This paper presents a novel method for detecting and estimating contact forces only from human motions using machine learning techniques. Knowing the location of the contacts with the environment and the magnitude of the exerted force is critical for dynamic human motion analysis. However, their annotation is usually made manually from captured motion data especially in case of multiple contacts even if the data includes force measurement. Moreover, most existing human motion datasets do not include contact force. To overcome these bottlenecks, we introduce a network that leverages vector-quantized variational autoencoder (VQ-VAE) and self-attention that learns a small set of discrete feature values representing various contact states. These feature values, called contact codes, allow human motions to be converted to contact states and resulting forces. By applying an optimization for contact estimation with a reduced set of manual annotations, the existence of contacts can be automatically determined, which is essential information for dynamic analysis. We validated the effectiveness and potential usefulness of the proposed method with a human walking gait dataset, by converting the human motions into contact sequences and forces and applying the estimated contacts to dynamic motion analysis.

This paper presents an inverse kinematics (IK) method which can control future velocities and accelerations for multi-body systems. The proposed IK method is formulated as a quadratic programing (QP) that optimizes future joint trajectories. The features of the proposed IK are: (1) the evaluation of accelerations at future time instances, (2) the trajectory representation that can implicitly integrate the time integral formula into QP, (3) the computation of future Jacobian matrices based on the comprehensive theory of differential kinematics proposed in our previous work. Those features enable a stable and fast IK computation while evaluating the future accelerations. We also conducted thorough numerical studies to show the efficiency of the proposed method.

The paper aims to develop a methodology of transferring the knowledge obtained from the experiments of laboratory animals to human musculoskeletal system. To achieve the goal, we propose a method for estimating the homologous posture of the mammalian skeletal system corresponding to the human body posture. We hypothesize the homology of bone geometry between mammalian species implies that of biomechanical functions. The method relies on this homology and determines the homologous postures according to the anatomical landmarks of bone geometry. This paper shows the results of the analysis on homologous postures between the human and mouse skeletal models to validate our hypothesis. A pilot study also introduces comparison of mechanical functions between the two models by using the homologous postures.

Motor functions of the biological system has been forged through 4 billion years evolution. From a neurorobotics view, it is important not only to know how well it works, but also how it fails. To quantitatively describe early onset symptoms of a neurodegenerative disease, we analyzed phenotypes of genetically engineered Huntington disease (HD) model mice, which reveal progressive impaired motor functions. We devised a simple yet sensitive paradigm called the crystalized motion profile (CMP), by which we successfully detected subtle difference between normal and abnormal mice in terms of whole-body level motor coordination. Our long-term objective is to remodel human mind and body to regain impaired motor and cognitive functions with ageing. To do so, we are developing a soft neurorobotic suit that provides integrated cognitive and physical interventions to users. Our analysis on the HD model mice is important as the first step to bridge between molecular mechanisms (altered genetic code) and the macroscopic neuro-musculoskeletal model. With this, we can extrapolate from knowledge of non-human mammals to human to derive the remodeling.

This paper presents a novel optimization method for generating persistently exciting trajectories for inertial parameters identification of a robot. The exciting performance of the trajectories is usually evaluated by the condition number of the regressor matrix, which appears in the linear regression model for identification. In this paper, the efficient formulation is presented to directly compute the gradient of the condition number with respect to joint trajectory parameters, by deriving the derivative of the singular values and regressor matrices. Direct gradient computation can enhance computational performance of optimization, which is essential for large DOF systems under many physical consistent conditions such as humanoid robots. The proposed method is validated by generating several trajectories for the humanoid robot HRP-4.

This paper presents a novel method for retargeting human motions onto a humanoid robot. The method solves the following three simultaneous problems: the geometric parameter identification that morphs the human model to the robot model, motion planning for a robot, and the inverse kinematics of the human motion-capture data. Simultaneous solutions can imitate the original motion more accurately than conventional approaches, which solve the problems sequentially. The proposed method can reconstruct the human motion within the physical constraints imposed by robot dynamics. A reconstruction step enables quantitative analysis of the retargeting results through direct comparison with the original human motion. The method can also provide the precise morphing function as well as subject-specific models, which can handle the different body dimensions of human subjects. This new framework is suitable for applications that require an accurate generation of human-like motions with quantitative evaluation criteria, such as humanoid robots that evaluate assistive devices. Experimental tests of the proposed method were performed with humanoid robot HRP-4.

