Periodic Gait Research Articles

Stable Walking of a Biped Robot Controlled by Central Pattern Generator Using Multivariate Linear Mapping.

In order to improve the walking stability of a biped robot in multiple scenarios and reduce the complexity of the Central Pattern Generator (CPG) model, a new CPG walking controller based on multivariate linear mapping was proposed. At first, in order to establish a dynamics model, the lower limb mechanical structure of the biped robot was designed. According to the Lagrange and angular momentum conservation method, the hybrid dynamic model of the biped robot was established. The initial value of the robot's passive walking was found by means of Poincaré mapping and cell mapping methods. Then, a multivariate linear mapping model was established to form a new lightweight CPG model based on a Hopf oscillator. According to the parameter distribution of the new CPG model, a preliminary parameter-tuning idea was proposed. At last, the joint simulation of MATLAB and V-REP shows that the biped robot based on the new CPG control has a stable periodic gait in flat and uphill scenes. The proposed method could improve the stability and versatility of bipedal walking in various environments and can provide general CPG generation and a tuning method reference for robotics scholars.

DE-AFO: A Robotic Ankle Foot Orthosis for Children with Cerebral Palsy Powered by Dielectric Elastomer Artificial Muscle.

Conventional passive ankle foot orthoses (AFOs) have not seen substantial advances or functional improvements for decades, failing to meet the demands of many stakeholders, especially the pediatric population with neurological disorders. Our objective is to develop the first comfortable and unobtrusive powered AFO for children with cerebral palsy (CP), the DE-AFO. CP is the most diagnosed neuromotor disorder in the pediatric population. The standard of care for ankle control dysfunction associated with CP, however, is an unmechanized, bulky, and uncomfortable L-shaped conventional AFO. These passive orthoses constrain the ankle's motion and often cause muscle disuse atrophy, skin damage, and adverse neural adaptations. While powered orthoses could enhance natural ankle motion, their reliance on bulky, noisy, and rigid actuators like DC motors limits their acceptability. Our innovation, the DE-AFO, emerged from insights gathered during customer discovery interviews with 185 stakeholders within the AFO ecosystem as part of the NSF I-Corps program. The DE-AFO is a biomimetic robot that employs artificial muscles made from an electro-active polymer called dielectric elastomers (DEs) to assist ankle movements in the sagittal planes. It incorporates a gait phase detection controller to synchronize the artificial muscles with natural gait cycles, mimicking the function of natural ankle muscles. This device is the first of its kind to utilize lightweight, compact, soft, and silent artificial muscles that contract longitudinally, addressing traditional actuated AFOs' limitations by enhancing the orthosis's natural feel, comfort, and acceptability. In this paper, we outline our design approach and describe the three main components of the DE-AFO: the artificial muscle technology, the finite state machine (the gait phase detection system), and its mechanical structure. To verify the feasibility of our design, we theoretically calculated if DE-AFO can provide the necessary ankle moment assistance for children with CP-aligning with moments observed in typically developing children. To this end, we calculated the ankle moment deficit in a child with CP when compared with the normative moment of seven typically developing children. Our results demonstrated that the DE-AFO can provide meaningful ankle moment assistance, providing up to 69% and 100% of the required assistive force during the pre-swing phase and swing period of gait, respectively.

A three-dimensional spring-loaded inverted pendulum walking model considering human movement speed and frequency

The spring-loaded inverted pendulum (SLIP) model is an effective model to capture the essential dynamics during human walking and/or running. However, most of the existing three-dimensional (3D) SLIP model does not explicitly account for human movement speed and frequency. To address this knowledge gap, this paper develops a new SLIP model, which includes a roller foot, massless spring, and concentrated mass. The governing equations-of-motion for the SLIP model during its double support phase are derived. It is noted that in the current formulation, the motion of the roller foot is prescribed; therefore, only the equations for the concentrated mass need to be solved. To yield model parameters leading to a periodic walking gait, a constrained optimization problem is formulated and solved using a gradient-based approach with a global search strategy. The optimization results show that when the attack angle ranges from 68° to 74°, the 3D SLIP model can yield a periodic walking gait with walking speeds varying from 0.5 to 2.0 m s−1. The predicted human walking data are also compared with published experimental data, showing reasonable accuracy.

