A robotic bipedal model for human walking with slips

Chen, Kuo; Trkov, Mitja; Yi, Jingang; Zhang, Yizhai; Liu, Tao; Song, Dezhen

doi:10.1109/icra.2015.7140084

Cited by 25 publications

(5 citation statements)

References 14 publications

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“…As a strong coupled humachine system, the walking gait of the lower-limb exoskeleton robot is highly consistent with the human’s [5]. In human walking, the lower-limb joints have similar motion in the same gait phase [6,7]. The walking performance of the exoskeleton is mainly determined by the following three aspects: gait trajectory generation, gait execution and gait assessment [3,8,9], which are all related to gait phases.…”

Section: Introductionmentioning

confidence: 99%

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Liu

Wang

et al. 2016

Sensors

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Gait phase is widely used for gait trajectory generation, gait control and gait evaluation on lower-limb exoskeletons. So far, a variety of methods have been developed to identify the gait phase for lower-limb exoskeletons. Angular sensors on lower-limb exoskeletons are essential for joint closed-loop controlling; however, other types of sensors, such as plantar pressure, attitude or inertial measurement unit, are not indispensable.Therefore, to make full use of existing sensors, we propose a novel gait phase recognition method for lower-limb exoskeletons using only joint angular sensors. The method consists of two procedures. Firstly, the gait deviation distances during walking are calculated and classified by Fisher’s linear discriminant method, and one gait cycle is divided into eight gait phases. The validity of the classification results is also verified based on large gait samples. Secondly, we build a gait phase recognition model based on multilayer perceptron and train it with the phase-labeled gait data. The experimental result of cross-validation shows that the model has a 94.45% average correct rate of set (CRS) and an 87.22% average correct rate of phase (CRP) on the testing set, and it can predict the gait phase accurately. The novel method avoids installing additional sensors on the exoskeleton or human body and simplifies the sensory system of the lower-limb exoskeleton.

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Section: Introductionmentioning

confidence: 99%

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Liu

Wang

et al. 2016

Sensors

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“…The HZD stability conditions and properties are also discussed under a set of slip recovery gaits that are obtained from human subject experiments. This paper extends the previous conference publications [16,17] by providing additional details in bipedal model derivation, model validation, detailed HZD analyses of slip recovery stability examples and experiments.…”

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confidence: 63%

Bipedal Model and Hybrid Zero Dynamics of Human Walking With Foot Slip

Trkov

Chen

2019

Journal of Computational and Nonlinear Dynamics

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Foot slip is one of the major causes of falls in human locomotion. Analytical bipedal models provide an insight into the complex slip dynamics and reactive control strategies for slip-induced fall prevention. Most of the existing bipedal dynamics models are built on no foot slip assumption and cannot be used directly for such analysis. We relax the no-slip assumption and present a new bipedal model to capture and predict human walking locomotion under slip. We first validate the proposed slip walking dynamic model by tuning and optimizing the model parameters to match the experimental results. The results demonstrate that the model successfully predicts both the human walking and recovery gaits with slip. Then, we extend the hybrid zero dynamics (HZD) model and properties to capture human walking with slip. We present the closed-form of the HZD for human walking and discuss the transition between the non-slip and slip states through slip recovery control design. The analysis and design are illustrated through human walking experiments. The models and analysis can be further used to design and control wearable robotic assistive devices to prevent slip-and-fall. IntroductionFoot slip is one of the major causes for human falls and injuries. Slip-induced falls cause enormous economic and societal costs [1]. The direct costs for non-fatal fall-related injures among US elderly (≥ 65 years) were 19 billion dollars in the year 2000 [1] and increased to over 31 billion in the year 2015 [2]. Among the occupational population in the US, slips, trips and falls represented 27% of all non-fatal oc- * The authors equally contributed to this work. † Address all correspondence to J. Yi.cupational injuries in year 2015 [3]. To develop effective fall prevention strategies and technologies, it is critical to understand human locomotion and balance recovery under slip. Modeling of human walking locomotion with slip is an effective approach to assist in the design and control of new wearable assistive devices. Slip-and-fall has been extensively studied in the past two decades, for example, [4,5] and references therein. Most of these studies focus on human subjects and clinical experiments and a few use human locomotion dynamics to analyze the slipping mechanism. Simulation-based dynamic models are used to study motion stability of slip and fall. In [5], a 7-link, 9-degree-of-freedom (DOF) walking model in the sagittal plane with a 16-element foot model is used to simulate the human reaction control to a novel slip in gait. In [6], a simulation model is optimized with human experiments. Using this model, stability results are obtained and compared with the dynamic balance analyses by a simple invented pendulum model. The 2D musculoskeletal model in the sagittal plane is also discussed in [7] to determine the impact of the reduced required coefficient of friction (RCOF) on gait kinematics. Kinematic and muscle activity-based data-driven analysis (e.g., Lyapunov exponents) are used to capture the walking stability [8].Robotic bipedal mod...

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“…Combining the shoe-floor interactions model in this paper with the bipedal dynamics (e.g., Ref. [26]) to study the walking stability under slip is also among our future research tasks.…”

Section: Resultsmentioning

confidence: 99%

Shoe–Floor Interactions in Human Walking With Slips: Modeling and Experiments

Trkov

Liu

et al. 2018

Journal of Biomechanical Engineering

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Shoe-floor interactions play a crucial role in determining the possibility of potential slip and fall during human walking. Biomechanical and tribological parameters influence the friction characteristics between the shoe sole and the floor and the existing work mainly focus on experimental studies. In this paper, we present modeling, analysis, and experiments to understand slip and force distributions between the shoe sole and floor surface during human walking. We present results for both soft and hard sole material. The computational approaches for slip and friction force distributions are presented using a spring-beam networks model. The model predictions match the experimentally observed sole deformations with large soft sole deformation at the beginning and the end stages of the stance, which indicates the increased risk for slip. The experiments confirm that both the previously reported required coefficient of friction (RCOF) and the deformation measurements in this study can be used to predict slip occurrence. Moreover, the deformation and force distribution results reported in this study provide further understanding and knowledge of slip initiation and termination under various biomechanical conditions.

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A robotic bipedal model for human walking with slips

Cited by 25 publications

References 14 publications

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Bipedal Model and Hybrid Zero Dynamics of Human Walking With Foot Slip

Shoe–Floor Interactions in Human Walking With Slips: Modeling and Experiments

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