Vincent Bonnet

Real-time Estimate of Body Kinematics During a Planar Squat Task Using a Single Inertial Measurement Unit

This study aimed at the real-time estimation of the lower-limb joint and torso kinematics during a squat exercise, performed in the sagittal plane, using a single inertial measurement unit placed on the lower back. The human body was modeled with a 3-DOF planar chain. The planar IMU orientation and vertical displacement were estimated using one angular velocity and two acceleration components and a weighted Fourier linear combiner. The ankle, knee, and hip joint angles were thereafter obtained through a novel inverse kinematic module based on the use of a Jacobian pseudoinverse matrix and null-space decoupling. The aforementioned algorithms were validated on a humanoid robot for which the mechanical model used and the measured joint angles virtually exhibited no inaccuracies. Joint angles were estimated with a maximal error of 1.5°. The performance of the proposed analytical and experimental methodology was also assessed by conducting an experiment on human volunteers and by comparing the relevant results with those obtained through the more conventional photogrammetric approach. The joint angles provided by the two methods displayed differences equal to 3 ± 1°. These results, associated with the real-time capability of the method, open the door to future field applications in both rehabilitation and sport.

Whole Body Center of Mass Estimation with Portable Sensors: Using the Statically Equivalent Serial Chain and a Kinect

The trajectory of the whole body center of mass (CoM) is useful as a reliable metric of postural stability. If the evaluation of a subject-specific CoM were available outside of the laboratory environment, it would improve the assessment of the effects of physical rehabilitation. This paper develops a method that enables tracking CoM position using low-cost sensors that can be moved around by a therapist or easily installed inside a patients home. Here, we compare the accuracy of a personalized CoM estimation using the statically equivalent serial chain (SESC) method and measurements obtained with the Kinect to the case of a SESC obtained with high-end equipment (Vicon). We also compare these estimates to literature-based ones for both sensors. The method was validated with seven able-bodied volunteers for whom the SESC was identified using 40 static postures. The literature-based estimation with Vicon measurements had a average error 24.9 ± 3.7 mm; this error was reduced to 12.8 ± 9.1 mm with the SESC identification. When using Kinect measurements, the literature-based estimate had an error of 118.4 ± 50.0 mm, while the SESC error was 26.6 ± 6.0 mm. The subject-specific SESC estimate using low-cost sensors has an equivalent performance as the literature-based one with high-end sensors. The SESC method can improve CoM estimation of elderly and neurologically impaired subjects by considering variations in their mass distribution.

A least-squares identification algorithm for estimating squat exercise mechanics using a single inertial measurement unit

This study investigated the possibility of estimating lower-limb joint kinematics during a squat exercise performed in the sagittal plane based on data collected from a single inertial measurement unit located on the lower trunk. The human body was modeled as a three-degrees-of-freedom planar chain and the relevant joint angles (ankle, knee, and hip) are represented by Fourier series. A least-squares approach based on the minimization of the difference between the measured and estimated linear accelerations and the angular velocity of the lower trunk was used to solve the related analytical problem. The approach was validated on ten healthy young volunteers (ten trials each) using a force plate and a stereophotogrammetric system to collect reference data. The root mean square differences between the estimated joint angles and those reconstructed with the stereophotogrammetric system were lower than 4° with correlation coefficients higher than 0.99. The ankle joint resultant vertical force component was estimated with an accuracy of about 3% and a high correlation coefficient of r=0.95, whereas much lower percentage accuracies were found for the horizontal force and couple components. The latter accuracies were similar to those affecting these force and couple components as estimated through inverse dynamics and the stereophotogrammetric data in conjunction with the same mechanical model, which suggests that only minor errors were introduced by the proposed algorithm and measurement tools.

Estimate of lower trunk angles in pathological gaits using gyroscope data

Trunk mobility impairment can cause balance, postural and gait challenges during overground level walking in patients with different pathologies. Assessment of the rotations of the trunk during walking with an abnormal gait can provide knowledge required for a better understanding of the nature of the motor control deficit and support decision-making in patient rehabilitation. A method based on the use of a weighted Fourier linear combiner (WFLC) adaptive filter is proposed in this paper for the estimation of lower trunk angles during pathological overground level walking, using angular velocities measured at the lower trunk level with a wearable inertial sensor. This method was validated for a group of 24 patients, 13 with hemiplegia and 11 with Parkinsons disease, by comparing the estimated angles to those simultaneously obtained from a stereophotogrammetric system. Analysis of the root mean square error, correlation coefficient and offset results revealed that the WFLC approach is highly accurate in estimating lateral and frontal bending and axial rotations of the lower trunk in pathological level walking.

Use of weighted Fourier linear combiner filters to estimate lower trunk 3D orientation from gyroscope sensors data.