Experimental Evaluation of Pedestrian-Induced Multiaxial Gait Loads on Footbridges: Effects of the Structure-to-Human Interaction by Lateral Vibrating Platforms.

The introduction of resistant and lightweight materials in the construction industry has led to civil structures being vulnerable to excessive vibrations, particularly in footbridges exposed to human-induced gait loads. This interaction, known as Human-Structure Interaction (HSI), involves a complex interplay between structural vibrations and gait loads. Despite extensive research on HSI, the simultaneous effects of lateral structural vibrations with fundamental frequencies close to human gait frequency (around 1.0 Hz) and wide amplitudes (over 30.0 mm) remain inadequately understood, posing a contemporary structural challenge highlighted by incidents in iconic bridges like the Millennium Bridge in London, Solferino Bridge in Paris, and Premier Bridge in Cali, Colombia. This paper focuses on the experimental exploration of Structure-to-Human Interaction (S2HI) effects using the Human-Structure Interaction Multi-Axial Test Framework (HSI-MTF). The framework enables the simultaneous measurement of vertical and lateral loads induced by human gait on surfaces with diverse frequency ranges and wide-amplitude lateral harmonic motions. The study involved seven test subjects, evaluating gait loads on rigid and harmonic lateral surfaces with displacements ranging from 5.0 to 50.0 mm and frequency content from 0.70 to 1.30 Hz. A low-cost vision-based motion capture system with smartphones analyzed the support (Tsu) and swing (Tsw) periods of human gait. Results indicated substantial differences in Tsu and Tsw on lateral harmonic protocols, reaching up to 96.53% and 58.15%, respectively, compared to rigid surfaces. Normalized lateral loads (LL) relative to the subject's weight (W0) exhibited a linear growth proportional to lateral excitation frequency, with increased proportionality constants linked to higher vibration amplitudes. Linear regressions yielded an average R2 of 0.815. Regarding normalized vertical load (LV) with respect to W0, a consistent behavior was observed for amplitudes up to 30.0 mm, beyond which a linear increase, directly proportional to frequency, resulted in a 28.3% increment compared to rigid surfaces. Correlation analyses using Pearson linear coefficients determined relationships between structural surface vibration and pedestrian lateral motion, providing valuable insights into Structure-to-Human Interaction dynamics.

Human-in-the-Loop Cooperative Control of a Walking Exoskeleton for Following Time-Variable Human Intention.

This article presents a human-in-the-loop cooperative control of a walking exoskeleton to provide assistance to the user and enhance human mobility. First, a dynamic mathematical model of the human-exoskeleton system is derived, and then, a human-in-the-loop cooperative control framework is proposed in two ways: separable cooperative control (SCC) and interactive cooperative control (ICC), respectively. The SCC introduces a space division of the human and the exoskeleton, while the ICC allows the robot to perceive the human intention and follows human motor, thereby improving the collaborative performance. The ICC formulates the impedance connection between the human and the exoskeleton in the divided orthogonal subspaces of walking, such that the robot is able to modify its position of center of mass (COM) when its motor trajectory deviates the one of the human. In addition, a novel adaptation control is proposed to deal with the unmodeled dynamics and trajectory tracking. Finally, to validate the effectiveness of our proposed controller, a series of experiments are conducted in three adults in gait at different speeds. It shows that the proposed controller can preserve the periodic walking gait and inherit the robustness of dealing with perturbations during walking.

Smart Gait: A Gait Optimization Framework for Hexapod Robots

The current gait planning for legged robots is mostly based on human presets, which cannot match the flexible characteristics of natural mammals. This paper proposes a gait optimization framework for hexapod robots called Smart Gait. Smart Gait contains three modules: swing leg trajectory optimization, gait period & duty optimization, and gait sequence optimization. The full dynamics of a single leg, and the centroid dynamics of the overall robot are considered in the respective modules. The Smart Gait not only helps the robot to decrease the energy consumption when in locomotion, mostly, it enables the hexapod robot to determine its gait pattern transitions based on its current state, instead of repeating the formalistic clock-set step cycles. Our Smart Gait framework allows the hexapod robot to behave nimbly as a living animal when in 3D movements for the first time. The Smart Gait framework combines offline and online optimizations without any fussy data-driven training procedures, and it can run efficiently on board in real-time after deployment. Various experiments are carried out on the hexapod robot LittleStrong. The results show that the energy consumption is reduced by 15.9% when in locomotion. Adaptive gait patterns can be generated spontaneously both in regular and challenge environments, and when facing external interferences.