We propose a method for estimation of humanoid and human links' inertial parameters. Our approach formulates the problem as a hierarchical quadratic program by exploiting the linear properties of rigid body dynamics with respect to the inertia parameters. In order to assess our algorithm, we conducted experiments with a humanoid robot and a human subject. We compared ground reaction forces and moments estimated from force measurements with those computed using identified inertia parameters and movement information. Our method is able to accurately reconstruct ground reaction forces and force moments. Moreover, our method is able to estimate correctly masses of the robots links and to accurately detect additional masses placed on the human subject during the experiments.

This paper presents the method to retarget human motion. The method can evaluate the ability of the preservation of the original characteristics of human motion data. It enables to compute the joint trajectories of the human corresponding with the retargeted ones of the robot at the same time, by utilizing the geometric identification technique. The obtained trajectories of a human are the solution to minimize the cost function about motion reproduction. The proposed method is efficient for such applications that the robot needs to mimic human motion without modifying the detailed features of the original movement of each body segment. The results of the retargeted motions to a humanoid robot are shown.

The mass parameters of robots influence performances of model-based control and validation of the simulation results. The mass parameters provided by CAD data are usually rough approximation of the true parameters. Therefore several methods for estimation of those parameters have been proposed. Their precision depends on the used motion, called optimal exciting trajectories. This paper describes a new approach to determine humanoid robot exciting trajectories for mass parameters identification. The method was inspired by the studies done in the field of human mass parameters identification, and it is based on observation of condition numbers of sub-regressor matrices created from the columns of the regressor matrix. The method has been experimentally applied to identify mass parameters of HRP-2 and HRP-4 humanoid robots. The proposed method is able to reconstruct ground reaction forces and force moments more accurately than parameters obtained from CAD data.

Musculoskeletal models are effective tools for understanding living systems. To ensure proper model function, they must be checked against the literature or specimens. Existing checking methods require cadaver experimentation, highly knowledgeable medical personnel, and/or significant time. In this paper, we propose a quick and efficient method, called functional consistency checking, for use when these resources are not available. This method uses the literature to define a set of mathematical constraints, custom inverse dynamics software to interact with the model and its Jacobian in realtime and then evaluates the models consistency with these constraints. The method's usefulness will be demonstrated by constructing a human hand prototype, performing functional consistency checking, and then comparing the original to the output using data from a pianist motion capture.

This paper presents a fast inverse kinematics (IK) algorithm. In recent years, the robotics computation theory is often applied for detailed and complex multi-body systems. However, the computational complexity of IK is too high to be implemented in large DOF systems. IK of multi-body system is often formulated as nonlinear optimization to minimize the residuals from the references. It usually requires the computation of the gradient vector of the evaluation function. In the method, the computation of the gradient is decomposed into two parts. First, the residuals are considered as external forces and are distributed to each link. Then, the gradient can be computed from static equilibrium by the recursive Newton-Euler algorithm. In addition with the efficient direction search algorithms of nonlinear programing, the computation complexity of IK can be dramatically reduced. The results of numerical evaluation using a large-DOF manipulator and a human musculoskeletal model are shown.

To identify the joint parameters (e.g. the position of the joint center for a spherical joint, the position and the orientation of the joint axis for a revolute joint, etc.) from motion capture data, existing provably-correct algorithms require that at least three markers be attached to either of the two links adjacent to the joint. However, as shown in this article, it turns out that the identification of the joint parameters requires, for most types of joints, strictly less than three markers on any link. More precisely, we prove the structural identifiability of joint parameters in the following cases: (a) a spherical joint with two markers attached to each of the two adjacent links; (b) a revolute joint with two markers attached to one of the two links, and one marker attached to the other. We provide a practical algorithm to do the identification in case (a). Finally, we show that identification cannot be achieved with strictly fewer markers than listed in (a) and (b).

In this paper, we propose the inverse kinematics method based on high-order moment features and their Jacobian matrices, which can use an arbitrary information source about the shape of the targeted kinematic chain as reference input. The method is especially useful to generate the motion of humanoid robots and human figures, and we can generate the whole body pose from a set of 3D markers or pixels of 2D images, without labeling each feature point. The moment features are computed from various types of quantities, for example, geometric points, mass density, pixel images, and the probability labeled as a specific link, the fact of which shows generality and versatility of the method. Some results of motion of a human figure from the label-less 3D markers and the 2D images are illustrated.

