As academic demands increase, school-aged children and adolescents often carry heavy backpacks, impacting their posture and musculoskeletal health. This study examines how backpack carriage affects children's gait, focusing on the influence of waist strap design. In this study, twelve children underwent biomechanical tests while walking and running with and without backpacks, with weights adjusted based on daily habits. It can be found that waist strap-equipped backpacks significantly increased load response (F = 58.031, P < .001) and pre-swing phases (F = 58.031, P < .001) during both activities. In running, these backpacks also prolonged load response (F = 3.10, P = 0.004) and pre-swing phases (F = 3.10, P = 0.004). The study concludes that waist strap-equipped backpacks alter gait dynamics in children, affecting phases and contact times without impacting symmetry. This underscores the importance of waist straps in enhancing stability and reducing fatigue during backpack carriage.

Gymnastics moves are complex and varied, needing precise technique and body coordination, which traditional biomechanics methods struggle to capture in detail. This study aims to look at and judge how well new motion capture and analysis technology works in gymnastics biomechanics. This study picks the kipping bar muscle up move and uses the IMU-based Xsens system and the GymAware RS unit power test system to finely look at how athletes do the move in terms of body position, power, work done by the body, and main upper limb joint movements. The study tested 8 male elite collegiate gymnasts, collecting movement data with Xsens and power data with GymAware RS unit. Results show the kipping bar muscle up takes 1.42 seconds, with a 1.13-meter shift of the body's center and a peak speed of 3.40m/s. In terms of power, the peak output was 2772.96J/s, showing the need for explosive power and fast strength. Also, the total work done was 889.70J, showing the move's efficiency and energy level. This study shows that new motion capture and analysis tech is effective in capturing complex gymnastics moves. The use of these techs not only expands the ways biomechanics can be studied but also helps in making training better and improving how efficiently moves are done.

Diabetic foot is a common complication in patients with diabetes, which can lead to plantar ulcers and even necessitate amputation. This study aims to utilize finite element analysis to simulate the offloading effects of 3D-printed insoles with various structures on plantar pressure and to explore the use of machine learning in providing optimal plantar pressure offloading solutions for patients with diabetic foot. The results demonstrated that negative Poisson's ratio structured insoles were more effective in reducing plantar pressure (reducing pressure by an average of 39.2%) than barefoot and conventional structures. This was achieved through a unique lateral contraction deformation, which increased the contact area with the foot. The pressure-reducing effect of insoles may be weight-related, suggesting that heavier patients may require stiffer insoles. However, the machine learning algorithm demonstrated a poor fit (only 60.75%) in the task of recommending suitable insoles. In conclusion, this study demonstrated the significant effect of negative Poisson's ratio structured insoles in reducing plantar pressure in diabetic patients, providing new ideas for diabetic foot protection. With the development of data analysis technology in the future, the feasibility and application of personalised insole design will be more promising.