Walking Terrain Research Articles

Foot orientation and trajectory variability in locomotion: Effects of real-world terrain.

Capturing human locomotion in nearly any environment or context is becoming increasingly feasible with wearable sensors, giving access to commonly encountered walking conditions. While important in expanding our understanding of locomotor biomechanics, these more variable environments present challenges to identify changes in data due to person-level factors among the varying environment-level factors. Our study examined foot-specific biomechanics while walking on terrain commonly encountered with the goal of understanding the extent to which these variables change due to terrain. We recruited healthy adults to walk at self-selected speeds on stairs, flat ground, and both shallow and steep sloped terrain. A pair of inertial measurement units were embedded in both shoes to capture foot biomechanics while walking. Foot orientation was calculated using a strapdown procedure and foot trajectory was determined by double integrating the linear acceleration. Stance time, swing time, cadence, sagittal and frontal orientations, stride length and width were extracted as discrete variables. These data were compared within-participant and across terrain conditions. The physical constraints of the stairs resulted in shorter stride lengths, less time spent in swing, toe-first foot contact, and higher variability during stair ascent specifically (p<0.05). Stride lengths increased when ascending compared to descending slopes, and the sagittal foot angle at initial contact was greatest in the steep slope descent condition (p<0.05). No differences were found between conditions for horizontal foot angle in midstance (p≥0.067). Our results show that walking on slopes creates differential changes in foot biomechanics depending on whether one is descending or ascending, and stairs require different biomechanics and gait timing than slopes or flat ground. This may be an important factor to consider when making comparisons of real-world walking bouts, as greater proportions of one terrain feature in a data set could create bias in the outcomes. Classifying terrain in unsupervised walking datasets would be helpful to avoid comparing metrics from different walking terrain scenarios.

Active Balance Control of Humanoid Locomotion Based on Foot Position Compensation

A foot positioning compensator is developed in this paper for a full-body humanoid to retrieve its balance during continuous walking. An online Foot Position Compensator (FPC) is designed to improve the robustness of biped walking, which can modify predefined step position and step duration online with sensory feedback. Foot placement parameters are learned by the FPC based on the Policy Gradient Reinforcement Learning (PGRL) method. Moreover, the FPC assists the humanoid robot in rejecting external disturbances and recovering the walking position by re-planning the trajectories of walking pattern and the Center of Mass (CoM). An upper body pose control strategy is also presented to further enhance the performance of humanoid robots to overcome strong external disturbances. The advantages of this proposed method are that it neither requires prior information about the walking terrain conditions, nor relies on range sensor information for surface topology measurement. The effectiveness of the proposed method is verified via Webots simulation and real experiments on a full-body humanoid NAO robot.

Robust Control of Semi-passive Biped Dynamic Locomotion based on a Discrete Control Lyapunov Function

SUMMARYThis paper focuses on robust control of a simplest passive model, which is established on a DCLF (discrete control Lyapunov function) -based control system, and presents gait transition method based on the study of purely passive walker. Firstly, the DCLF is introduced to stabilize walking process between steps exponentially by modulating the length of next step. Next, the swing leg trajectory from mid-stance position to foot-strike can be planned. Then the control law is calculated to resist external disturbance. Besides, an impulse is added just before foot-strike to realize a periodic walking pattern on flat or uphill ground. With walking terrain varying, the robot can transit to an adaptive walking gait in a few steps. With different push or pull disturbances acting on hip joint and the robot gait transiting on a continuously slope-changing downhill, the effectiveness of the presented DCLF-based method is verified using simulation experiments. The ability to walk on a changing environment is also presented by simulation results. The insights of this paper can help to develop a robust control method and adaptive walking of dynamic passive locomotion robots.

Terrain Feature Estimation Method for a Lower Limb Exoskeleton Using Kinematic Analysis and Center of Pressure.

