Based on the 7-link dynamic model in the sagittal plane and the 5-link dynamic model in the lateral plane, the parametric gait of the biped robot is designed using walking velocity, step length and height of the hip. ...Based on the 7-link dynamic model in the sagittal plane and the 5-link dynamic model in the lateral plane, the parametric gait of the biped robot is designed using walking velocity, step length and height of the hip. According to the condition of the stability, body swings forward and backward to dynamically balance in sagittal plane and the whole biped swings left and right to dynamically balance in lateral plane. And the genetic algorithm is applied to obtain the optimal parameters on condition of keeping dynamic stability and the minimizing of the value of the dynamic balance.展开更多
Walking is the basic skill of a legged robot, and one of the promising ways to improve the walking performance and its adaptation to environment changes is to let the robot learn its walking by itself. Currently, most...Walking is the basic skill of a legged robot, and one of the promising ways to improve the walking performance and its adaptation to environment changes is to let the robot learn its walking by itself. Currently, most of the walking learning methods are based on robot vision system or some external sensing equipment to estimate the walking performance of certain walking parameters, and therefore are usually only applicable under laboratory condition, where environment can be pre-defined. Inspired by the rhythmic swing movement during walking of legged animals and the behavior of their adjusting their walking gait on different walking surfaces, a concept of walking rhythmic pattern（WRP） is proposed to evaluate the walking specialty of legged robot, which is just based on the walking dynamics of the robot. Based on the onboard acceleration sensor data, a method to calculate WRP using power spectrum in frequency domain and diverse smooth filters is also presented. Since the evaluation of WRP is only based on the walking dynamics data of the robot＇s body, the proposed method doesn＇t require prior knowledge of environment and thus can be applied in unknown environment. A gait learning approach of legged robots based on WRP and evolution algorithm（EA） is introduced. By using the proposed approach, a quadruped robot can learn its locomotion by its onboard sensing in an unknown environment, where the robot has no prior knowledge about this place. The experimental result proves proportional relationship exits between WRP match score and walking performance of legged robot, which can be used to evaluate the walking performance in walking optimization under unknown environment.展开更多
People with neurological disorders like Cerebral Palsy (CP) and Multiple Sclerosis (MS) suffer associated functional gait problems. The symptoms and sign of these gait deficits are different between subjects and even ...People with neurological disorders like Cerebral Palsy (CP) and Multiple Sclerosis (MS) suffer associated functional gait problems. The symptoms and sign of these gait deficits are different between subjects and even within a subject at different stage of the disease. Identifying these gait related abnormalities helps in the treatment planning and rehabilitation process. The current gait assessment process does not provide very specific information within the seven gait phases. The objective of this study is to investigate the possible application of granular computing to quantify gait parameters within the seven gait phases. In this process we applied fuzzy-granular computing on the vertical ground reaction force (VGRF) and surface electromyography (sEMG) data to obtain respective characteristic values for each gait phase. A fuzzy similarity (FS) measure is used to compare patient values with age and sex matched control able-bodied group. We specifically applied and tested this approach on 10 patients (4 Cerebral Palsy and 6 Multiple Sclerosis) to identify possible gait abnormalities. Different FS values for VGRF for right and left leg is observed. The VGRF analysis shows smaller FS values during the swing phase in CP and MS subjects that are evidence of associated stability problem. Similarly, FS values for muscle activates of the four-selected muscle display a broad range of values due to difference between subjects. Degraded FS values for different muscles at different stage of the gait cycle are reported. Smaller FS values are sign of abnormal activity of the respective muscles. This approach provides individual centered and very specific information within the gait phases that can be employed for diagnosis, treatment and rehabilitation process.展开更多
目的步态识别是交通管理、监控安防领域的关键技术,为了解决现有步态识别算法无法充分捕捉和利用人体生物特征,在协变量干扰下模型精度降低的问题,本文提出一种深度提取和融合步态特征与身形特征的高精度步态识别方法。方法首先使用高...目的步态识别是交通管理、监控安防领域的关键技术,为了解决现有步态识别算法无法充分捕捉和利用人体生物特征,在协变量干扰下模型精度降低的问题,本文提出一种深度提取和融合步态特征与身形特征的高精度步态识别方法。方法首先使用高分辨率网络(high resolution network,HRNet)提取出人体骨架关键点;以残差神经网络ResNet-50(residual network)为主干,利用深度残差模块的复杂特征学习能力,从骨架信息中充分提取相对稳定的身形特征与提供显性高效运动本质表达的步态特征;设计多分支特征融合(multi-branch feature fusion,MFF)模块,进行不同通道间的尺寸对齐与权重优化,通过动态权重矩阵调节各分支贡献,把身形特征和步态特征融合为区分度更强的总体特征。结果室内数据集采用跨视角多状态CASIA-B(Institute of Automation,Chinese Academy of Sciences)数据集,本文方法在跨视角实验中表现稳健;在多状态实验中,常规组的识别准确率为94.52%,外套干扰组在同类算法中的识别性能最佳。在开放场景数据集中,模型同样体现出较高的泛化能力,相比于现有算法,本文方法的准确率提升了4.1%。结论本文设计的步态识别方法充分利用了深度残差模块的特征提取能力与多特征融合的互补优势,面向复杂识别场景仍具有较高的模型识别精度与泛化能力。展开更多
基金the Equipment Research Institute of the Fujitsu CompanyJapan
文摘Based on the 7-link dynamic model in the sagittal plane and the 5-link dynamic model in the lateral plane, the parametric gait of the biped robot is designed using walking velocity, step length and height of the hip. According to the condition of the stability, body swings forward and backward to dynamically balance in sagittal plane and the whole biped swings left and right to dynamically balance in lateral plane. And the genetic algorithm is applied to obtain the optimal parameters on condition of keeping dynamic stability and the minimizing of the value of the dynamic balance.
