A tethered exosuit reduces the metabolic cost of running.

Human body enhancement is an interesting branch of robotics. It focuses on wearable robots in order to improve the performance of human body, reduce energy consumption and delay fatigue, as well as increase body speed. Robot-assisted equipment, such as wearable exoskeletons, are wearable robot systems that integrate human intelligence and robot power. After careful design and adaptation, the human body has energy-saving sports, but it is an arduous task for the exoskeleton to achieve considerable reduction in metabolic rate. Therefore, it is necessary to understand the biomechanics of human sports, the body, and its weaknesses. In this study, a lower limb exoskeleton was classified according to the power source, and the working principle, design idea, wearing mode, material and performance of different types of lower limb exoskeletons were compared and analyzed. The study shows that the unpowered exoskeleton robot has inherent advantages in endurance, mass, volume, and cost, which is a new development direction of robot exoskeletons. This paper not only summarizes the existing research but also points out its shortcomings through the comparative analysis of different lower limb wearable exoskeletons. Furthermore, improvement measures suitable for practical application have been provided.

The application of an effective and reliable foot type classification method is very important for foot type judgment, injury risk assessment, and correction. Therefore, this paper mainly aims to propose a new foot type classification method for young people based on bitmap index (BI), compare it with the traditional footprint classification method, and put forward and analyze the factors affecting foot type classification. Thirty-one healthy volunteers were asked to perform two types tests in order to study the plantar pressure distribution with static and dynamic conditions, the first type is footprint test with full load of static, and the other type is plantar pressure distribution, which contains four different tests: no load, half load, and full load of static state, as well as dynamic plantar pressure distribution during process of walking. The Intraclass Correlation Coefficient results (ICC) were good reliable and reproducible for BI value with dynamic test (DT-BI value 0.738, 95% confidence interval [0.535, 0.848], [Formula: see text]) and full-load (FL-BI value 0.725 [0.281, 0.814], [Formula: see text], p < 0.001), obtained with individual measures and a two-way mixed-effects model. It can be seen from Kappa coefficient and density map that DT-BI has high classification accuracy. Classification of foot type based on bitmap index values showed good reliability in people with varying BMI, which can help clinicians and researchers segment the sample population to better distinguish between different foot types of activity, gait or treatment effects.

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