Free Gait Generation of Quadruped Robots via Impulse-Based Feasibility Analysis

Online gait adaptation is crucial for improving the dynamic performance of quadruped locomotion systems, while current approaches often adopt the predefined periodic gait pattern. This article proposes a free gait generation algorithm that only takes the robot state as input. Specifically, we introduce the feasible impulse polytope, which takes into account both linear and angular momentum impulses acting on the body in a prediction horizon. This naturally leads us to formulate a leg capability metric related to the effect of take-off and touch-down on the body motion. Then, gait sequence, take-off timing, and touch-down location can be automatically adjusted online as long as a metric threshold is given. To cope with rough terrain, a series of quadratic programming problems are established for online pose optimization under mild assumptions. Furthermore, we fully integrate the proposed algorithms with the robot control and estimation framework for real-time implementation. We first validate our approach with the quadruped robot SCIT-Dog through locomotion with speed changes, on stairs and slopes, and under lateral disturbances. Besides, a push-recovery experiment is established to show the superiority of the proposed approach over the baseline method with prespecified gait patterns. Experiment results verify the ability of the proposed algorithms to achieve autonomous gait transitions in response to various emergencies.

Hip-Knee Motion-Lagged Coordination Mapping Enables Speed Adaptive Walking for Powered Knee Prosthesis.

The commonly used finite-state-machine (FSM) impedance control for powered prostheses deploys diverse control parameters according to different gait phases, resulting in dozens of parameter adjustments and possible gait phase misrecognition. In contrast, this study presents a straightforward, continuous, and speed-adaptive control approach based on hip-knee motion-lagged coordination mapping (MLCM). The mapping, featured by the motion lag, can effectively generate the prosthetic knee's goal gait within a second-order polynomial. It is also verified from extensive gait analysis that the motion lag and polynomial coefficients evolve linearly with respect to walking speed and gait period, promising a simple real-time deployment for prosthesis control. Experimental validation with two non-disabled subjects and two transfemoral amputees wearing a prosthesis demonstrates the MLCM controller's ability to reduce the hip compensatory behavior, generate biomimetic knee kinematics, stance phase time, stride length, and hip-knee motion coordination across various speeds. Furthermore, compared to the benchmark FSM impedance controller, the MLCM controller reduces the number of control parameters from 17 to 7 and avoids misrecognition during gait phase transitions.

A Multi-Agent Reinforcement Learning Method for Omnidirectional Walking of Bipedal Robots.

Achieving omnidirectional walking for bipedal robots is considered one of the most challenging tasks in robotics technology. Reinforcement learning (RL) methods have proved effective in bipedal walking tasks. However, most existing methods use state machines to switch between multiple policies and achieve omnidirectional gait, which results in shaking during the policy switching process for bipedal robots. To achieve a seamless transition between omnidirectional gait and transient motion for full-size bipedal robots, we propose a novel multi-agent RL method. Firstly, a multi-agent RL algorithm based on the actor-critic framework is designed, and policy entropy is introduced to improve exploration efficiency. By learning agents with parallel initial state distributions, we minimize reliance on gait planner effectiveness in the Robot Operating System (ROS). Additionally, we design a novel heterogeneous policy experience replay mechanism based on Euclidean distance. Secondly, considering the periodicity of bipedal robot walking, we develop a new periodic gait function. Including periodic objectives in the policy can accelerate the convergence speed of training periodic gait functions. Finally, to enhance the robustness of the policy, we construct a novel curriculum learning method by discretizing Gaussian distribution and incorporate it into the robot's training task. Our method is validated in a simulation environment, and the results show that our method can achieve multiple gaits through a policy network and achieve smooth transitions between different gaits.

A Deep Learning Approach for Biped Robot Locomotion Interface Using a Single Inertial Sensor.