The mass parameters of the human body segments are important when studying motion dynamics and the in vivo method to obtain accurate parameters is required in biomechanics studies and for some medical applications. In our previous works, we proposed the method to identify inertial parameters of human body segments in real-time during measurement of motion. However, some obtained parameters are not physically consistent; some masses are negative and inertia tensor matrices are not positive definite. These parameters generate problems in the analysis and the simulation requiring physical consistency. In this paper, we propose the real-time identification method considering physical consistency.

In current biomechanics approach, the assumptions are commonly used in body-segment parameters and muscle strength parameters due to the difficulty in accessing those subject-specific values. Especially in the rehabilitation and sports science where each subject can easily have quite different anthropometry and muscle condition due to disease, age or training history, it would be important to identify those parameters to take benefits correctly from the recent advances in computational musculoskeletal modeling. In this paper, Mass Distribution Identification to improve the joint torque estimation and Muscle Strength Identification to improve the muscle force estimation were performed combined with previously proposed methods in muscle tension optimization. This first result highlights that the reliable muscle force estimation could be extracted after these identifications. The proposed framework toward subject-specific musculoskeletal modeling would contribute to a patient-oriented computational rehabilitation.

The mass properties are important to control robot dynamics or study human dynamics. In our previous works, we proposed a method to identify inertial parameters of legged mechanisms from base-link dynamics, using generalized coordinates and external forces information. In this paper, we propose an identification method based on floating-base dynamics, when the system has no external force. Inertial parameters can be identified without force measurement, only from motion data. The method has been tested on two examples; a simple chain consisted of two links and the human body dynamics.

When robots cooperate with humans it is necessary for robots to move safely on sudden impact. Joint torque sensing is vital for robots to realize safe behavior and enhance physical performance. Firstly, this paper describes a new torque sensor with linear encoders which demonstrates electro magnetic noise immunity and is unaffected temperature changes. Secondly, we propose a friction compensation method using a disturbance observer to improve the positioning accuracy. In addition, we describe a torque feedback control method which scales down the motor inertia and enhances the joint flexibility. Experimental results of the proposed controller are presented.

The identification method for industrial manipulators considering physical consistency such as positive definiteness of inertial parameters has been developed, however it has to solve the quadratic programming with the non-linear inequality constraints. In identifying the large DOF systems like humanoid robots, the converged solution is difficult to be obtained. In this paper, we propose the method to realize physical consistency and computational stability. As inertial parameters of each link are represented with a finite number of mass points, the constraints can be approximated by linear inequalities. We also propose to solve the optimization problem, which minimizes the errors both from measured data and the priori parameters extracted from the geometric model like CAD data. The method can estimate standard inertial parameters, which is a useful notation to be used for other applications.

Identification results dramatically depend on the excitation properties of the motion used to sample the identification model. Strategies to define persistent exciting trajectories have been developed for manipulator robots with few DOF. However they can not easily be extended to humanoid systems and humans due to the important number of DOF; and empirical knowledge is often used to generate and select persistent exciting motions. In this paper we propose a method to choose persistent exciting motions from an existing dataset in order to optimize both the identification results and the computation time. This method is based on the use of the identification model of legged systems obtained from the base-link equations. Instead of using well-established consideration on the condition number of the regressor matrix, the method uses a decomposition of the regressor into elementary sub-regressors and the computation of the condition number for each. A selection rule is then proposed. The overall method is experimentally tested to identify the human body inertial parameters using a data-set of 40 motions. Comparative results obtained from different combinations of motions are given.

Mass parameters of the human body segments are mandatory when studying motion dynamics. In orthopedics, biomechanics and rehabilitation they are of crucial importance. Inaccuracies their value generate errors in the motion analysis, misleading the interpretation of results. No systematic method to estimate them has been proposed so far. Rather, parameters are scaled from generic tables or estimated with methods inappropriate for in-patient care. Based on our previous works, we propose a real-time software and its interface that allow to estimate the whole-body segment parameters, and to visualize the progresses of the completion of the identification. The visualization is used as a visual feedback to optimize the excitation and thus the identification results. The method is experimentally tested and obtained results are discussed.

When simulating and controlling robot dynamics it is necessary to know the inertial parameters and the joint dynamics accurately. As these parameters are usually not provided by manufacturers, identification is then an essential step in robotics. In addition with the up coming wide-spreading of humanoid robots in the society the identification of humanoid dynamics has became mandatory to insure safety. This paper proposes a method to estimate humanoid robots inertial parameters using a minimal set of sensors. Only joint angles and external forces information are required. Simulations have provided exciting trajectories that are reproduced on a small-size humanoid robot. Experimental results are given.