In this study, an effective noncontact and nonattached technique that is based on electrostatic induction current generated during walking motion is proposed for the detection and assessment of human physical activity...In this study, an effective noncontact and nonattached technique that is based on electrostatic induction current generated during walking motion is proposed for the detection and assessment of human physical activity. In addition, a theoretical model is proposed for the electrostatic induction current generated owing to variation in the electric potential of the human body. The proposed electrostatic induction current model is compared with the theoretical model, and the proposed model is shown to effectively explain the behavior of the electrostatic induction current waveform. The normal walking motions of daily living are recorded with a portable sensor located in a regular house. The obtained results show that detailed information of physical activity such as a gait cycle can be estimated using our proposed technique. Additionally, the walking signal was measured when the subject walked with the ankle and knee fastened to a splint with bandages to simulate a limp. Therefore, the proposed technique, which is based on the detection of signal generated during walking, can be successfully employed to assess human physical activity.展开更多
The synthesis of human walking is of great interest in biomechanics and biomimetic engineering due to its predictive capabilities and potential applications in clinical biomechanics, rehabilitation engineering and bio...The synthesis of human walking is of great interest in biomechanics and biomimetic engineering due to its predictive capabilities and potential applications in clinical biomechanics, rehabilitation engineering and biomimetic robotics. In this paper, the various methods that have been used to synthesize humanwalking are reviewed from an engineering viewpoint. This involves a wide spectrum of approaches, from simple passive walking theories to large-scale computational models integrating the nervous, muscular and skeletal systems. These methods are roughly categorized under four headings： models inspired by the concept of a CPG （Central Pattern Generator）, methods based on the principles of control engineering, predictive gait simulation using optimisation, and models inspired by passive walking theory. The shortcomings and advantages of these methods are examined, and future directions are discussed in the context of providing insights into the neural control objectives driving gait and improving the stability of the predicted gaits. Future advancements are likely to be motivated by improved understanding of neural control strategies and the subtle complexities of the musculoskeletal system during human locomotion. It is only a matter of time before predictive gait models become a practical and valuable tool in clinical diagnosis, rehabilitation engineering and robotics.展开更多
Walking is a complex task which includes hundreds of muscles, bones and joints working together to deliver smooth movements. With the complexity, walking has been widely investigated in order to identify the pattern o...Walking is a complex task which includes hundreds of muscles, bones and joints working together to deliver smooth movements. With the complexity, walking has been widely investigated in order to identify the pattern of multi-segment movement and reveal the control mechanism. The degree of freedom and dimensional properties provide a view of the coordinative structure during walking, which has been extensively studied by using dimension reduction technique. In this paper, the studies related to the coordinative structure, dimensions detection and pattern reorganization during walking have been reviewed. Principal component analysis, as a popular technique, is widely used in the processing of human movement data. Both the principle and the outcomes of principal component analysis were introduced in this paper. This technique has been reported to successfully reduce the redundancy within the original data, identify the physical meaning represented by the extracted principal components and discriminate the different patterns. The coordinative structure during walking assessed by this technique could provide further information of the body control mechanism and correlate walking pattern with injury.展开更多
室内人员活动是室内颗粒物再悬浮的重要因素,研究室内颗粒物再悬浮对评估室内空气质量有重要意义.以南开大学津南校区某公共教室为研究区域,通过现场试验研究了室内地面不同积尘负荷下,人员行走引起的PM 2.5再悬浮浓度及其扩散速率.结...室内人员活动是室内颗粒物再悬浮的重要因素,研究室内颗粒物再悬浮对评估室内空气质量有重要意义.以南开大学津南校区某公共教室为研究区域,通过现场试验研究了室内地面不同积尘负荷下,人员行走引起的PM 2.5再悬浮浓度及其扩散速率.结果表明:①不同时间间隔内的室内地面总颗粒积尘负荷不同,时间间隔为3、7、15 d时,室内PM 2.5积尘负荷分别为0.11、0.18、0.30 g m 2.②室内地面不同总颗粒积尘负荷下,在室内中心过道处行走时引起的PM 2.5再悬浮浓度约1 min后达到最高值,PM 2.5再悬浮浓度达到最高值的时间与地面不同总颗粒积尘负荷的关系不明显.③随着室内地面总颗粒积尘负荷的增加,人体行走引起的PM 2.5再悬浮浓度也会增加.当室内PM 2.5积尘负荷为0.30 g m 2时,行走路径中游坐姿1.1 m处与站姿呼吸平面1.5 m处的PM 2.5再悬浮浓度平均值分别为3.03、2.68μg m 3,约是室内PM 2.5积尘负荷为0.18与0.11 g m 2时引起的PM 2.5再悬浮浓度平均值的2~3倍.④利用颗粒物传输模型对PM 2.5再悬浮进行量化分析发现,室内地面不同总颗粒积尘负荷的大小与PM 2.5再悬浮分数无关,PM 2.5再悬浮分数为2.2×10-8;室内PM 2.5积尘负荷为0.11、0.18、0.30 g m 2时,行走引起的再悬浮PM 2.5扩散速率分别为7.62×10-11、1.25×10-10、2.08×10-10 kg s.研究显示,地面积尘负荷的大小会影响人体行走时颗粒物的扩散速率与室内PM 2.5浓度.展开更多
文摘In this study, an effective noncontact and nonattached technique that is based on electrostatic induction current generated during walking motion is proposed for the detection and assessment of human physical activity. In addition, a theoretical model is proposed for the electrostatic induction current generated owing to variation in the electric potential of the human body. The proposed electrostatic induction current model is compared with the theoretical model, and the proposed model is shown to effectively explain the behavior of the electrostatic induction current waveform. The normal walking motions of daily living are recorded with a portable sensor located in a regular house. The obtained results show that detailed information of physical activity such as a gait cycle can be estimated using our proposed technique. Additionally, the walking signal was measured when the subject walked with the ankle and knee fastened to a splint with bandages to simulate a limp. Therefore, the proposed technique, which is based on the detection of signal generated during walking, can be successfully employed to assess human physical activity.
