Adult Skeletal Age-at-Death Estimation through Deep Random Neural Networks: A New Method and Its Computational Analysis

Age-at-death assessment is a crucial step in the identification process of skeletal human remains. Nonetheless, in adult individuals this task is particularly difficult to achieve with reasonable accuracy due to high variability in the senescence processes. To improve the accuracy of age-at-estimation, in this work we propose a new method based on a multifactorial macroscopic analysis and deep random neural network models. A sample of 500 identified skeletons was used to establish a reference dataset (age-at-death: 19-101 years old, 250 males and 250 females). A total of 64 skeletal traits are covered in the proposed macroscopic technique. Age-at-death estimation is tackled from a function approximation perspective and a regression approach is used to infer both point and prediction interval estimates. Based on cross-validation and computational experiments, our results demonstrate that age estimation from skeletal remains can be accurately (~6 years mean absolute error) inferred across the entire adult age span and informative estimates and prediction intervals can be obtained for the elderly population. A novel software tool, DRNNAGE, was made available to the community

SAMS: a decisional tool for selecting and evaluating sub-adult age estimation methods

International audienc

Sub-adult aging method selection (SAMS): A decisional tool for selecting and evaluating sub-adult age estimation methods based on standardized methodological parameters

International audienc

Dead weight: validation of mass regression equations on experimentally burned skeletal remains to assess skeleton completeness

In very fragmentary remains, the thorough inventory of skeletal elements is often impossible to accomplish. Mass has been used instead to assess the completeness of the skeleton. Two different mass-based methods of assessing skeleton completeness were tested on a sample of experimentally burned skeletons with the objective of determining which of them is more reliable. The first method was based on a simple comparison of the mass of each individual skeleton with previously published mass references. The second method was based on mass linear regressions from individual bones to estimate complete skeleton mass. The clavicle, humerus, femur, patella, metacarpal, metatarsal and tarsal bones were used. The sample was composed of 20 experimentally burned skeletons from 10 males and 10 females with ages-at-death between 68 and 90 years old. Results demonstrated that the regression approach is more objective and more reliable than the reference comparison approach even though not all bones provided satisfactory estimations of the complete skeleton mass. The femur, humerus and patella provided the best performances among the individual bones. The estimations based on the latter had root mean squared errors (RMSE) smaller than 300 g. Results demonstrated that the regression approach is quite promising although the patella was the only reasonable predictor expected to survive sufficiently intact to a burning event at high temperatures. The mass comparison approach has the advantage of not depending on the preservation of individual bones. Whenever bones are intact though, the application of mass regressions should be preferentially used because it is less subjective

AncesTrees: ancestry estimation with randomized decision trees

In forensic anthropology, ancestry estimation is essential in establishing the individual biological profile. The aim of this study is to present a new program--AncesTrees--developed for assessing ancestry based on metric analysis. AncesTrees relies on a machine learning ensemble algorithm, random forest, to classify the human skull. In the ensemble learning paradigm, several models are generated and co-jointly used to arrive at the final decision. The random forest algorithm creates ensembles of decision trees classifiers, a non-linear and non-parametric classification technique. The database used in AncesTrees is composed by 23 craniometric variables from 1,734 individuals, representative of six major ancestral groups and selected from the Howells' craniometric series. The program was tested in 128 adult crania from the following collections: the African slaves' skeletal collection of Valle da Gafaria; the Medical School Skull Collection and the Identified Skeletal Collection of 21st Century, both curated at the University of Coimbra. The first step of the test analysis was to perform ancestry estimation including all the ancestral groups of the database. The second stage of our test analysis was to conduct ancestry estimation including only the European and the African ancestral groups. In the first test analysis, 75% of the individuals of African ancestry and 79.2% of the individuals of European ancestry were correctly identified. The model involving only African and European ancestral groups had a better performance: 93.8% of all individuals were correctly classified. The obtained results show that AncesTrees can be a valuable tool in forensic anthropology

Towards automatic non-metric traits analysis on 3D models of skulls

The morphological and metric methods used by anthropologists to assess ancestry can generate results with low repeatability besides damaging the specimens while handling. These problems have led to the development of a new approach based on skulls acquisition with a 3D scanner, using the resulting models to make measurements and morphological analyzes in the CraMs application Craniometric Measurements). This paper focuses on the development of new methods for the morphological analysis, and the extraction and classification of structures with the objective of reducing inter and intra observer variability. The final aim is to ease the process of estimating the individual’s ancestry

BEHAVIOUR OF GLOBALLY CHAOTIC PARAMETERS OF HEART RATE VARIABILITY FOLLOWING A PROTOCOL OF EXERCISE WITH FLEXIBLE POLE

Aim. The aim of this study was to evaluate the effect of flexible pole exercise on cardiac autonomic modulation. This was investigated while at rest before and, then in the recovery phase from the flexible pole exercise.Material and methods. Thirty-two female subjects were allocated to equal groups. The analysis of cardiac autonomic modulation was through the recording of temporal separations of interpeak RR intervals taken from the heart rate monitor. The analysis was performed by chaotic global measures of heart rate variability (HRV). Two parameters were proposed based on the greater resolution of multi-taper method (MTM) power spectra. They were high spectral entropy (hsEntropy) and high spectral detrended fluctuation analysis (hsDFA) and were applied owing to the greater parametric response in short data series. After applying Anderson-Darling and Lilliefors tests for confirmation of high non-normality; Kruskal-Wallis test of significance was used for the statistical analysis, with the level of significance moderately set at (p<0.15).Results. On recovery from flexible pole exercise there was a significant decrease in three of the combinations of CFP. The algorithm which applied all three chaotic global parameters was the optimum statistically measured by Kruskal-Wallis and standard deviation. It was also the most influential by principal component analysis (PCA) with almost all variation covered by the first two components.Conclusion. Flexible pole exercise leads to a further significant decrease in chaosity measured by the combination of chaotic globals

A method for sex estimation using the proximal femur

The assessment of sex is crucial to the establishment of a biological profile of an unidentified skeletal individual. The best methods currently available for the sexual diagnosis of human skeletal remains generally rely on the presence of well-preserved pelvic bones, which is not always the case. Postcranial elements, including the femur, have been used to accurately estimate sex in skeletal remains from forensic and bioarcheological settings. In this study, we present an approach to estimate sex using two measurements (femoral neck width [FNW] and femoral neck axis length [FNAL]) of the proximal femur. FNW and FNAL were obtained in a training sample (114 females and 138 males) from the Luis Lopes Collection (National History Museum of Lisbon). Logistic regression and the C4.5 algorithm were used to develop models to predict sex in unknown individuals. Proposed cross-validated models correctly predicted sex in 82.5-85.7% of the cases. The models were also evaluated in a test sample (96 females and 96 males) from the Coimbra Identified Skeletal Collection (University of Coimbra), resulting in a sex allocation accuracy of 80.1-86.2%. This study supports the relative value of the proximal femur to estimate sex in skeletal remains, especially when other exceedingly dimorphic skeletal elements are not accessible for analysis. (C) 2016 Elsevier Ireland Ltd. All rights reserved