Human Shape Variation Research Articles

Calcaneal shape variation in humans, nonhuman primates, and early hominins

The foot has played a prominent role in evaluating early hominin locomotion. The calcaneus, in particular, plays an important role in weight-bearing. Although the calcanei of early hominins have been previously scrutinized, a three-dimensional analysis of the entire calcaneal shape has not been conducted. Here, we investigate the relationship between external calcaneal shape and locomotion in modern Homo sapiens (n = 130), Gorilla (n = 86), Pan (n = 112), Pongo (n = 31), Papio (n = 28), and hylobatids (Hylobates, Symphalangus; n = 32). We use these results to place the calcanei attributed to Australopithecus sediba, A. africanus, A. afarensis, H. naledi, and Homo habilis/Paranthropus boisei into a locomotor context. Calcanei were scanned using either surface scanning or micro-CT and their external shape analyzed using a three-dimensional geometric morphometric sliding semilandmark analysis. Blomberg's K statistic was used to estimate phylogenetic signal in the shape data. Shape variation was summarized using a principal components analysis. Procrustes distances between all taxa as well as distances between each fossil and the average of each taxon were calculated. Blomberg's K statistic was small (K = 0.1651), indicating weak phylogenetic effects, suggesting variation is driven by factors other than phylogeny (e.g., locomotion or body size). Modern humans have a large calcaneus relative to body size and display a uniquely convex cuboid facet, facilitating a rigid midfoot for bipedalism. More arboreal great apes display relatively deeper cuboid facet pivot regions for increased midfoot mobility. Australopithecus afarensis demonstrates the most human-like calcaneus, consistent with obligate bipedalism. Homo naledi is primarily modern human-like, but with some intermediate traits, suggesting a different form of bipedalism than modern humans. Australopithecus africanus, A. sediba, and H. habilis/P. boisei calcanei all possess unique combinations of human and nonhuman ape-like morphologies, suggesting a combination of bipedal and arboreal behaviors.

Elderly fall detection based on multi-stream deep convolutional networks

Fall is the biggest threat to seniors, with significant emotional, physical and financial implications. It is the major cause of serious injuries, disabilities, hospitalizations and even death especially for elderly people living alone. Timely detection could provide immediate medical service to the injured and avoid its harmful consequences. Great number of vision-based techniques has been proposed by installing cameras in several everyday environments. Recently, deep learning has revolutionized these techniques, mostly using convolutional neural networks (CNNs). In this paper, we propose weighted multi-stream deep convolutional neural networks that exploit the rich multimodal data provided by RGB-D cameras. Our method detects automatically fall events and sends a help request to the caregivers. Our contribution is three-fold. We build a new architecture composed of four separate CNN streams, one for each modality. The first modality is based on a single combined RGB and depth image to encode static appearance information. RGB image is used to capture color and texture and depth image deals with illumination variations. In contrast of the first feature that lacks the contextual information about previous and next frames, the second modality characterizes the human shape variations. After background subtraction and person recognition, human silhouette is extracted and stacked to define history of binary motion HBMI. The last two modalities are used to more discriminate the motion information. Stacked amplitude and oriented flow are used in addition to stacked optical flow field to describe respectively the velocity, the direction and the motion displacements. The main motivation behind the use of these multimodal data is to combine complementary information such as motion, shape, RGB and depth appearance to achieve more accurate detection than using only one modality. Our second contribution is the combination of the four streams to generate the final decision for fall detection. We evaluate early and late fusion strategies and we have defined the weight of each modality based on its overall system performance. Weighted score fusion is finally adopted based on our experiments. In the third contribution, transfer learning and data augmentation are applied to increase the amount of training data, avoid over fitting and improve the accuracy. Experiments have been conducted on publicly available standard datasets and demonstrate the effectiveness of the proposed method compared to existing methods.

Novel genetic loci affecting facial shape variation in humans.