BackgroundThe present study aimed at devising a data processing procedure that provides an optimal estimation of the 3-D instantaneous orientation of an inertial measurement unit (IMU). This result is usually obtained by fusing the data measured with accelerometers, gyroscopes, and magnetometers. Nevertheless, issues related to compensation of integration errors and high sensitivity of these devices to magnetic disturbances call for different solutions. In this study, a method based on the use of gyroscope data only is presented, which uses a Weighted Fourier Linear Combiner adaptive filter to perform a drift-free estimate of the 3D orientation of an IMU located on the lower trunk during walking.MethodsA tuning of the algorithm parameters and a sensitivity analysis to its initial conditions was performed using treadmill walking data from 3 healthy subjects. The accuracy of the method was then assessed using data collected from 15 young healthy subjects during treadmill walking at variable speeds and comparing the pitch, roll, and yaw angles estimated from the gyroscopes data to those obtained with a stereophotogrammetric system. Root mean square (RMS) difference and correlation coefficients (r) were used for this purpose.ResultsAn optimal set of values of the algorithm parameters was established. At all the observed speeds, and also in all the various sub-phases, the investigated angles were all estimated to within an average RMS difference of less than 1.2 deg and an average r greater than 0.90.ConclusionsThis study proved the effectiveness of the Weighted Fourier Linear Combiner method in accurately reconstructing the 3D orientation of an IMU located on the lower trunk of a subject during treadmill walking. This method is expected to also perform satisfactorily for overground walking data and to be applicable also to other “quasi-periodic” tasks, such as squatting, rowing, running, or swimming.

A structurally optimal control model for predicting and analyzing human postural coordination

This paper proposes a closed-loop optimal control model predicting changes between in-phase and anti-phase postural coordination during standing and related supra-postural activities. The model allows the evaluation of the influence of body dynamics and balance constraints onto the adoption of postural coordination. This model minimizes the instantaneous norm of the joint torques with a controller in the head space, in contrast with classical linear optimal models used in the postural literature and defined in joint space. The balance constraint is addressed with an adaptive ankle torque saturation. Numerical simulations showed that the model was able to predict changes between in-phase and anti-phase postural coordination modes and other non-linear transient dynamics phenomena.

Fast Determination of the Planar Body Segment Inertial Parameters Using Affordable Sensors

This study aimed at developing and evaluating a new method for the fast and reliable identification of body segment inertial parameters with a planar model using affordable sensors. A Kinect sensor, with a new marker-based tracking system, and a Wii balance board were used as an affordable and portable motion capture system. A set of optimal exciting motions was used in a biofeedback interface to identify the body segment parameters. The method was validated with 12 subjects performing various standardized motions. The same dynamometric quantities estimated both with the proposed system and, as a reference, with a laboratory grade force-plate were compared. The results showed that the proposed method could successfully estimate the resultant moment and the vertical ground reaction force (rms errors less than 8 Nm and 12 N, respectively). Finally, when local segment values were artificially varied, the proposed method was able to detect and estimate the additional masses accurately and with an error of less than 0.5 Kg, contrary to values generated with commonly used anthropometric tables.

Optimal Exciting Dance for Identifying Inertial Parameters of an Anthropomorphic Structure

Knowledge of the mass and inertial parameters of a humanoid robot or a human being is crucial for the development of model-based control, as well as for monitoring the rehabilitation process. These parameters are also important for obtaining realistic simulations in the field of motion planning and human motor control. For robots, they are often provided by computer-aided design data, while averaged anthropometric table values are often used for human subjects. The unit/subject-specific inertial parameters can be identified by using the external wrench caused by the ground reaction. However, the identification accuracy intrinsically depends on the excitation properties of the recorded motion. In this paper, a new method for obtaining optimal excitation motions is proposed. This method is based on the identification model of legged systems and on optimization processes to generate excitation motions while handling mechanical constraints. A pragmatic decomposition of this problem, the use of a new excitation criterion, and a quadratic program to identify inertial parameters are proposed. The method has been experimentally validated onto an HOAP-3 humanoid robot and with one human subject.

Determination of subject specific whole-body centre of mass using the 3D Statically Equivalent Serial Chain

This study investigates the possibility of using the so-called Statically Equivalent Serial Chain approach to estimate the subject-specific 3D whole-body centre of mass (CoM) location. This approach is based on a compact formulation of the 3D whole-body CoM position associated with a least squares identification process. This process requires a calibration phase that uses stereophotogrammetric and dynamometric data collected in selected static postures. After this calibration phase, the instantaneous position of the identified subject-specific 3D whole-body CoM can be estimated for any motor task using kinematic data only. This approach was experimentally validated on twelve healthy young subjects. The Statically Equivalent Serial Chain solution was validated during static trials with the centre of pressure, with the double integrated ground reaction forces during dynamic tasks, and also compared with a segmental method using a stereophotogrammetric system and anthropometric tables. Considerations relative to the choice of algorithm parameters, such as the number of necessary static postures and their time duration, are discussed. The proposed method shows much smaller differences between the projection of the centre of mass and the centre of pressure (root mean square value under 3.5%) than the method using anthropometric tables (root mean square value over 9%). Same conclusion can be made during dynamic tasks with a smaller difference obtained for SESC (root mean square value under 4% at contrary the 20% obtained with anthropometric table).

A constrained Extended Kalman Filter for dynamically consistent inverse kinematics and inertial parameters identification

This paper presents a method for the real-time determination of joint angles, velocities, accelerations and joint torques of a human. The proposed method is based on a constrained Extended Kalman Filter that combines stereophotogrammetric and dynamometric data. In addition to the joint variables, subject-specific segment lengths and inertial parameters are identified. Constraints are added to the filter, by restricting the optimal Kalman gain, in order to obtain physically consistent parameters. An optimal tuning procedure of the filters gains and a sensitivity analysis is presented. The method is validated in the plane on four human subjects and shows very good tracking of skin markers with a RMS difference lower than 15 mm. External ground reaction forces and resultant moment are also accurately estimated with an RMS difference below 3 N and 6 N.m, respectively.