Influence of the hip joint modeling approaches on the kinematics of human gait

The influence of the hip joint formulation on the kinematic response of the model of human gait is investigated throughout this work. To accomplish this goal, the fundamental issues of the modeling process of a planar hip joint under the framework of multibody systems are revisited. In particular, the formulations for the ideal, dry, and lubricated revolute joints are described and utilized for the interaction of femur head inside acetabulum or the hip bone. In this process, the main kinematic and dynamic aspects of hip joints are analyzed. In a simple manner, the forces that are generated during human gait, for both dry and lubricated hip joint models, are computed in terms of the system’s state variables and subsequently introduced into the dynamics equations of motion of the multibody system as external generalized forces. Moreover, a human multibody model is considered, which incorporates the different approaches for the hip articulation, namely ideal joint, dry, and lubricated models. Finally, several computational simulations based on different approaches are performed, and the main results presented and compared to identify differences among the methodologies and procedures adopted in this work. The input conditions to the models correspond to the experimental data capture from an adult male during normal gait. In general, the obtained results in terms of positions do not differ significantly when the different hip joint models are considered. In sharp contrast, the velocity and acceleration plotted vary significantly. The effect of the hip joint modeling approach is clearly measurable and visible in terms of peaks and oscillations of the velocities and accelerations. In general, with the dry hip model, intra-joint force peaks can be observed, which can be associated with the multiple impacts between the femur head and the cup. In turn, when the lubricant is present, the system’s response tends to be smoother due to the damping effects of the synovial fluid.

O acesso enquanto direito humano: relato da Conferência Federation of International Human Rights Museums

Centro em Rede de Investigação em Antropologia UID/ANT/04038/2013

Silica nanoparticles as dexamethasone delivery systems able to induce the osteogenic differentiation of human bone marrow-derived mesenchymal stem cells

Bioactive glasses, especially silica-based materials, are reported to pres- ent osteoconductive and osteoinductive properties, fundamental char- acteristics in bone regeneration [1,2]. Additionally, dexamethasone (Dex) is one of the bioactive agents able to induce the osteogenic differ- entiation of mesenchymal stem cells by increasing the alkaline phos- phatase activity, and the expression levels of Osteocalcin and Bone Sialoprotein [3]. Herein, we synthesised silica (SiO2) nanoparticles (that present inherent bioactivity and ability to act as a sustained drug delivery system), and coated their surface using poly-L-lysine (PLL) and hyaluronic acid (HA) using the layer-by-layer processing technique. Further on, we studied the influence of these new SiO2-polyelectrolyte coated nanoparticles as Dex sustained delivery systems. The SiO2 nanoparticles were loaded with Dex (SiO2-Dex) and coated with PLL and HA (SiO2-Dex-PLL-HA). Their Dex release profile was evaluated and a more sustained release was obtained with the SiO2-Dex-PLL-HA. All the particles were cultured with human bone marrow-derived mes- enchymal stem cells (hBMSCs) under osteogenic differentiation culture conditions. hBMSCs adhered, proliferated and differentiated towards the osteogenic lineage in the presence of SiO2 (DLS 174nm), SiO2-Dex (DLS 175nm) and SiO2-Dex-PLL-HA (DLS 679nm). The presence of these materials induced the overexpression of osteogenic transcripts, namely of Osteocalcin, Bone Sialoprotein and Runx2. Scanning Elec- tron Microscopy/Electron Dispersive Spectroscopy analysis demon- strated that hBMSCs synthesised calcium phosphates when cultured with SiO2-Dex and SiO2-Dex-PLL-HA nanoparticles. These results indi- cate the potential use of these SiO2-polyelectrolytes coated nanoparti- cles as dexamethasone delivery systems capable of promoting osteogenic differentiation of hBMSCs.

In vitro chondrogenic commitment of human Wharton's jelly stem cells by co-culture with human articular chondrocytes.

Wharton's jelly stem cells (WJSCs) are a potential source of transplantable stem cells in cartilage-regenerative strategies, due to their highly proliferative and multilineage differentiation capacity. We hypothesized that a non-direct co-culture system with human articular chondrocytes (hACs) could enhance the potential chondrogenic phenotype of hWJSCs during the expansion phase compared to those expanded in monoculture conditions. Primary hWJSCs were cultured in the bottom of a multiwell plate separated by a porous transwell membrane insert seeded with hACs. No statistically significant differences in hWJSCs duplication number were observed under either of the culture conditions during the expansion phase. hWJSCs under co-culture conditions show upregulations of collagen type I and II, COMP, TGFβ1 and aggrecan, as well as of the main cartilage transcription factor, SOX9, when compared to those cultured in the absence of chondrocytes. Chondrogenic differentiation of hWJSCs, previously expanded in co-culture and monoculture conditions, was evaluated for each cellular passage using the micromass culture model. Cells expanded in co-culture showed higher accumulation of glycosaminoglycans (GAGs) compared to cells in monoculture, and immunohistochemistry for localization of collagen type I revealed a strong detection signal when hWJSCs were expanded under monoculture conditions. In contrast, type II collagen was detected when cells were expanded under co-culture conditions, where numerous round-shaped cell clusters were observed. Using a micromass differentiation model, hWJSCs, previously exposed to soluble factors secreted by hACs, were able to express higher levels of chondrogenic genes with deposition of cartilage extracellular matrix components, suggesting their use as an alternative cell source for treating degenerated cartilage.

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.