A planar passive walking model with straight legs and round feet was discussed. This model can walk down steps, both on stairs with even steps and with random steps. Simulations showed that models with small moments o...A planar passive walking model with straight legs and round feet was discussed. This model can walk down steps, both on stairs with even steps and with random steps. Simulations showed that models with small moments of inertia can navigate large height steps. Period-doubling has been observed when the space between steps grows. This period-doubling has been validated by experiments, and the results of experiments were coincident with the simulation.展开更多
Recognizing and predicting the movement and intention of the wearer in control of an exoskeleton robot is very challenging. It is difficult for exoskeleton robots, which measure and drive human movements, to interact ...Recognizing and predicting the movement and intention of the wearer in control of an exoskeleton robot is very challenging. It is difficult for exoskeleton robots, which measure and drive human movements, to interact with humans. Therefore, many different types of sensors are needed. When using various sensors, a data design is needed for effective sensing. An electromyographic(EMG) signal can be used to identify intended motion before the actual movement, and the delay time can be shortened via control of the exoskeleton robot. Before using a lower limb exoskeleton to help in walking, the aim of this work is to distinguish the walking environment and gait period using various sensors, including the surface electromyography(sE MG) sensor. For this purpose, a gait experiment was performed on four subjects using the ground reaction force, human–robot interaction force, and position sensors with sEMG sensors. The purpose of this paper is to show progress with the use of sEMG when recognizing walking environments and the gait period with other sensors. For effective data design, we used a combination of sensor types, sEMG sensor locations, and sEMG features. The results obtained using an individual mechanical sensor together with sEMG showed improvement compared to the case of using an individual sensor, and the combination of sEMG and position information showed the best performance in the same number of combinations of three sensors. When four sensor combinations were used, the environment classification accuracy was 96.1%, and the gait period classification accuracy was 97.8%. Vastus medialis(VM) and gastrocnemius(GAS) were the most effective combinations of two muscle types among the five sEMG sensor locations on the legs, and the results were 74.4% in pre-heel contact(preHC) and 71.7% in pre-toe-off(preTO) for environment classification, and 68.0% for gait period classification, when using only the sE MG sensor. The two effective sE MG feature combinations were "mean absolute value(MAV), zero crossings(ZC)" an展开更多
人体运动雷达微多普勒能够为目标识别提供特征。由于行人雷达回波的复杂性,从雷达回波中提取运动参数难度很大。为了实现目标精确识别,提出了一种估计单人行走平动速度、步态周期和步长的方法。首先应用广义S变换(GST)得到雷达回波的微...人体运动雷达微多普勒能够为目标识别提供特征。由于行人雷达回波的复杂性,从雷达回波中提取运动参数难度很大。为了实现目标精确识别,提出了一种估计单人行走平动速度、步态周期和步长的方法。首先应用广义S变换(GST)得到雷达回波的微多普勒谱,然后提取出躯干部位的微多普勒分量,并将此分量转为一维时间频率序列,最后从序列中直接估计行走的平动速度和步态周期,用这2个估计值间接估计步长。仿真实验证明:本文方法抗噪声性能好,当信噪比大于4 d B时,估计精度高。展开更多
文摘A planar passive walking model with straight legs and round feet was discussed. This model can walk down steps, both on stairs with even steps and with random steps. Simulations showed that models with small moments of inertia can navigate large height steps. Period-doubling has been observed when the space between steps grows. This period-doubling has been validated by experiments, and the results of experiments were coincident with the simulation.
基金Project supported by the Agency for Defense Development and Defense Acquisition Program Administration(No.UD160059BD)
文摘Recognizing and predicting the movement and intention of the wearer in control of an exoskeleton robot is very challenging. It is difficult for exoskeleton robots, which measure and drive human movements, to interact with humans. Therefore, many different types of sensors are needed. When using various sensors, a data design is needed for effective sensing. An electromyographic(EMG) signal can be used to identify intended motion before the actual movement, and the delay time can be shortened via control of the exoskeleton robot. Before using a lower limb exoskeleton to help in walking, the aim of this work is to distinguish the walking environment and gait period using various sensors, including the surface electromyography(sE MG) sensor. For this purpose, a gait experiment was performed on four subjects using the ground reaction force, human–robot interaction force, and position sensors with sEMG sensors. The purpose of this paper is to show progress with the use of sEMG when recognizing walking environments and the gait period with other sensors. For effective data design, we used a combination of sensor types, sEMG sensor locations, and sEMG features. The results obtained using an individual mechanical sensor together with sEMG showed improvement compared to the case of using an individual sensor, and the combination of sEMG and position information showed the best performance in the same number of combinations of three sensors. When four sensor combinations were used, the environment classification accuracy was 96.1%, and the gait period classification accuracy was 97.8%. Vastus medialis(VM) and gastrocnemius(GAS) were the most effective combinations of two muscle types among the five sEMG sensor locations on the legs, and the results were 74.4% in pre-heel contact(preHC) and 71.7% in pre-toe-off(preTO) for environment classification, and 68.0% for gait period classification, when using only the sE MG sensor. The two effective sE MG feature combinations were "mean absolute value(MAV), zero crossings(ZC)" an
文摘人体运动雷达微多普勒能够为目标识别提供特征。由于行人雷达回波的复杂性,从雷达回波中提取运动参数难度很大。为了实现目标精确识别,提出了一种估计单人行走平动速度、步态周期和步长的方法。首先应用广义S变换(GST)得到雷达回波的微多普勒谱,然后提取出躯干部位的微多普勒分量,并将此分量转为一维时间频率序列,最后从序列中直接估计行走的平动速度和步态周期,用这2个估计值间接估计步长。仿真实验证明:本文方法抗噪声性能好,当信噪比大于4 d B时,估计精度高。