While controlling a lower limb exoskeleton providing walking assistance to wearers, the walking terrain is an important factor that should be considered for meeting performance and safety requirements. Therefore, we developed a method to estimate the slope and elevation using the contact points between the limb exoskeleton and ground. We used the center of pressure as a contact point on the ground and calculated the location of the contact points on the walking terrain based on kinematic analysis of the exoskeleton. Then, a set of contact points collected from each step during walking was modeled as the plane that represents the surface of the walking terrain through the least-square method. Finally, by comparing the normal vectors of the modeled planes for each step, features of the walking terrain were estimated. We analyzed the estimation accuracy of the proposed method through experiments on level ground, stairs, and a ramp. Classification using the estimated features showed recognition accuracy higher than 95% for all experimental motions. The proposed method approximately analyzed the movement of the exoskeleton on various terrains even though no prior information on the walking terrain was provided. The method can enable exoskeleton systems to actively assist walking in various environments.

Treading on the unknown increases prefrontal activity: A pilot fNIRS study

BackgroundComplex walking conditions (e.g. dual tasking) have been associated with increased prefrontal (PFC) activity. However, most paradigms include a predictable environment, specifically, a predictable walking terrain. In the present study we investigate PFC activity under an unusual walking condition where each foot placement was on unexpected terrain, thus causing a mismatch between visuospatial perception and lower-extremity proprioception. Research objectiveTo assess whether PFC activity increases under unstable unpredictable conditions compared to unstable but predictable conditions. MethodsThis was a prospective study involving twenty healthy adults. Participants walked in two conditions: unstable but predictable, and unstable and unpredictable. To assess walking stability, both stride-time (ST) and stride-time variability (CV) were measured. To assess PFC activity, two wireless near-infrared spectroscopy devices were used. The group hemodynamic response (GHR) was calculated for each condition. For statistical analysis, a linear-mixed-effects model was used. ResultsWalking with unpredictable perturbations did not change the ST (t = 0.51, p = 0.61) but significantly increased the parameter CV (t = 11.74, p < 0.001). The GHR of both conditions indicated brief per-initiation PFC activity that was similar across conditions. However, when GHRs were calculated relative to normal walking (i.e., the participants’ own shoes), continuous activity was evident. Compared to the predictable condition, the unpredictable condition significantly increased this activity during steady-state walking (t = 2.13, p = 0.033). SignificanceObservations from the present study suggest that at least two neural components are present in the measured signal—a brief one, occurring per-initiation, and a continuous one, sensitive to the predictability of the terrain. The second component was accompanied by a decrease in walking stability. These results may contribute to our understanding of the control mechanism underlying gait and future planning of rehabilitation protocols.

Tripping risk evaluation system based on human behavior simulation in laser-scanned 3D as-is environments

Abstract Tripping-related falls are the leading cause of unintentional deaths in the elderly. Tripping risk must be evaluated by considering intrinsic and extrinsic factors affecting the interactions between humans and the environment. The goal of this study is to develop a new tripping risk evaluation system based on human behavior simulation in an “as-is” environment model, i.e., environment model reflecting detailed environmental geometry. First, a three-dimensional (3D) as-is environment model is automatically constructed from laser-scanned point clouds in the environment. From this model, potential tripping-hazard regions are automatically detected. Finally, the tripping risk of detected hazard region is quantitatively evaluated based on a combined human behavior and Monte Carlo simulation. The simulated tripping risk is compared with that measured using real humans, and it is revealed that the dimensional limit of risk evaluation accuracy in terms of barrier height on a walking terrain is very close to 10 mm.

Analysis of Lower-Limb Motion during Walking on Various Types of Terrain in Daily Life