In this study, we introduce a novel framework that combines human motion parameterization from a single inertial sensor, motion synthesis from these parameters, and biped robot motion control using the synthesized motion. This framework applies advanced deep learning methods to data obtained from an IMU attached to a human subject's pelvis. This minimalistic sensor setup simplifies the data collection process, overcoming price and complexity challenges related to multi-sensor systems. We employed a Bi-LSTM encoder to estimate key human motion parameters: walking velocity and gait phase from the IMU sensor. This step is followed by a feedforward motion generator-decoder network that accurately produces lower limb joint angles and displacement corresponding to these parameters. Additionally, our method also introduces a Fourier series-based approach to generate these key motion parameters solely from user commands, specifically walking speed and gait period. Hence, the decoder can receive inputs either from the encoder or directly from the Fourier series parameter generator. The output of the decoder network is then utilized as a reference motion for the walking control of a biped robot, employing a constraint-consistent inverse dynamics control algorithm. This framework facilitates biped robot motion planning based on data from either a single inertial sensor or two user commands. The proposed method was validated through robot simulations in the MuJoco physics engine environment. The motion controller achieved an error of ≤5° in tracking the joint angles demonstrating the effectiveness of the proposed framework. This was accomplished using minimal sensor data or few user commands, marking a promising foundation for robotic control and human-robot interaction.

Intermittent theta burst stimulation combined with cognitive training improves cognitive dysfunction and physical dysfunction in patients with post-stroke cognitive impairment

ObjectivePost-stroke cognitive impairment (PSCI) is a common complication of stroke. Intermittent theta burst stimulation (iTBS) can inducing motor learning. We observed the effects of combination of iTBS with cognitive training on physical/cognitive dysfunctions in PSCI patients. MethodsPSCI patients treated with basic treatment & cognitive training (Control group)/iTBS & cognitive training (iTBS group) were enrolled, with Mini-mental State Examination (MMSE)/Montreal Cognitive Assessment (MoCA)/Frontal Assessment Battery (FAB)/barthel index (BI)/Upper Limb Fugl-Meyer Assessment (U-FMA)/Action Research Arm Test (ARAT) scores compared. Gait spatiotemporal parameters/dynamic parameters were analyzed by 3D gait analysis. Correlations between MMSE/MoCA scores and gait parameters in PSCI patients after iTBS & cognitive training were analyzed by Spearman analysis. ResultsIncreased MMSE/MoCA/FAB/BI/U-FMA/ARAT scores, step speed, step frequency, stride length, step width, step length on the affected side, percentage of swing phase on the affected side, hip joint flexion angle on the affected side, knee joint flexion angle on the affected side, and ankle plantar flexion angle on the affected side and reduced gait period on the affected side and percentage of stance phase on the affected side were found in patients of both groups after treatment, with the effects in the iTBS group more profound. ConclusioniTBS & cognitive training obviously improved the cognitive function scores/upper limb function scores/gait parameters in PSCI patients versus cognitive training treatment. After combination therapy, the MMSE/MoCA scores of PSCI patients were significantly correlated with gait parameters. This provided more data support for iTBS & cognitive training application in the rehabilitation treatment of PSCI patients.

A new method for finding the proper initial conditions in passive locomotion of bipedal robotic systems

In this paper, the walking motion of a biped robot consisting of upper and lower body members (arms, legs, upper trunk and neck) on an inclined surface is analyzed kinematically and dynamically. Some of the challenges faced by this research include the dual-phase dynamics that govern the periodic gait of this robotic system and the consideration of all the body parts that are involved in the walking motion; which make it extremely difficult to derive the kinetic equations of such robotic systems. To deal with these challenges, the Gibbs-Appell formulation and the Newton's impact laws are employed to derive the most general form of the system's dynamic equations in the swing and transient phases of motion. Subsequently, by using the obtained motion equations and implementing a systematic procedure, we achieve an eigenvalue problem. By solving this problem, the suitable initial conditions that are necessary for the passive gait of this biped robot on a sloping surface are determined. By considering the obtained initial conditions as well as the dynamic response of the system to the Earth's gravity, we describe a general method for designing a biped robot's walking trajectory on an inclined surface. The most important feature of the designed trajectory is its close agreement with the trajectory followed by a biped robot walking passively down a sloping ramp. This close match between the said trajectories enables us to guide the robot in tracking a desired path by applying very small control torques to the robot joints at the start of each step. This claim is corroborated by the simulations carried out for a biped walking robot composed of 6 rigid links, in which the control torques that are applied to the actuated robot joints are obtained by employing a feedback linearization method and via a designed LQR controller.

