In recent years, researchers working in biomedical science and technology have investigated alternatives for enhancing the mechanical properties of biomedical materials. In this work, sodium alginate (SA) hydrogel-reinforced nanoparticles (NPs) of hydroxyapatite (HA) were prepared to enhance the mechanical properties of this polymer. Compression tests showed an increase of 354.54% in ultimate compressive strength (UCS), and 154.36% in Young’s modulus with the addition of these NPs compared with pure SA. Thermogravimetric analysis (TGA) revealed that the amount of residual water is not negligible and covered a range from 20 to 35 wt%, and the decomposition degree of the alginate depends on the hydroxyapatite content, possibly due to the displacement of sodium ions by the hydroxyapatite and not by calcium chloride. Further, there is an important effect possibly due to the existence of an interaction of hydrogen bonds between the hydroxyl of the alginate and the oxygen atoms of the hydroxyapatite, so signals appear upfield in nuclear magnetic resonance (NMR) data. An increase in the accumulation of HA particles was observed with the use of X-ray microtomography, in which the quantified volume of particles per reconstructed volume corresponded accordingly to the increase in the mechanical properties of the hydrogel.

This work presents a survey literature review on biomechanics, specifically aimed at the study of existent biomechanical tools through video analysis, in order to identify opportunities for researchers in the field, and discuss future proposals and perspectives. Scientific literature (journal papers and conference proceedings) in the field of video-based biomechanics published after 2010 were selected and discussed. The most common application of the study of biomechanics using this technique is sports, where the most reported applications are american football, soccer, basketball, baseball, jumping, among others. These techniques have also been studied in a less proportion, in ergonomy, and injury prevention. From the revised literature, it is clear that biomechanics studies mainly focus on the analysis of angles, speed or acceleration, however, not many studies explore the dynamical forces in the joints. The development of video-based biomechanic tools for force analysis could provide methods for assessment and prediction of biomechanical force associated risks such as injuries and fractures. Therefore, it is convenient to start exploring this field. A few case studies are reported, where force estimation is performed via manual tracking in different scenarios. This demonstration is carried out using conventional manual tracking, however, the inclusion of similar methods in an automated manner could help in the development of intelligent healthcare, force prediction tools for athletes and/or elderly population. Future trends and challenges in this field are also discussed, where data availability and artificial intelligence models will be key to proposing new and more reliable methods for biomechanical analysis.

We propose an experimental technique for the estimation of quality parameters of post-mortem human bone samples with signs of osteoporosis. Since we were interested in characterizing bone microstructure by evaluating porosity, trabecular thickness, and space, we obtained reference values of those parameters for some of our samples through micro-computerized tomography (micro-CT). Nevertheless, as such technology is not widely available, here we used a 2D imaging approximation of those quality parameters. We based our estimation on simple digital microscopy and calibrated to ensure a percentage of similarity higher than 95% in comparison to those values achieved through micro-CT. Additionally, we subjected our samples to mechanical testing to obtain a complete characterization of our samples and to provide a preliminary assessment of the possible relationship between clinical tests of bone mineral density (BMD) and bone quality parameters. Our results show that BMD may have very low specificity in the assessment of the authentic microstructure of bone, then a broader analysis of quality parameters is required to enhance the reliability of future osteoporosis diagnosis technologies.

Reliable and innovative methods for estimating forces are critical aspects of biomechanical sports research. Using them, athletes can improve their performance and technique and reduce the possibility of fractures and other injuries. For this purpose, throughout this project, we proceeded to research the use of video in biomechanics. To refine this method, we propose an RNN trained on a biomechanical dataset of regular runners that measures both kinematics and kinetics. The model will allow analyzing, extracting, and drawing conclusions about continuous variable predictions through the body. It marks different anatomical and reflective points (96 in total, 32 per dimension) that will allow the prediction of forces (N) in three dimensions ($F_x$, $F_y$, $F_z$), measured on a treadmill with a force plate at different velocities (2.5 m/s, 3.5 m/s, 4.5 m/s). In order to obtain the best model, a grid search of different parameters that combined various types of layers (Simple, GRU, LSTM), loss functions (MAE, MSE, MSLE), and sampling techniques (down-sampling, up-sampling) helped obtain the best performing model (LSTM, MSE, down-sampling) achieved an average coefficient of determination of 0.68, although when excluding $F_z$ it reached 0.92.

Cobalt-based alloys are widely used in the manufacture of joint prostheses. In this study, the effect of boron additions and heat treatment on the ASTM F75 was evaluated by rotating bending fatigue. The boron ranged from 0.06–1 wt %. The alloys were tested in as-cast and heat-treated conditions. In the as-cast condition, the infinite life was observed at 380 MPa, improving to 433–615 MPa according to the amount of boron added. In the heat treatment condition, the fatigue resistance was improved only in the base alloy. The addition of 0.06 wt % boron and heat treatment led to the same resistance as in the as-cast condition. Adding large amounts of boron combined with heat treatment diminished the fatigue limit. The fracture analysis revealed primarily brittle behaviour with some ductile features even on the same sample; only the heat-treated alloy with 0.06 wt % boron was clearly ductile. This alloy also exhibited notably better toughness to crack propagation.

Fatigue fractures in materials are the main cause of approximately 80% of all material failures, and it is believed that such failures can be predicted and mathematically calculated in a reliable manner. It is possible to establish prediction modalities in cases of fatigue fractures according to three fundamental variables in fatigue, such as volume, number of fracture cycles, as well as applied stress, with the integration of Weibull constants (length characteristic). In this investigation, mechanical fatigue tests were carried out on specimens smaller than 4 mm², made of different industrial materials. Their subsequent analysis was performed through precision computed tomography, in search for microfractures. The measurement of these microfractures, along with their metrics and classifications, was recorded. A convolutional neural network trained with deep learning was used to achieve the detection of microfractures in image processing. The detection of microfractures in images with resolutions of 480 × 854 or 960 × 960 pixels is the primary objective of this network, and its accuracy is above 95%. Images that have microfractures and those without are classified using the network. Subsequently, by means of image processing, the microfracture is isolated. Finally, the images containing this feature are interpreted using image processing to obtain their area, perimeter, characteristic length, circularity, orientation, and microfracture-type metrics. All values are obtained in pixels and converted to metric units (μm) through a conversion factor based on image resolution. The growth of microfractures will be used to define trends in the development of fatigue fractures through the studies presented.