Objective:This research analyzed the lower-limb motion in kinetic and kinematic way while walking on various terrains to develop Foot-Ground Contact Detection (FGCD) algorithm using the Inertial Measurement Unit (IMU). Background: To estimate the location of human in GPS-denied environments, it is well known that the lower-limb kinematics based on IMU sensors, and pressure insoles are very useful. IMU is mainly used to solve the lower-limb kinematics, and pressure insole are mainly used to detect the foot-ground contacts in stance phase. However, the use of multiple sensors are not desirable in most cases. Therefore, only IMU based FGCD can be an efficient method. Method: Orientation and acceleration of lower-limb of 10 participants were measured using IMU while walking on flat ground, ascending and descending slope and stairs. And the inertial information showing significant changes at the Heel strike (HS), Full contact (FC), Heel off (HO) and Toe off (TO) was analyzed. Results: The results confirm that pitch angle, rate of pitch angle of foot and shank, and acceleration in x, z directions of the foot are useful in detecting the four different contacts in five different walking terrain. Conclusion: IMU based FGCD Algorithm considering all walking terrain possible in daily life was successfully developed based on all IMU output signals showing significant changes at the four steps of stance phase. Application: The information of the contact between foot and ground can be used for solving lower-limb kinematics to estimating an individuals location and walking speed.

Development of an IMU-based foot-ground contact detection (FGCD) algorithm

It is well known that, to locate humans in GPS-denied environments, a lower limb kinematic solution based on Inertial Measurement Unit (IMU), force plate, and pressure insoles is essential. The force plate and pressure insole are used to detect foot-ground contacts. However, the use of multiple sensors is not desirable in most cases. This paper documents the development of an IMU-based FGCD (foot-ground contact detection) algorithm considering the variations of both walking terrain and speed. All IMU outputs showing significant changes on the moments of foot-ground contact phases are fully identified through experiments in five walking terrains. For the experiment on each walking terrain, variations of walking speeds are also examined to confirm the correlations between walking speed and the main parameters in the FGCD algorithm. As experimental results, FGCD algorithm successfully detecting four contact phases is developed, and validation of performance of the FGCD algorithm is also implemented.Practitioner Summary: In this research, it was demonstrated that the four contact phases of Heel strike (or Toe strike), Full contact, Heel off and Toe off can be independently detected regardless of the walking speed and walking terrain based on the detection criteria composed of the ranges and the rates of change of the main parameters measured from the Inertial Measurement Unit sensors.

Toward Design of an Environment-Aware Adaptive Locomotion-Mode-Recognition System

In this study, we aimed to improve the performance of a locomotion-mode-recognition system based on neuromuscular-mechanical fusion by introducing additional information about the walking environment. Linear-discriminant-analysis-based classifiers were first designed to identify a lower limb prosthesis user's locomotion mode based on electromyographic signals recorded from residual leg muscles and ground reaction forces measured from the prosthetic pylon. Nine transfemoral amputees who wore a passive hydraulic knee or powered prosthetic knee participated in this study. Information about the walking terrain was simulated and modeled as prior probability based on the principle of maximum entropy and integrated into the discriminant functions of the classifier. When the correct prior knowledge of walking terrain was simulated, the classification accuracy for each locomotion mode significantly increased and no task transitions were missed. In addition, simulated incorrect prior knowledge did not significantly reduce system performance, indicating that our design is robust against noisy and imperfect prior information. Furthermore, these observations were independent of the type of prosthesis applied. The promising results in this study may assist the further development of an environment-aware adaptive system for locomotion-mode recognition for powered lower limb prostheses or orthoses.

Application of Model Predictive Impedance Control (MPIC) in analysis of human walking on rough terrains

By using Model Predictive Impedance Control (MPIC), two essential questions about human walking control have been considered in this paper: Which parameters are controlled by the control system and what strategies are applied? As a general method, MPIC was proposed as a possible model for controlling human movements in the previous paper of one of the authors (1) and it was also applied to periodic movements such as walking (2). According to the acceptable results of primary experiences, a more realistic walking model is presented in this paper to be controlled by MPIC strategy. A 3-dimensional 5-link simulated walking robot with 8 degrees of freedom in sagittal and lateral planes is employed to be analyzed while being controlled on an irregular condition, like ascending and descending stairs. The objective of the controller is to optimize the energy consumption, vertical orientation of the body, and forward speed of its center of mass. Visual feedback effect is applied when the robot targets specific footprints over the walking terrain. Each optimization sequence has been applied during the prediction horizon with one human step duration. The results have been animated for comparison with natural walking of a human in similar conditions.