Search Results (2)

Gait recognition has received widespread attention due to its non-intrusive recognition mechanism. Currently, most gait recognition methods use appearance-based recognition methods, and such methods are easily affected by occlusions when facing complex environments, which in turn affects the recognition accuracy. With the maturity of pose estimation techniques, model-based gait recognition methods have received more and more attention due to their robustness in complex environments. However, the current model-based gait recognition methods mainly focus on modeling the global feature information in the spatial dimension, ignoring the importance of local features and their influence on recognition accuracy. Meanwhile, in the temporal dimension, these methods usually use single-scale temporal information extraction, which does not take into account the inconsistency of the motion cycles of the limbs when a human body is walking (e.g., arm swing and leg pace), leading to the loss of some limb temporal information. To solve these problems, we propose a gait recognition network based on a Global–Local Graph Convolutional Network, called GaitMGL. Specifically, we introduce a new spatio-temporal feature extraction module, MGL (Multi-scale Temporal and Global–Local Spatial Extraction Module), which consists of GLGCN (Global–Local Graph Convolutional Network) and MTCN (Multi-scale Temporal Convolutional Network). GLGCN models both global and local features, and extracts global–local motion information. MTCN, on the other hand, takes into account the inconsistency of local limb motion cycles, and facilitates multi-scale temporal convolution to capture the temporal information of limb motion. In short, our GaitMGL solves the problems of loss of local information and loss of temporal information at a single scale that exist in existing model-based gait recognition networks. We evaluated our method on three publicly available datasets, CASIA-B, Gait3D, and GREW, and the experimental results show that our method demonstrates surprising performance and achieves an accuracy of 63.12% in the dataset GREW, exceeding all existing model-based gait recognition networks. Full article

One of the latest theories on ageing focuses on immune response, and considers the activation of subclinical and chronic inflammation. The study was designed to explain whether anti-inflammatory diet and lifestyle exercise affect an inflammatory profile in the Polish elderly population. Sixty individuals (80.2 ± 7.9 years) were allocated to a low-grade inflammation (LGI n = 33) or high-grade inflammation (HGI n = 27) group, based on C-reactive protein concentration (<3 or ≥3 mg/L) as a conventional marker of systemic inflammation. Diet analysis focused on vitamins D, C, E, A, β-carotene, n-3 and n-6 PUFA using single 24-h dietary recall. LGI demonstrated a lower n-6/n-3 PUFA but higher vitamin D intake than HGI. Physical performance based on 6-min walk test (6MWT) classified the elderly as physically inactive, whereby LGI demonstrated a significantly higher gait speed (1.09 ± 0.26 m/s) than HGI (0.72 ± 0.28 m/s). Circulating interleukins IL-1β, IL-6, IL-13, TNFα and cfDNA demonstrated high concentrations in the elderly with low 6MWT, confirming an impairment of physical performance by persistent systemic inflammation. These findings reveal that increased intake of anti-inflammatory diet ingredients and physical activity sustained throughout life attenuate progression of inflammaging in the elderly and indicate potential therapeutic strategies to counteract pathophysiological effects of ageing. Full article