Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity

Human age estimation is an important and difcult challenge. Diferent biomarkers and numerous approaches have been studied for biological age estimation, each with its advantages and limitations. In this work, we investigate whether physical activity can be exploited for biological age estimation for adult humans. We introduce an approach based on deep convolutional long short term memory (ConvLSTM) to predict biological age, using human physical activity as recorded by a wearable device. We also demonstrate fve deep biological age estimation models including the proposed approach and compare their performance on the NHANES physical activity dataset. Results on mortality hazard analysis using both the Cox proportional hazard model and Kaplan-Meier curves each show that the proposed method for estimating biological age outperforms other state-of-the-art approaches. This work has signifcant implications in combining wearable sensors and deep learning techniques for improved health monitoring, for instance, in a mobile health environment. Mobile health (mHealth) applications provide patients, caregivers, and administrators continuous information about a patient, even outside the hospital

Analysis of Travel Behavior in Khulna Metropolitan City, Bangladesh

In modeling travel demand and analyzing travel behavior, it is important to know the behavior of a large number of individuals. How the individuals choose an alternative among others given in the choice set, and how they assess and consider the different alternatives, must be a function of several factors including their need, task, socio-economic, environmental and the level of service offered by the various alternatives. A qualitative analysis of travel behavior was done with a number of individuals as the samples. To provide the required data, a field survey as direct home interview survey and travel time survey were conducted and given a number of 233 households and 871 respondents obtained as the samples. Meanwhile, the socio-economic data were obtained directly from the institution concerned. . The collected data were analyzed by using the Statistical Package for Social Science (SPSS) Software.The results of the analysis show that people with higher income and more automobile availability make more travel than people with low income and less automobile availability. The home-based trips take the largest percentage (50%) of people in the study area. The result also indicates that the shopping trips (15%) contribute higher among different trip purpose. The results also show that about 57% of individuals are between 20-50 years. The number of trips generated from each zone is strongly related to the amount of households, population, active workers and students of that zone. By considering a significant level of 5% four trip generation models have been developed. By using these models future trip generation from each zone can be determined. By applying the Gravity Model and the Fratar Method, the trip distribution models have been developed. Three basic models have been introduced by using travel time, road distance and straight distance as the resistance index. From these models the future travel pattern of Khulna Metropolitan city, Bangladesh can be predicted. Keywords: Khulna Metropolitan City, Household survey, Travel behavior, Trip Distribution Models, Trip Generation Models

Efficient design in building construction with rubber bearing in medium risk seismicity: case study and assessment

Earthquakes pose tremendous threats to life, property and a country's economy, not least due to their capability of destroying buildings and causing enormous structural damage. The hazard from ground excitations should be properly assessed to mitigate their action on building structures. This study is concerned with medium risk seismic regions. Specifically, the heavily populated capital city Dhaka in Bangladesh has been considered. Recent earthquakes that occurred inside and very close to the city have manifested the city's earthquake sources and vulnerability. Micro-seismicity data supports the existence of at least four earthquake source points in and around Dhaka. The effects of the earthquakes on buildings are studied for this region. Rubber base isolation is selected as an innovative option to lessen seismic loads on buildings. Case studies have been carried out for fixed and isolated based multi-storey buildings. Lead rubber bearing and high damping rubber bearing have been designed and incorporated in building bases. Structural response behaviours have been evaluated through static and dynamic analyses. For the probable severe earthquake, rubber bearing isolation can be a suitable alternative as it mitigates seismic effects, reduces structural responses and provides structural and economic benefits

