Locomotor impairment is a high-prevalent and significant source of disability and significantly impacts a large population’s quality of life. Despite decades of research in human locomotion, the challenges of simulating human movement to study the features of musculoskeletal drivers and clinical conditions remain. Most recent efforts in utilizing reinforcement learning (RL) techniques are promising to simulate human locomotion and reveal musculoskeletal drives. However, these simulations often failed to mimic natural human locomotion because most reinforcement strategies have yet to consider any reference data regarding human movement. To address these challenges, in this study, we designed a reward function based on the trajectory optimization rewards (TOR), and bio-inspired rewards, which includes the rewards obtained from reference motion data captured by a single Intertial Moment Unit (IMU) sensor. The sensor was equipped on the participants’ pelvis to capture reference motion data. Also, we adapted the reward function by leveraging previous research in walking simulation for TOR. The experimental results showed that the simulated agents with the modified reward function performed better in mimicking the collected IMU data from participants, which means the simulated human locomotion was more realistic. Also, as this bio-inspired defined cost, IMU data enhanced the agent’s capacity to converge during the training process. As a result, the models’ convergence is faster than those developed without reference motion data. Consequently, human locomotion can be simulated more quicker and in a broader range of environments with a better simulation performance.
IntroductionHuman locomotion is affected by several factors, such as growth and aging, health conditions, and physical activity levels for maintaining overall health and well-being. Notably, impaired locomotion is a prevalent cause of disability, significantly impacting the quality of life of individuals. The uniqueness and high prevalence of human locomotion have led to a surge of research to develop experimental protocols for studying the brain substrates, muscle responses, and motion signatures associated with locomotion. However, from a technical perspective, reproducing locomotion experiments has been challenging due to the lack of standardized protocols and benchmarking tools, which impairs the evaluation of research quality and the validation of previous findings.MethodsThis paper addresses the challenges by conducting a systematic review of existing neuroimaging studies on human locomotion, focusing on the settings of experimental protocols, such as locomotion intensity, duration, distance, adopted brain imaging technologies, and corresponding brain activation patterns. Also, this study provides practical recommendations for future experiment protocols.ResultsThe findings indicate that EEG is the preferred neuroimaging sensor for detecting brain activity patterns, compared to fMRI, fNIRS, and PET. Walking is the most studied human locomotion task, likely due to its fundamental nature and status as a reference task. In contrast, running has received little attention in research. Additionally, cycling on an ergometer at a speed of 60 rpm using fNIRS has provided some research basis. Dual-task walking tasks are typically used to observe changes in cognitive function. Moreover, research on locomotion has primarily focused on healthy individuals, as this is the scenario most closely resembling free-living activity in real-world environments.DiscussionFinally, the paper outlines the standards and recommendations for setting up future experiment protocols based on the review findings. It discusses the impact of neurological and musculoskeletal factors, as well as the cognitive and locomotive demands, on the experiment design. It also considers the limitations imposed by the sensing techniques used, including the acceptable level of motion artifacts in brain-body imaging experiments and the effects of spatial and temporal resolutions on brain sensor performance. Additionally, various experiment protocol constraints that need to be addressed and analyzed are explained.
In this research, a novel method is developed to manipulate smart structures' natural frequencies to eliminate or alleviate the detrimental effects caused by vibrating close to the natural frequencies. To this end, this work considers a sandwich plate structure with Terfenol-D, which is a magnetostrictive material, comprising its middle layer. The stiffness of this smart material changes based on the magnetic field that it is exposed to. Thus, natural frequencies and resonances of the whole structure can be manipulated. Furthermore, in this research, the Terfenol-D in the middle layer is divided into five parallel sections so that each of them can be controlled separately. Therefore, it is possible to selectively activate portions of the magnetostrictive layers that run parallel along one of the plate's directions to create periodic changes in the structure's stiffness. Thus, the structure can be kept safe when excitations or disturbances approach one of its natural frequencies by activating sections to produce configurations that modify the natural frequencies. To this end, the structure's natural frequencies are obtained analytically for a thin plate with Kirchhoff equations. Then, the results are verified by the numerical results obtained using the finite element method. Moreover, activating certain portions of the Terfenol-D layer provides a periodic structure with a band gap that can filter out oscillatory motions with frequencies that fall within the band gap. This structure's band gap has been examined in two 1D periodic, two 2D periodic, and two non-periodic conditions using the finite element method.
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