Effect of hip abduction assistance on metabolic cost and balance during human walking.

The use of wearable robots to provide walking assistance has rapidly grown over the past decade, with notable advances made in robot design and control methods toward reducing physical effort while performing an activity. The reduction in walking effort has mainly been achieved by assisting forward progression in the sagittal plane. Human gait, however, is a complex movement that combines motions in three planes, not only the sagittal but also the transverse and frontal planes. In the frontal plane, the hip joint plays a key role in gait, including balance. However, wearable robots targeting this motion have rarely been investigated. In this study, we developed a hip abduction assistance wearable robot by formulating the hypothesis that assistance that mimics the biological hip abduction moment or power could reduce the metabolic cost of walking and affect the dynamic balance. We found that hip abduction assistance with a biological moment second peak mimic profile reduced the metabolic cost of walking by 11.6% compared with the normal walking condition. The assistance also influenced balance-related parameters, including the margin of stability. Hip abduction assistance influenced the center-of-mass movement in the mediolateral direction. When the robot assistance was applied as the center of mass moved toward the opposite leg, the assistance replaced some of the efforts that would have otherwise been provided by the human. This indicates that hip abduction assistance can reduce physical effort during human walking while influencing balance.

Reducing sprint time with exosuit assistance in the real world.

Hip extension assistance with the aid of exosuits can reduce sprinting time.

Lighter and Simpler Design Paradigm for Widespread Use of Ankle Exosuits Based on Bio-Inspired Patterns.

Soft wearable robots are attracting immense attention owing to their high usability and wearability. In particular, studies on soft exosuits have achieved remarkable progress. Walking is one of the most basic human actions in daily life. During walking, the ankle joint has considerable influence. Therefore, an exosuit design paradigm having a light and simple structure was developed with the goal of fabricating a soft exosuit that supports the ankle. The new exosuit matches the performance of existing exosuits while being as comfortable as everyday wear. A walking test through a combination with a mobile actuator system, which can maximize these advantages, was also conducted. The combination with the mobile system demonstrates the potential of using the new ankle exosuit as inner wear that maximizes the advantages of a lighter and simpler design. The exosuit design paradigm could serve as an effective guideline for manufacturing assistive exosuits for various body parts in the future.

Deep-learning approach for predicting laser-beam absorptance in full-penetration laser keyhole welding.

Laser-beam absorptance in a keyhole is generally calculated using either a ray-tracing method or electrodynamic simulation, both physics-based. As such, the entire computation must be repeated when the keyhole geometry changes. In this study, a data-based deep-learning model for predicting laser-beam absorptance in full-penetration laser keyhole welding is proposed. The model uses a set of keyhole top- and bottom-aperture as inputs. From these, an artificial intelligence (AI) model is trained to predict the laser-energy absorptance value. For the training dataset, various keyhole geometries (i.e., top- and bottom-aperture shapes) are hypothetically created, upon which the ray-tracing model is employed to compute the corresponding absorptance values. An image classification model, ResNet, is employed as a learning recognizer of features to predict absorptance. For image regression, several modifications are applied to the structure. Five model depths are tested, and the optimal AI architecture is used to predict the absorptance with an R2 accuracy of 99.76% within 1.66 s for 740 keyhole shapes. Using this model, several keyhole parameters affecting the keyhole absorptance are identified.