Quantifying Human Biological Age: A Machine Learning Approach

Quantifying human biological age is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age prediction, each with its advantages and limitations. In this work, we first introduce a new anthropometric measure (called Surface-based Body Shape Index, SBSI) that accounts for both body shape and body size, and evaluate its performance as a predictor of all-cause mortality. We analyzed data from the National Health and Human Nutrition Examination Survey (NHANES). Based on the analysis, we introduce a new body shape index constructed from four important anthropometric determinants of body shape and body size: body surface area (BSA), vertical trunk circumference (VTC), height (H) and waist circumference (WC). SBSI is generally linear with age, and increases with increasing mortality, when compared with other popular anthropometric indices of body shape. We then investigate whether human body shape can be exploited for reliable age estimation for adult humans. We introduce a new multi-stage approach, based on human body measurements. Specifically, we develop an eigen body shape model, and use this to perform body shape clustering. Each cluster contains individuals with similar body shapes as captured by the eigen body shape model. First, we perform initial age estimation based on the body shape model. This initial estimate is then used to assign the subject into a probable age group. The second stage of estimation is then performed by using a specific estimation model as determined by the age group and body shape model. We then apply information from the neighborhood context to further improve estimation accuracy and stability. Experimental results show that, with appropriate modeling, human body shape can be used in human age estimation. We obtain a mean absolute error (MAE) of 5.90 years on the NHANES dataset, using 10-fold cross-validation. We then study the question of whether blood biomarkers can be used for reliable biological age estimation. We propose a new biological age estimation method, and investigate the performance of the new method against popular biological age estimation methods. We introduce a centroid based approach, using the notion of age neighborhoods. Specifically, we develop a model, based on which we compute biological age using blood biomarkers. Compared with current popular methods for biological age prediction, our results show that, the proposed age neighborhood model is robust, and results in improved performance in human biological age prediction. Furthermore, we investigate whether human locomotor activity can be exploited for biological age estimation for adult humans. We introduce an approach based on deep convolutional long short term memory (ConvLSTM) to predict biological age, using human physical activity as recorded by a wearable device. We consider five deep biological age estimation models including the proposed approach and compare their performance on the NHANES physical activity dataset. Results on mortality hazard prediction using both the Cox proportionality hazard model and Kaplan-Meier curves each show that the proposed method for estimating biological age outperforms other state-of-the-art approaches. This work has significant implications in various fields, such as health assessment, forensic science, biometrics, security, and in vaccination and immunization when the true age of the subject is unknown. Our work also has implications in combining wearable sensors and deep learning techniques for improved health monitoring, for instance, in a mobile health environment

Surface-Based Body Shape Index and Its Relationship with All-Cause Mortality

<div><p>Background</p><p>Obesity is a global public health challenge. In the US, for instance, obesity prevalence remains high at more than one-third of the adult population, while over two-thirds are obese or overweight. Obesity is associated with various health problems, such as diabetes, cardiovascular diseases (CVDs), depression, some forms of cancer, sleep apnea, osteoarthritis, among others. The body mass index (BMI) is one of the best known measures of obesity. The BMI, however, has serious limitations, for instance, its inability to capture the distribution of lean mass and adipose tissue, which is a better predictor of diabetes and CVDs, and its curved (“U-shaped”) relationship with mortality hazard. Other anthropometric measures and their relation to obesity have been studied, each with its advantages and limitations. In this work, we introduce a new anthropometric measure (called Surface-based Body Shape Index, SBSI) that accounts for both body shape and body size, and evaluate its performance as a predictor of all-cause mortality.</p><p>Methods and Findings</p><p>We analyzed data on 11,808 subjects (ages 18–85), from the National Health and Human Nutrition Examination Survey (NHANES) 1999–2004, with 8-year mortality follow up. Based on the analysis, we introduce a new body shape index constructed from four important anthropometric determinants of body shape and body size: body surface area (BSA), vertical trunk circumference (VTC), height (H) and waist circumference (WC). The surface-based body shape index (SBSI) is defined as follows:</p><p><math><mrow><mi>S</mi><mi>B</mi><mi>S</mi><mi>I</mi><mo>=</mo><mrow><mrow><mo>(</mo><mi>H</mi><mrow><mn>7</mn><mo>/</mo><mn>4</mn></mrow><mo>)</mo></mrow><mrow><mo>(</mo><mi>W</mi><mi>C</mi><mrow><mn>5</mn><mo>/</mo><mn>6</mn></mrow><mo>)</mo></mrow></mrow><mrow><mi>B</mi><mi>S</mi><mi>A</mi><mi>V</mi><mi>T</mi><mi>C</mi></mrow></mrow></math>(1)</p>SBSI has negative correlation with BMI and weight respectively, no correlation with WC, and shows a generally linear relationship with age. Results on mortality hazard prediction using both the Cox proportionality model, and Kaplan-Meier curves each show that SBSI outperforms currently popular body shape indices (e.g., BMI, WC, waist-to-height ratio (WHtR), waist-to-hip ratio (WHR), A Body Shape Index (ABSI)) in predicting all-cause mortality.<p></p><p>Conclusions</p><p>We combine measures of both body shape and body size to construct a novel anthropometric measure, the surface-based body shape index (SBSI). SBSI is generally linear with age, and increases with increasing mortality, when compared with other popular anthropometric indices of body shape.</p></div

Variation of different body shape indices with age (in years).

<p>Variation of different body shape indices with age (in years).</p

Relationship between BSA, VTC, height and WC for given BMI categories.

<p>The BSA and height, and VTC and height can predict the BMI categories (a, b). BSA and WC (and VTC and WC) show a non-linear relationship for a given BMI category (c, d).</p

The Kaplan Meier curves for four body shape indices using all subjects.

<p>The SBSI shows a better prediction performance than other body shape measures (with more separation between the curves, and less crossovers). 1st Q, 2nd Q, etc. denote respectively 1st quartile, 2nd quartile, etc.</p

Variation of relative death rate with increasing values of SBSI z-score.

<p>(a) Female; (b) Male.</p