基金supported by National Hi-tech Research and Development Program of China (863 Program, Grant No. 2009AA04Z213)"Dawn Tracking" Program of Shanghai Education Commission, China (Grant No. 10GG11)+1 种基金International Technology Co-operation Project (Grant No. 2010DFA12210)Shanghai Science and Technology Committee Talent Program of China (Grant No. 11XD1404800)
文摘Walking is the basic skill of a legged robot, and one of the promising ways to improve the walking performance and its adaptation to environment changes is to let the robot learn its walking by itself. Currently, most of the walking learning methods are based on robot vision system or some external sensing equipment to estimate the walking performance of certain walking parameters, and therefore are usually only applicable under laboratory condition, where environment can be pre-defined. Inspired by the rhythmic swing movement during walking of legged animals and the behavior of their adjusting their walking gait on different walking surfaces, a concept of walking rhythmic pattern（WRP） is proposed to evaluate the walking specialty of legged robot, which is just based on the walking dynamics of the robot. Based on the onboard acceleration sensor data, a method to calculate WRP using power spectrum in frequency domain and diverse smooth filters is also presented. Since the evaluation of WRP is only based on the walking dynamics data of the robot＇s body, the proposed method doesn＇t require prior knowledge of environment and thus can be applied in unknown environment. A gait learning approach of legged robots based on WRP and evolution algorithm（EA） is introduced. By using the proposed approach, a quadruped robot can learn its locomotion by its onboard sensing in an unknown environment, where the robot has no prior knowledge about this place. The experimental result proves proportional relationship exits between WRP match score and walking performance of legged robot, which can be used to evaluate the walking performance in walking optimization under unknown environment.
文摘People with neurological disorders like Cerebral Palsy (CP) and Multiple Sclerosis (MS) suffer associated functional gait problems. The symptoms and sign of these gait deficits are different between subjects and even within a subject at different stage of the disease. Identifying these gait related abnormalities helps in the treatment planning and rehabilitation process. The current gait assessment process does not provide very specific information within the seven gait phases. The objective of this study is to investigate the possible application of granular computing to quantify gait parameters within the seven gait phases. In this process we applied fuzzy-granular computing on the vertical ground reaction force (VGRF) and surface electromyography (sEMG) data to obtain respective characteristic values for each gait phase. A fuzzy similarity (FS) measure is used to compare patient values with age and sex matched control able-bodied group. We specifically applied and tested this approach on 10 patients (4 Cerebral Palsy and 6 Multiple Sclerosis) to identify possible gait abnormalities. Different FS values for VGRF for right and left leg is observed. The VGRF analysis shows smaller FS values during the swing phase in CP and MS subjects that are evidence of associated stability problem. Similarly, FS values for muscle activates of the four-selected muscle display a broad range of values due to difference between subjects. Degraded FS values for different muscles at different stage of the gait cycle are reported. Smaller FS values are sign of abnormal activity of the respective muscles. This approach provides individual centered and very specific information within the gait phases that can be employed for diagnosis, treatment and rehabilitation process.
文摘目的步态识别是交通管理、监控安防领域的关键技术,为了解决现有步态识别算法无法充分捕捉和利用人体生物特征,在协变量干扰下模型精度降低的问题,本文提出一种深度提取和融合步态特征与身形特征的高精度步态识别方法。方法首先使用高分辨率网络(high resolution network,HRNet)提取出人体骨架关键点;以残差神经网络ResNet-50(residual network)为主干,利用深度残差模块的复杂特征学习能力,从骨架信息中充分提取相对稳定的身形特征与提供显性高效运动本质表达的步态特征;设计多分支特征融合(multi-branch feature fusion,MFF)模块,进行不同通道间的尺寸对齐与权重优化,通过动态权重矩阵调节各分支贡献,把身形特征和步态特征融合为区分度更强的总体特征。结果室内数据集采用跨视角多状态CASIA-B(Institute of Automation,Chinese Academy of Sciences)数据集,本文方法在跨视角实验中表现稳健;在多状态实验中,常规组的识别准确率为94.52%,外套干扰组在同类算法中的识别性能最佳。在开放场景数据集中,模型同样体现出较高的泛化能力,相比于现有算法,本文方法的准确率提升了4.1%。结论本文设计的步态识别方法充分利用了深度残差模块的特征提取能力与多特征融合的互补优势,面向复杂识别场景仍具有较高的模型识别精度与泛化能力。