Multifractality in stride-to-stride variations reveals that walking involves more movement tuning and adjusting than running

Introduction: The seemingly periodic human gait exhibits stride-to-stride variations as it adapts to the changing task constraints. The optimal movement variability hypothesis (OMVH) states that healthy stride-to-stride variations exhibit “fractality”—a specific temporal structure in consecutive strides that are ordered, stable but also variable, and adaptable. Previous research has primarily focused on a single fractality measure, “monofractality.” However, this measure can vary across time; strideto-stride variations can show “multifractality.” Greater multifractality in stride-tostride variations would highlight the ability to tune and adjust movements more.Methods: We investigated monofractality and multifractality in a cohort of eight healthy adults during self-paced walking and running trials, both on a treadmill and overground. Footfall data were collected through force-sensitive sensors positioned on their heels and feet. We examined the effects of self-paced walking vs. running and treadmill vs. overground locomotion on the measure of monofractality, α-DFA, in addition to the multifractal spectrum width, W, and the asymmetry in the multifractal spectrum, WAsym, of stride interval time series.Results: While the α-DFA was larger than 0.50 for almost all conditions, α-DFA was higher in running and locomoting overground than walking and locomoting on a treadmill. Similarly, W was greater while locomoting overground than on a treadmill, but an opposite trend indicated that W was greater in walking than running. Larger WAsym values in the negative direction suggest that walking exhibits more variation in the persistence of shorter stride intervals than running. However, the ability to tune and adjust movements does not differ between treadmill and overground, although both exhibit more variation in the persistence of shorter stride intervals.Discussion: Hence, greater heterogeneity in shorter than longer stride intervals contributed to greater multifractality in walking compared to running, indicated by larger negative WAsym values. Our results highlight the need to incorporate multifractal methods to test the predictions of the OMVH.

A parallel heterogeneous policy deep reinforcement learning algorithm for bipedal walking motion design.

Considering the dynamics and non-linear characteristics of biped robots, gait optimization is an extremely challenging task. To tackle this issue, a parallel heterogeneous policy Deep Reinforcement Learning (DRL) algorithm for gait optimization is proposed. Firstly, the Deep Deterministic Policy Gradient (DDPG) algorithm is used as the main architecture to run multiple biped robots in parallel to interact with the environment. And the network is shared to improve the training efficiency. Furthermore, heterogeneous experience replay is employed instead of the traditional experience replay mechanism to optimize the utilization of experience. Secondly, according to the walking characteristics of biped robots, a biped robot periodic gait is designed with reference to sinusoidal curves. The periodic gait takes into account the effects of foot lift height, walking period, foot lift speed and ground contact force of the biped robot. Finally, different environments and different biped robot models pose challenges for different optimization algorithms. Thus, a unified gait optimization framework for biped robots based on the RoboCup3D platform is established. Comparative experiments were conducted using the unified gait optimization framework, and the experimental results show that the method outlined in this paper can make the biped robot walk faster and more stably.

Suitability of visual cues for freezing of gait in patients with idiopathic Parkinson’s disease: a case–control pilot study

