Purpose: The body maintains stability by integrating inputs from the central nervous system of vision, hearing, proprioception, and multiple senses. With the development of smart wearable devices, smart wearable devices can provide real-time center of pressure position-assisted balance control, which is beneficial to maintain physical balance. Methods: Forty healthy college students (20 male, 20 female) participated in this study, and the posture balance actions of left-leg stance non-visual feedback, left-leg stance visual feedback, right-leg stance non-visual feedback, and right-leg stance visual feedback were performed. Visual feedback provided smart insoles matching Podoon APP on a tablet computer with the COP position displayed by a dot as real-time visual feedback. Results: The experimental results show that the displacement, velocity, radius, and area of the COP decreased significantly in the left-leg stance visual feedback/right-leg stance visual feedback, the test compared the parameters in the left-leg stance non-visual feedback/right-leg stance nonvisual feedback (P < 0.05). Providing visual feedback through intelligent insoles can reduce the movement of the center of mass and maintain physical stability for healthy young people of different genders. In the one leg visual/non-visual in standing, the COP maximum anteroposterior displacement, COP anteroposterior velocity, COP radius, and COP area in women are significantly smaller than in men (P < 0.05). Women have better real-time balance control ability than men with smart insoles. Conclusions: The simple intelligent wearable assisted devices can immediately increase the control ability in static stance of men and women, and women have better real-time balance control ability than men.
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The patients may show various patellar order samples. Patellofemoral disorders are defined in all cases with patellofemoral pain syndrome. Right and left knees belonging to the 60 men and 60 women volunteers were measured. By using the distances from the measurements, ligamentum patellae (LP) angle was estimated by artificial neural network (ANN) method. According to the results, one can confidently say that the method is capable for estimating this angle. The related root mean square error belonging to the test data of ANN is between 0.488 and 1.188. These angles for women are two times larger than men's. Eventually, the risk of patellofemoral disorders is greater for women than men.
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