Influence of Undergarments on the Comfort Level of Scoliosis Brace Wearers.

Bracing has proven to be an effective method for the conventional treatment of scoliosis in young people. A brace, a therapeutic device, covers the upper body and promotes healing by applying pressure to specific areas. However, wearing a scoliosis brace negatively affects the user's thermo-physiological well-being and often leads to discomfort. In this study, we investigated the influence of T-shirts as an undergarment on the thermo-physiological well-being of the brace wearer. For this purpose, we performed a comparative analysis of six T-shirts made from different special knitted fabrics. We carried out wearing tests in a computer-controlled climate chamber according to a predetermined protocol. The test subject wore the orthopedic brace over the different T-shirts at three different temperatures. The results indicate that the knitted fabrics of undergarments and environmental conditions considerably impact the wearer's thermo-physiological comfort. In the tests, the T-shirts made from the selected functional fabrics performed very well. The T-shirt made from the classic cotton fabric containing elastane yarn also performed well and was the most environmentally friendly. Currently, due to its lower price and easier availability, this cotton T-shirt can be recommended for wearing under a scoliosis brace.

Human Gait Activity Recognition Machine Learning Methods.

Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification/evaluation of drugs. This paper proposes a robust, wearable gait motion data acquisition system that allows either the classification of recorded gait data into desirable activities or the identification of common risk factors, thus enhancing the subject's quality of life. Gait motion information was acquired using accelerometers and gyroscopes mounted on the lower limbs, where the sensors were exposed to inertial forces during gait. Additionally, leg muscle activity was measured using strain gauge sensors. As a matter of fact, we wanted to identify different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of attention-based convolutional and recurrent neural networks algorithms outperformed the other tested algorithms and was individually tested further on the datasets of five subjects and delivered the following averaged results of classification: 98.9% accuracy, 96.8% precision, 97.8% sensitivity, 99.1% specificity and 97.3% F1-score. Moreover, the algorithm's robustness was also verified with the successful detection of freezing gait episodes in a Parkinson's disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization.

Analysis of Polygonal Computer Model Parameters and Influence on Fabric Drape Simulation.

Contemporary CAD systems enable 3D clothing simulation for the purpose of predicting the appearance and behavior of conventional and intelligent clothing in real conditions. The physical and mechanical properties of the fabric and the simulation parameters play an important role in this issue. The paper presents an analysis of the parameters of the polygonal computer model that affect fabric drape simulation. Experimental research on physical and mechanical properties were performed for nine fabrics. For this purpose, the values of the parameters for the tensile, bending, shear, and compression properties were determined at low loads, while the complex deformations were analyzed using Cusick drape meter devices. The fabric drape simulations were performed using the 2D/3D CAD system for a computer clothing design on a disk model, corresponding to real testing on the drape tester in order to allow a correlation analysis between the values of drape parameters of the simulated fabrics and the realistically measured values for each fabric. Each fabric was simulated as a polygonal model with a variable related to the side length of the polygon to analyze the influence of the polygon size, i.e., mesh density, on the model behavior in the simulation. Based on the simulated fabric drape shape, the values of the areas within the curves necessary to calculate the drape coefficients of the simulated fabrics were determined in the program for 3D modelling. The results were statistically processed and correlations between the values of the drape coefficients and the optimal parameters for simulating certain physical and mechanical properties of the fabric were determined. The results showed that the mesh density of the polygonal model is an important parameter for the simulation results.

Study on the Compression Effect of Clothing on the Physiological Response of the Athlete.

The study aimed to analyze whether the high compression of unique, tight-fitting sportswear influences the clothing physiology comfort of the athlete. Three specific sportswear with different compression were tested on four subjects while they were running on a treadmill with increasing intensity. The compression effect of the sportswear on the body of the test persons, the temperature distribution of the subjects, and the intensity of their perspiration during running were determined. The results indicate that the compression effect exerted by the garments significantly influences the clothing physiology comfort of the athlete; a higher compression load leads to more intense sweating and higher skin temperature.