambios relacionados con la edad en la habilidad de leer los labios eran similares para hombre y mujeres, comparando las habilidades de lectura labial de grupos separados de adultos jóvenes y viejos. Mujeres viejas, hombres viejos, mujeres jóvenes y hombres jóvenes completaron pruebas de reconocimiento del lenguaje únicamente por medio de la visión de: (1) 13 consonantes en un ambiente vocálico /i/-C-/i/; (2) de palabras en una frase portadora; y (3) de frases significativas. Además del porcentaje correcto de desempeño, los datos de las consonantes se analizaron en cuanto a desempeño dentro de las categorías de visemas. Los resultados sugieren que mientras los adultos más viejos no leen los labios tan bien como los adultos más jóvenes, las diferencias entre participantes más viejos y más jóvenes fueron comparables entre los géneros. Tampoco encontramos diferencias en las habilidades de lectura labial de hombres y mujeres, sin importar el tipo de estímulo (p.e., consonantes, palabras, frases), un hallazgo que difiere con algunos reportes de investigadores previos (p.e., Dancer, Krain, Thompson, Davis, & Glenn, 1994).

scholarly journals Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches (Preprint)

2019 ◽  
Author(s):  
Adane Tarekegn ◽  
Fulvio Ricceri ◽  
Giuseppe Costa ◽  
Elisa Ferracin ◽  
Mario Giacobini
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Elderly People ◽  
Emergency Admission ◽  
Evaluation Metrics ◽  
Support Vector ◽  
Learning Models ◽  
Increased Risk ◽  
Wide Range ◽  
Machine Learning Models

BACKGROUND Frailty is one of the most critical age-related conditions in older adults. It is often recognized as a syndrome of physiological decline in late life, characterized by a marked vulnerability to adverse health outcomes. A clear operational definition of frailty, however, has not been agreed so far. There is a wide range of studies on the detection of frailty and their association with mortality. Several of these studies have focused on the possible risk factors associated with frailty in the elderly population while predicting who will be at increased risk of frailty is still overlooked in clinical settings. OBJECTIVE The objective of our study was to develop predictive models for frailty conditions in older people using different machine learning methods based on a database of clinical characteristics and socioeconomic factors. METHODS An administrative health database containing 1,095,612 elderly people aged 65 or older with 58 input variables and 6 output variables was used. We first identify and define six problems/outputs as surrogates of frailty. We then resolve the imbalanced nature of the data through resampling process and a comparative study between the different machine learning (ML) algorithms – Artificial neural network (ANN), Genetic programming (GP), Support vector machines (SVM), Random Forest (RF), Logistic regression (LR) and Decision tree (DT) – was carried out. The performance of each model was evaluated using a separate unseen dataset. RESULTS Predicting mortality outcome has shown higher performance with ANN (TPR 0.81, TNR 0.76, accuracy 0.78, F1-score 0.79) and SVM (TPR 0.77, TNR 0.80, accuracy 0.79, F1-score 0.78) than predicting the other outcomes. On average, over the six problems, the DT classifier has shown the lowest accuracy, while other models (GP, LR, RF, ANN, and SVM) performed better. All models have shown lower accuracy in predicting an event of an emergency admission with red code than predicting fracture and disability. In predicting urgent hospitalization, only SVM achieved better performance (TPR 0.75, TNR 0.77, accuracy 0.73, F1-score 0.76) with the 10-fold cross validation compared with other models in all evaluation metrics. CONCLUSIONS We developed machine learning models for predicting frailty conditions (mortality, urgent hospitalization, disability, fracture, and emergency admission). The results show that the prediction performance of machine learning models significantly varies from problem to problem in terms of different evaluation metrics. Through further improvement, the model that performs better can be used as a base for developing decision-support tools to improve early identification and prediction of frail older adults.

scholarly journals Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Qian Su ◽  
Rui Zhao ◽  
ShuoWen Wang ◽  
HaoYang Tu ◽  
Xing Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Functional Mri ◽  
Resting State ◽  
Cervical Spondylotic Myelopathy ◽  
Preliminary Evidence ◽  
Neurological Recovery ◽  
Support Vector ◽  
Learning Analysis ◽  
Cross Site

Currently, strategies to diagnose patients and predict neurological recovery in cervical spondylotic myelopathy (CSM) using MR images of the cervical spine are urgently required. In light of this, this study aimed at exploring potential preoperative brain biomarkers that can be used to diagnose and predict neurological recovery in CSM patients using functional connectivity (FC) analysis of a resting-state functional MRI (rs-fMRI) data. Two independent datasets, including total of 53 patients with CSM and 47 age- and sex-matched healthy controls (HCs), underwent the preoperative rs-fMRI procedure. The FC was calculated from the automated anatomical labeling (AAL) template and used as features for machine learning analysis. After that, three analyses were used, namely, the classification of CSM patients from healthy adults using the support vector machine (SVM) within and across datasets, the prediction of preoperative neurological function in CSM patients via support vector regression (SVR) within and across datasets, and the prediction of neurological recovery in CSM patients via SVR within and across datasets. The results showed that CSM patients could be successfully identified from HCs with high classification accuracies (84.2% for dataset 1, 95.2% for dataset 2, and 73.0% for cross-site validation). Furthermore, the rs-FC combined with SVR could successfully predict the neurological recovery in CSM patients. Additionally, our results from cross-site validation analyses exhibited good reproducibility and generalization across the two datasets. Therefore, our findings provide preliminary evidence toward the development of novel strategies to predict neurological recovery in CSM patients using rs-fMRI and machine learning technique.

scholarly journals Systematic Evaluation of Machine Learning Algorithms for Neuroanatomically-Based Age Prediction in Youth

2021 ◽  
Author(s):  
Mandana Modabbernia ◽  
Heather C Whalley ◽  
David Glahn ◽  
Paul M. Thompson ◽  
Rene S. Kahn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Efficiency ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sensitivity Analyses ◽  
Gradient Boosting ◽  
Support Vector ◽  
Age Related ◽  
Extreme Gradient Boosting ◽  
Brain Age

Application of machine learning algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the machine learning approach in estimating brain-age in children and adolescents is important because age-related brain changes in these age-groups are dynamic. However, the comparative performance of the multiple machine learning algorithms available has not been systematically appraised. To address this gap, the present study evaluated the accuracy (Mean Absolute Error; MAE) and computational efficiency of 21 machine learning algorithms using sMRI data from 2,105 typically developing individuals aged 5 to 22 years from five cohorts. The trained models were then tested in an independent holdout datasets, comprising 4,078 pre-adolescents (aged 9-10 years). The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree-based, and kernel-based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross-validation folds, and sample size. The best performing algorithms were Extreme Gradient Boosting (MAE of 1.25 years for females and 1.57 years for males), Random Forest Regression (MAE of 1.23 years for females and 1.65 years for males) and Support Vector Regression with Radial Basis Function Kernel (MAE of 1.47 years for females and 1.72 years for males) which had acceptable and comparable computational efficiency. Findings of the present study could be used as a guide for optimizing methodology when quantifying age-related changes during development.

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