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.

The performanceof previous machine learning models for gait phase is only satisfactory under limited conditions. First, they produce accurate estimations only when the ground truth of the gait phase (of the target subject) is known. In contrast, when the ground truth of a target subject is not used to train an algorithm, the estimation error noticeably increases. Expensive equipment is required to precisely measure the ground truth of the gait phase. Thus, previous methods have practical shortcoming when they are optimized for individual users. To address this problem, this study introduces an unsupervised domain adaptation technique for estimation without the true gait phase of the target subject. Specifically, a domain-adversarial neural network was modified to perform regression on continuous gait phases. Second, the accuracy of previous models can be degraded by variations in stride time. To address this problem, this study developed an adaptive window method that actively considers changes in stride time. This model considerably reduces estimation errors for walking and running motions. Finally, this study proposed a new method to select the optimal source subject (among several subjects) by defining the similarity between sequential embedding features.

A tuned mass damper (TMD) is a system that effectively reduces the vibrations of floating offshore wind turbines (FOWTs). To maximize the performance of TMDs, it is necessary to optimize their design parameters (i.e., stiffness, damping, and installation location). However, this optimization process is challenging because of the existence of multiple local minima. Although various methods have been proposed to determine the global minimum (e.g., exhaustive search, genetic algorithms, and artificial fish swarm algorithms), they are computationally intensive. To address this issue, a novel optimization approach based on a parent nested optimizing structure and approximative search is proposed in this paper. The approximative search determines an initial parameter set (close to the optimal set) with fewer calculations. Then, the global minimum can be rapidly determined using the nested and parent optimizers. The effectiveness of this approach was verified with an FOWT exposed to stochastic winds. The results show that this approach is 30–55 times faster than a conventional global optimization method.

For gait phase estimation, time-series data of lower-limb motion can be segmented according to time windows. Time-domain features can then be calculated from the signal enclosed in a time window. A set of time-domain features is used for gait phase estimation. In this approach, the components of the feature set and the length of the time window are influential parameters for gait phase estimation. However, optimal parameter values, which determine a feature set and its values, can vary across subjects. Previously, these parameters were determined empirically, which led to a degraded estimation performance. To address this problem, this paper proposes a new feature extraction approach. Specifically, the components of the feature set are selected using a binary genetic algorithm, and the length of the time window is determined through Bayesian optimization. In this approach, the two optimization techniques are integrated to conduct a dual optimization task. The proposed method is validated using data from five walking and five running motions. For walking, the proposed approach reduced the gait phase estimation error from 1.284% to 0.910%, while for running, the error decreased from 1.997% to 1.484%.