BackgroundFreezing of gait (FOG) is one of the most debilitating symptoms in patients with idiopathic Parkinson’s disease (IPD). Visual cues can relieve FOG symptoms. However, there is no consensus on patient characteristics that can benefit from visual cues. Therefore, we examined the differences in IPD patient characteristics according to the effectiveness of visual cueing.MethodsThrough gait experiments, we investigated the number of FOG occurrences, average FOG period per episode, proportion of FOG duration in the total gait cycles, and FOG-free period gait spatiotemporal parameters in ten participants diagnosed with FOG due to IPD. Subsequently, the differences between their clinical characteristics and striatal dopamine active transporter availability from six subregions of the striatum were compared by dividing them into two groups based on the three reduction rates: occurrence numbers, mean durations per episode, and proportion of FOG duration in the total gait cycles improved by visual cueing using laser shoes. The relationships among these three reduction rates and other FOG-related parameters were also investigated using Spearman correlation analyses.ResultsAccording to the three FOG-related reduction rates, the group assignments were the same, which was also related to the baseline self-reported FOG severity score (New Freezing of Gait Questionnaire): the more severe the FOG, the poorer the response to the visual cueing. By visual cueing, the better response group demonstrated the characteristics of lower new FOG questionnaire total scores, higher dopamine active transporter availability of the anterior and posterior putamen, and shorter mean duration of FOG per episode in the absence of cueing. These results were replicated using Spearman correlation analyses.ConclusionsFor FOG symptoms following IPD, gait assistance by visual cueing may be more effective when the total NFOGQ score is lower and the DAT of putamen is higher. Through this study, we demonstrated clinical and striatal dopaminergic conditions to select patients who may be more likely to benefit from visual cueing with laser shoes, and these findings lead to the need for early diagnosis of FOG in patients with IPD.Trial registrationClinicalTrials.gov identifier: NCT05080413. Registered on September 14, 2021.

Autonomous navigation of underactuated bipedal robots in height-constrained environments

Navigating a large-scaled robot in unknown and cluttered height-constrained environments is challenging. Not only is a fast and reliable planning algorithm required to go around obstacles, the robot should also be able to change its intrinsic dimension by crouching in order to travel underneath height-constrained regions. There are few mobile robots that are capable of handling such a challenge, and bipedal robots provide a solution. However, as bipedal robots have nonlinear and hybrid dynamics, trajectory planning while ensuring dynamic feasibility and safety on these robots is challenging. This paper presents an end-to-end autonomous navigation framework which leverages three layers of planners and a variable walking height controller to enable bipedal robots to safely explore height-constrained environments. A vertically actuated spring-loaded inverted pendulum (vSLIP) model is introduced to capture the robot’s coupled dynamics of planar walking and vertical walking height. This reduced-order model is utilized to optimize for long-term and short-term safe trajectory plans. A variable walking height controller is leveraged to enable the bipedal robot to maintain stable periodic walking gaits while following the planned trajectory. The entire framework is tested and experimentally validated using a bipedal robot Cassie. This demonstrates reliable autonomy to drive the robot to safely avoid obstacles while walking to the goal location in various kinds of height-constrained cluttered environments.

Verification of damped bipedal inverted pendulum model against kinematic and kinetic data of human walking on rigid-level ground

In this study, kinematic and kinetic data for 14 test subjects walking on a treadmill were recorded to verify a damped bipedal inverted pendulum (DBIP) model. The verification aim was for the DBIP model to match the step frequency and first harmonic dynamic load factor (DLF1) extracted from the measured data, as these are key parameters of interest in civil engineering applications. The verification was conducted in several phases. First, kinematic data from 39 markers attached to the human body were used to get the trajectory of body’s centre of mass (BCoM). This trajectory, in conjunction with the vertical and longitudinal ground reaction forces (GRFs) were then used to plot the leg force – leg length relationship. Linear curve fitting yielded the spring stiffness and spring length parameters. These parameters, together with the body mass and damping coefficient, were fed into the DBIP model to identify initial conditions for the motion of BCoM leading to the stable periodic gait. For the 14 test subjects, the model produced excellent agreement with the measured step frequency and DLF1, while relative error for the walking speed was between -12.6% and 2.0%. The simulated waveform of the vertical BCoM displacement was found to match the measured signal well. In addition, match between simulated and measured GRF profiles was satisfactory except for one test subject. Regression functions have been established for estimating model parameters, based on which step-by-step procedures have then been proposed for modelling a pedestrian within the population. The results suggest that the DBIP succeeds in modelling pedestrians walking on rigid level surfaces. In the next step the model should be considered for modelling pedestrians on vibrating civil engineering structures.

STrain Analysis and Mapping of the Plantar Surface (STAMPS): A novel technique of plantar load analysis during gait.

