Whole body segment inertia parameters estimation from movement and ground reaction forces: a feasibility study

Robert, Thomas; Leborgne, P.; Abid, Mahdi; Bonnet, Vincent; Venture, Gentiane; Dumas, Raphaël

doi:10.1080/10255842.2017.1382919

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…To solve the indeterminacy problem of DSP, Quanbury and Winter [ 27 ] and Robertson and Winter [ 28 ] suggest extrapolating the

signals during DSP by fitting cubic polynomials to the known force values at the beginning and end of the SSP for each foot. A cubic spline was fitted to a set of points derived from heel, ankle and toe kinematics to estimate

.…”

Section: Methods Based On Measured Kinematic Datamentioning

confidence: 99%

Measurement of Walking Ground Reactions in Real-Life Environments: A Systematic Review of Techniques and Technologies

Shahabpoor

Pavić

2017

Sensors

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Monitoring natural human gait in real-life environments is essential in many applications, including quantification of disease progression, monitoring the effects of treatment, and monitoring alteration of performance biomarkers in professional sports. Nevertheless, developing reliable and practical techniques and technologies necessary for continuous real-life monitoring of gait is still an open challenge. A systematic review of English-language articles from scientific databases including Scopus, ScienceDirect, Pubmed, IEEE Xplore, EBSCO and MEDLINE were carried out to analyse the ‘accuracy’ and ‘practicality’ of the current techniques and technologies for quantitative measurement of the tri-axial walking ground reactions outside the laboratory environment, and to highlight their strengths and shortcomings. In total, 679 relevant abstracts were identified, 54 full-text papers were included in the paper and the quantitative results of 17 papers were used for meta-analysis and comparison. Three classes of methods were reviewed: (1) methods based on measured kinematic data; (2) methods based on measured plantar pressure; and (3) methods based on direct measurement of ground reactions. It was found that all three classes of methods have competitive accuracy levels with methods based on direct measurement of the ground reactions showing highest accuracy while being least practical for long-term real-life measurement. On the other hand, methods that estimate ground reactions using measured body kinematics show highest practicality of the three classes of methods reviewed. Among the most prominent technical and technological challenges are: (1) reducing the size and price of tri-axial load-cells; (2) improving the accuracy of orientation measurement using IMUs; (3) minimizing the number and optimizing the location of required IMUs for kinematic measurement; (4) increasing the durability of pressure insole sensors, and (5) enhancing the robustness and versatility of the ground reactions estimation methods to include pathological gaits and natural variability of gait in real-life physical environment.

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“…To solve the indeterminacy problem of DSP, Quanbury and Winter [ 27 ] and Robertson and Winter [ 28 ] suggest extrapolating the

.…”

Section: Methods Based On Measured Kinematic Datamentioning

confidence: 99%

Measurement of Walking Ground Reactions in Real-Life Environments: A Systematic Review of Techniques and Technologies

Shahabpoor

Pavić

2017

Sensors

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“…The use of the anthropometric tables is currently the most used tool for estimating such values. Even though the tables allow to scale the model along with the subject, this could be a rough approximation for the non-standard population samples (children, elderly, obese, individual with prostheses) and this is pushing the scientific community towards the development of new alternative ways to estimate these parameters directly from data [Venture et al, 2009], [Robert et al, 2017].…”

Section: Discussionmentioning

confidence: 99%

Human Whole-Body Dynamics Estimation for Enhancing Physical Human-Robot Interaction

Latella

2019

Preprint

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In the last two decades the scientific community has shown a great interest in understanding and shaping the interaction mechanisms between humans and robots. The interaction implies communication between two dyadic agents and, if the type of interaction is 'physical', the communication is represented by the set of forces exchanged during the interaction. Within this context, the role of quantifying these forces becomes of pivotal importance for understanding the interaction mechanisms. At the current scientific stage, classical robots are built to act for humans, but the scientific demand is going towards the direction of robots that will have to collaborate with humans. This will be possible by providing the robots with sufficient pieces of information of the agent they are interacting with (i.e., kinematic and dynamic model of the human). In a modern age where humans need the help of robots − apparently in an opposite trend − this thesis attempts to answer the following questions: "Do robots need humans? Should robots know their human partners?". A tentative answer is provided here in the form of a novel framework for the simultaneous human whole-body motion tracking and dynamics estimation, in a real-time scenario. The framework encompasses a set of body-mounted sensors and a probabilistic algorithm able of estimating physical quantities that in humans are not directly measurable (i.e., torques and internal forces). This thesis is mainly focussed on the paramount role in retrieving the human dynamics estimation but straightforwardly it leaves the door open to the next development step: passing the human dynamics feedback to the robot controllers. This step will enable the robot with the capability to observe and understand the human partner by generating an enhanced (intentional) interaction.First of all, I would like to thank Francesco (Nori) for the opportunity he gave me three years ago: they have been, professionally and personally speaking, very beautiful years. To Francesco (Romans) for helping, supporting and tolerating me and to Daniele for being a great friend of mine before that of a colleague. To Silvio for his constant help and for what he has developed throughout these years: he gave us a solid foundation on which developing our work.To the new An.Dy group. To Luca (Tagliapietra), he was the last one to arrive but he is already a companion of a thousand misadventures (or 'disagi', as we prefer to call them). To Yeshi, our new control man, for his important contribution to the human-in-the-loop stuff! And to Diego, our technician. But above all, to Marta and Maria, our M&M's: even if they are not currently part of our group, they collaborated in the past to build what the human research line is now . For me, they will be always An.Dy girls! To all my lab colleagues (of today and of the past):

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“…The use of the anthropometric tables is currently the most widespread tool for estimating such values. Even though they allow to scale the model along with the subject, this could be a rough approximation for the non-standard population samples (children, elderly, obese, individual with prostheses) and this is pushing the scientific community towards the development of new alternative ways to estimate these parameters directly from data (Venture et al 2009;Robert et al 2017).…”

Section: Discussionmentioning

confidence: 99%

Towards real-time whole-body human dynamics estimation through probabilistic sensor fusion algorithms

et al. 2018

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Physical human-robot interaction is receiving a growing attention from the scientific community. One of the main challenges is to understand the principles governing the mutual behaviour during collaborative interactions between humans. In this context, the knowledge of human whole-body motion and forces plays a pivotal role. Current state of the art methods, however, do not allow one for reliable estimations of the human dynamics during physical human-robot interaction. This paper builds upon our former work on human dynamics estimation by proposing a probabilistic framework and an estimation tool for online monitoring of the human dynamics during human-robot collaboration tasks. The soundness of the proposed approach is verified in human-robot collaboration experiments and the results show that our probabilistic framework is able to estimate the human dynamic, thereby laying the foundation for more complex collaboration scenarios.

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Whole body segment inertia parameters estimation from movement and ground reaction forces: a feasibility study

Abstract: a univ lyon, université Claude Bernard lyon 1, iFsttar, lBmC umr_t9406, lyon, France; b lissi, university of paris-est Créteil; c department of mechanical systems engineering, tokyo university of agriculture and technology

Cited by 6 publications

References 6 publications

Measurement of Walking Ground Reactions in Real-Life Environments: A Systematic Review of Techniques and Technologies

Measurement of Walking Ground Reactions in Real-Life Environments: A Systematic Review of Techniques and Technologies

Human Whole-Body Dynamics Estimation for Enhancing Physical Human-Robot Interaction

Towards real-time whole-body human dynamics estimation through probabilistic sensor fusion algorithms

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