Computational Models to Synthesize Human Walking

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.     

行走模式对人体下肢三维步态、肌电信号的影响

目的:揭示人体在主动和被动两种行走模式下的步态特征与下肢主要肌群的肌电信号变化规律.方法:选取12名在校男大学生,通过Greenjog履带式自发力跑台和h/p/cosmos电动跑台建立主动式和被动式行走模型,先后在两种模式下以3种递增速度即慢速(2 km/h)、常速(4 km/h)、和快速(6 km/h)进行一次性步行...     

Reflections on clinical gait analysis

Clinical gait analysis allows the measurement and assessment of walking biomechanics, which facilitates the identification of abnormal characteristics and the recommendation of treatment alternatives. The predominant methods for this analysis currently include the tracking of external markers placed on the patient, the monitoring of patient/ground interaction (e.g. ground reaction forces), and the recording of muscle electromyographic (EMG) activity, all during gait. These data allow the computation of stride and temporal parameters, joint/segment kinematics, joint kinetics, and EMG plots that are used to gain a better understanding of a patient's walking difficulties. Gait interpretation involves a systemic evaluation of each of these types of data, noting both corroborating and conflicting information while identifying functionally significant deviations from the normal. Understanding the etiology of these abnormalities allows the formulation of a treatment plan that may involve physical therapy, bracing, and/or surgery. This process is challenging because of the complexity of the motion, neuromuscular involvement of the patient (e.g. dynamic spasticity), variability of treatment outcome, and on occasion, uncertainty about the quality of the gait data. The experience of the interpretation team with respect to gait biomechanics, a particular patient population, and the effectiveness of different treatment modalities is the principal determinant of the success of this approach. The clinical gait analysis process continues to evolve positively. It has become more comprehensive and meaningful because of an improved understanding of normal gait biomechanics and more rigorous data collection/reduction protocols that complement accumulated clinically relevant experience.     

Diurnal variation in gait characteristics and transition speed

The aim of this study was to investigate the effect of time-of-day on Preferred Transition Speed (PTS) and spatiotemporal organization of walking and running movements. Twelve active male subjects participated in the study (age: 27.2?±?4.9 years; height: 177.9?±?5.4?cm; body mass: 75.9?±?5.86?kg). First, PTS was determined at 08:00?h and 18:00?h. The mean of the two PTS recorded at the two times-of-day tested was used as a reference (PTS_m). Then, subjects were asked to walk and run on a treadmill at three imposed speeds (PTS_m, PTS_m?+?0.3?m.s^?1, and PTS_m???0.3?m.s^?1) at 08:00?h and 18:00?h. Mean stride length, temporal stride, spatial stride variability, and temporal stride variability were used for gait analysis. The PTS observed at 08:00?h (2.10?±?0.17?m.s^?1) tends to be lower (p?=?0.077) than that recorded at 18:00?h (2.14?±?0.19?m.s^?1). Stride lengths recorded while walking (p?=?0.038) and running (p?=?0.041) were shorter at 08:00?h than 18:00?h. No time-of-day effect was observed for stride frequency during walking and running trials. When walking, spatial stride variability (p?=?0.020) and temporal stride variability (p?=?0.028) were lower at 08:00?h than at 18:00?h. When running, no diurnal variation of spatial stride variability or temporal stride variability was detected.     

Verification of accuracy and validity of gait phase detection system using motion sensors for applying walking assistive FES

In this study, we have analysed heel strike (HS) and toe off (TO) of normal individuals and hemiplegic patients, taking advantage of output curves acquired from various sensors, and verified the validity of sensor detection methods and their effectiveness when they were used for hemiplegic gaits. Gait phase detections using three different motion sensors were valid, since they all had reliabilities more than 95%, when compared with foot velocity algorithm. Results showed that the tilt sensor and the gyrosensor could detect gait phase more accurately in normal individuals. Vertical acceleration could detect HS most accurately in hemiplegic patient group A. The gyrosensor could detect HS and TO most accurately in hemiplegic patient groups A and B. The detection of TO from all sensor signals was valid in both the patient groups A and B. However, the vertical acceleration detected HS validly in patient group A and the gyrosensor detected HS validly in patient group B.     

Stabilisation of walking by intrinsic muscle properties revealed in a three-dimensional muscle-driven simulation

A fundamental question in movement science is how humans perform stable movements in the presence of disturbances such as contact with objects. It remains unclear how the nervous system, with delayed responses to disturbances, maintains the stability of complex movements. We hypothesised that intrinsic muscle properties (i.e. the force–length–velocity properties of muscle fibres and tendon elasticity) may help stabilise human walking by responding instantaneously to a disturbance and providing forces that help maintain the movement trajectory. To investigate this issue, we generated a 3D muscle-driven simulation of walking and analysed the changes in the simulation's motion when a disturbance was applied to models with and without intrinsic muscle properties. Removing the intrinsic properties reduced the stability; this was true when the disturbing force was applied at a variety of times and in different directions. Thus, intrinsic muscle properties play a unique role in stabilising walking, complementing the delayed response of the central nervous system.     

Computational evaluation of load carriage effects on gait balance stability

Evaluating the effects of load carriage on gait balance stability is important in various applications. However, their quantification has not been rigorously addressed in the current literature, partially due to the lack of relevant computational indices. The novel Dynamic Gait Measure (DGM) characterizes gait balance stability by quantifying the relative effects of inertia in terms of zero-moment point, ground projection of center of mass, and time-varying foot support region. In this study, the DGM is formulated in terms of the gait parameters that explicitly reflect the gait strategy of a given walking pattern and is used for computational evaluation of the distinct balance stability of loaded walking. The observed gait adaptations caused by load carriage (decreased single support duration, inertia effects, and step length) result in decreased DGM values (p < 0.0001), which indicate that loaded walking motions are more statically stable compared with the unloaded normal walking. Comparison of the DGM with other common gait stability indices (the maximum Floquet multiplier and the margin of stability) validates the unique characterization capability of the DGM, which is consistently informative of the presence of the added load.