Diabetic foot ulceration is driven by peripheral neuropathy, resulting in abnormal foot biomechanics and elevated plantar load. Plantar load comprises normal pressure and tangential shear stress. Currently, there are no in-shoe devices measuring both components of plantar load. The STAMPS (STrain Analysis and Mapping of the Plantar Surface) system was developed to address this and utilises digital image correlation (DIC) to determine the strain sustained by a plastically deformable insole, providing an assessment of plantar load at the foot-surface interface during gait. STAMPS was developed as a multi-layered insole, comprising a deformable mid-layer, onto which a stochastic speckle pattern film is applied. A custom-built imaging platform is used to obtain high resolution pre- and post-walking images. Images are imported into commercially available DIC software (GOM Correlate, 2020) to obtain pointwise strain data. The strain and displacement data are exported and post-processed with custom analysis routines (MATLAB, Mathworks Inc.), to obtain the resultant global and regional peak strain (SMAG), antero-posterior strain (SAP) and medio-lateral strain (SML). To validate the core technique an experimental test process used a Universal Mechanical Tester (UMT) system (UMT TriboLab, Bruker) to apply controlled vertical and tangential load regimes to the proposed multi-layer insole. A pilot study was then conducted to assess the efficacy of using the STAMPS system to measure in-shoe plantar strain in three healthy participants. Each participant walked 10 steps on the STAMPS insole using a standardised shoe. They also walked 10 m in the same shoe using a plantar pressure measurement insole (Novel Pedar®) to record peak plantar pressure (PPP) as a gold-standard comparator. The results of the experimental validation tests show that with increased normal force, at a constant shear distance, SMAG increased in a linear fashion. Furthermore, they showed that with increased shear distance, at a constant force, SMAG increased. The results of the pilot study found participant 1 demonstrated greatest SMAG in the region toes 3-5 (15.31%). The highest mean SMAG for participant 2 was at the hallux (29.31%). Participant 3 exhibited highest strain in the regions of the first and second metatarsal heads (58.85% and 41.62% respectively). Increased PPP was strongly associated with increased SMAG with a Spearman's correlation coefficient 0.673 (p < 0.0001). This study has demonstrated the efficacy of a novel method to assess plantar load across the plantar surface of the foot. Experimental testing validated the sensitivity of the method to both normal pressure and tangential shear stress. This technique was successfully incorporated into the STAMPS insole to reliably measure and quantify the cumulative degree of strain sustained by a plastically deformable insole during a period of gait, which can be used to infer plantar loading patterns. Future work will explore how these measures relate to different pathologies, such as regions at risk of diabetic foot ulceration.

Control of stability in semi-passive robot based on RBF neural network

The stability control of passive walking has been a major challenge in the field of passive robots because of its significant nonlinear characteristics. Using the adaptive sliding mode control based on local approximation of Radial Basis Function Neural Network (RBFNN), the walking stability of the simplest point foot model of biped passive robot is investigated in this paper. Simulation results show that the passive robot with this control method could achieve stable periodic gait with fewer steps than the robot without the control. In addition, given different control target state functions according to the characteristics of passive walking gait, passive robot can stabilize to corresponding periodic walking state with small control errors under the adaptive sliding mode control based on RBFNN. Finally, the influences of the gain coefficients of weight updating formula of RBFNN and the robustness coefficients of adaptive sliding mode controller on the control error results are discussed. It is found that the error decreases when the parameters increase within a certain limit, and the control effect becomes less obvious when the parameter exceeds the limit range.

Multistability of gaits, the basin of attraction and its external topology in the simplest passive walking model on stairs

For the passive walking robot, the basin of attraction is very small and thin, and thus makes it a great challenge to produce stable passive dynamic walking. This paper reports systematically the effect of initial conditions on gait dynamics of the simplest passive walking model on stairs. We find two new gaits with period-3 and period-4, and they both lead to higher periodic gaits and chaos by period-doubling bifurcations. The critical period-3 unstable orbit generated via the cyclic-fold bifurcation takes part in the annihilation of chaotic attractors twice, and this double boundary crisis becomes the main cause to the falling down on stairs of the passive walker. In particular, we compute the Lyapunov exponents to verify the effectiveness of the bifurcation process of these new gaits. More importantly, through analyzing the topology of phase space, the external domain of basin of attraction is classified in detail and marked precisely with different falling patterns. We believe such findings to add significantly to the knowledge of the selection of initial values and to be potentially helpful in the design and optimization of passive walking robots.