The human face represents a combined set of highly heritable phenotypes, but knowledge on its genetic architecture remains limited, despite the relevance for various fields. A series of genome-wide association studies on 78 facial shape phenotypes quantified from 3-dimensional facial images of 10,115 Europeans identified 24 genetic loci reaching study-wide suggestive association (p < 5 × 10-8), among which 17 were previously unreported. A follow-up multi-ethnic study in additional 7917 individuals confirmed 10 loci including six unreported ones (padjusted < 2.1 × 10-3). A global map of derived polygenic face scores assembled facial features in major continental groups consistent with anthropological knowledge. Analyses of epigenomic datasets from cranial neural crest cells revealed abundant cis-regulatory activities at the face-associated genetic loci. Luciferase reporter assays in neural crest progenitor cells highlighted enhancer activities of several face-associated DNA variants. These results substantially advance our understanding of the genetic basis underlying human facial variation and provide candidates for future in-vivo functional studies.

Novel Genetic Loci Affecting Facial Shape Variation in Humans

The human face represents a combined set of highly heritable phenotypes, but knowledge on its genetic architecture remains limited. A series of genome-wide association studies on 78 facial shape phenotypes quantified from 3-dimensional facial images of 10,115 Europeans identified 24 genetic loci, among which 17 were previously unpublished. A multi-ethnic replication study in 7,917 individuals confirmed 13 loci including 8 unpublished ones. Allele frequencies at the face-associated loci were significantly more differentiated among populations than genome background, supporting a role of nature selection in shaping genetic differentiation underlying facial variation. Analyses of epigenomic datasets from cranial neural crest cells revealed abundant cis-regulatory activities at the face-associated loci. Luciferase reporter assays in neural crest progenitor cells highlighted enhancer activities at INTU, PAX3, RPGRIP1L. Knocking-out two face-associated genes TBX15 and PAX1 in mice resulted in craniofacial dysmorphology. These results substantially advance our understanding of the genetic and functional basis of human facial variation.

A simple vision-based fall detection technique for indoor video surveillance

Falls are one of the major health hazards among the aging population aged 65 and above, which could potentially result in a significant hindrance to their independent living. With the advances in medical science in the last few decades, the aging population increases every year, and thus, fall detection system at home is increasingly important. This paper presents a new vision-based fall detection technique that is based on human shape variation where only three points are used to represent a person instead of the conventional ellipse or bounding box. Falls are detected by analyzing the shape change of the human silhouette through the features extracted from the three points. Experiment results show that in comparison with the conventional ellipse and bounding box techniques, the proposed three point–based technique increases the fall detection rate without increasing the computational complexity.

A video‐based human fall detection system for smart homes

In recent years, the global population has begun to age rapidly. Automatic fall detection for senior citizens has become an important issue for smart homes. This paper presents a novel video‐based human fall detection system that can detect a human fall in real‐time with a high detection rate. This fall detection system is based on an ingenious combination of skeleton features and human shape variations, which can efficiently distinguish “fall‐down incidents” from “fall‐like” ones. The experimental results indicate that the proposed human fall detection system can achieve a high detection rate and low false alarm rate.

Characterizing human shape variation using 3D anthropometric data

Characterizing the variations of the human body shape is fundamentally important in many applications ranging from animation to product design. 3D scanning technology makes it possible to digitize the complete surfaces of a large number of human bodies, providing much richer information about the body shape than traditional anthropometric measurements. This technology opens up opportunities to extract new measurements for quantifying the body shape. In this paper, we present a new method for extracting the main modes of variations of the human shape from a 3D anthropometric database. Previous approaches rely on anatomical landmarks. Using a volumetric representation, we show that human shape analysis can be performed despite the lack of such information. We first introduce a technique for repairing the 3D models from the original scans. Principal component analysis analysis is then applied to the volumetric description of a set of human models to extract dominant components of shape variability for a target population. We demonstrate a good reconstruction of the original models from a reduced number of components. Finally, we provide tools for visualizing the main modes of human shape variation.