文摘The synthesis of human walking is of great interest in biomechanics and biomimetic engineering due to its predictive capabilities and potential applications in clinical biomechanics, rehabilitation engineering and biomimetic robotics. In this paper, the various methods that have been used to synthesize humanwalking are reviewed from an engineering viewpoint. This involves a wide spectrum of approaches, from simple passive walking theories to large-scale computational models integrating the nervous, muscular and skeletal systems. These methods are roughly categorized under four headings： models inspired by the concept of a CPG （Central Pattern Generator）, methods based on the principles of control engineering, predictive gait simulation using optimisation, and models inspired by passive walking theory. The shortcomings and advantages of these methods are examined, and future directions are discussed in the context of providing insights into the neural control objectives driving gait and improving the stability of the predicted gaits. Future advancements are likely to be motivated by improved understanding of neural control strategies and the subtle complexities of the musculoskeletal system during human locomotion. It is only a matter of time before predictive gait models become a practical and valuable tool in clinical diagnosis, rehabilitation engineering and robotics.
文摘Walking is a complex task which includes hundreds of muscles, bones and joints working together to deliver smooth movements. With the complexity, walking has been widely investigated in order to identify the pattern of multi-segment movement and reveal the control mechanism. The degree of freedom and dimensional properties provide a view of the coordinative structure during walking, which has been extensively studied by using dimension reduction technique. In this paper, the studies related to the coordinative structure, dimensions detection and pattern reorganization during walking have been reviewed. Principal component analysis, as a popular technique, is widely used in the processing of human movement data. Both the principle and the outcomes of principal component analysis were introduced in this paper. This technique has been reported to successfully reduce the redundancy within the original data, identify the physical meaning represented by the extracted principal components and discriminate the different patterns. The coordinative structure during walking assessed by this technique could provide further information of the body control mechanism and correlate walking pattern with injury.
文摘室内人员活动是室内颗粒物再悬浮的重要因素,研究室内颗粒物再悬浮对评估室内空气质量有重要意义.以南开大学津南校区某公共教室为研究区域,通过现场试验研究了室内地面不同积尘负荷下,人员行走引起的PM 2.5再悬浮浓度及其扩散速率.结果表明:①不同时间间隔内的室内地面总颗粒积尘负荷不同,时间间隔为3、7、15 d时,室内PM 2.5积尘负荷分别为0.11、0.18、0.30 g m 2.②室内地面不同总颗粒积尘负荷下,在室内中心过道处行走时引起的PM 2.5再悬浮浓度约1 min后达到最高值,PM 2.5再悬浮浓度达到最高值的时间与地面不同总颗粒积尘负荷的关系不明显.③随着室内地面总颗粒积尘负荷的增加,人体行走引起的PM 2.5再悬浮浓度也会增加.当室内PM 2.5积尘负荷为0.30 g m 2时,行走路径中游坐姿1.1 m处与站姿呼吸平面1.5 m处的PM 2.5再悬浮浓度平均值分别为3.03、2.68μg m 3,约是室内PM 2.5积尘负荷为0.18与0.11 g m 2时引起的PM 2.5再悬浮浓度平均值的2~3倍.④利用颗粒物传输模型对PM 2.5再悬浮进行量化分析发现,室内地面不同总颗粒积尘负荷的大小与PM 2.5再悬浮分数无关,PM 2.5再悬浮分数为2.2×10-8;室内PM 2.5积尘负荷为0.11、0.18、0.30 g m 2时,行走引起的再悬浮PM 2.5扩散速率分别为7.62×10-11、1.25×10-10、2.08×10-10 kg s.研究显示,地面积尘负荷的大小会影响人体行走时颗粒物的扩散速率与室内PM 2.5浓度.
文摘行走追踪在真实场景中具有广泛应用,可以用于安防监控、老人看护、室内导航等场景.近年来,基于无线射频信号的非接触式行走追踪受到了研究人员的广泛关注,包括基于Wi-Fi信号、RFID信号等的行走追踪系统.然而,现有的行走追踪系统依然面临感知范围小、感知设备部署稠密等问题.在本文中,我们首次将用于物联网低功耗、远距离通信的LoRa技术应用到非接触式的大范围行走追踪中,显著地增加了行走追踪系统的感知距离.特别地,通过利用LoRa网关上配置的多天线,利用两根天线上接收信号的比,可以有效地消除噪声干扰以及收发不同步带来的误差,从而进一步提升了感知范围,然后利用计算切线向量相位变化的方法准确计算原始信号中动态分量的相位变化来在估计行走距离和方向.基于此,本文提出基于LoRa的非接触感知系统,可以在一段自然连续的行走活动中准确地判断人的动静状态并切割出行走片段,进而计算出行走的距离和方向,实现人的行走追踪.实验验证了系统计算行走距离和方向的准确性和实时性,其中距离计算的平均误差为3.8%,准确判断行走方向所需时间为0.7 s.
