Alejandro González

Estimation of the center of mass with Kinect and Wii balance board

Center of mass (CoM) trajectory is important during standing and walking since it can be used as an index for stability and fall prediction. Unfortunately current methods for CoM estimation require the use of specialized equipment (such as motion capture and force platforms) in controlled environments. This paper aims at applying the statically equivalent serial chain (SESC) method to obtain CoM position using widely available and portable hardware; a Microsofts Kinect and a Nintendos Wii balance board. During identification, CoM is approximated by CoP measurements and the virtual chain is created for able-bodied subjects. The result demostrates that the SESC method can be applied outside the laboratory environment using a Kinect. Cross-validation of the identified model was performed to evaluate the accuracy of the method. Results obtained of five subjects are shown and discussed.

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.

Adaptive Interface for Personalized Center of Mass Self-Identification in Home Rehabilitation

As the center of mass (CoM) position can be used to determine stability, current rehabilitation standards may be improved by tracking it. A personalized CoM estimate can be obtained using the statically equivalent serial chain (SESC) once the model parameters are identified. The identification phase can be completed using low-cost sensors (Kinect and Wii balance board) outside the laboratory making CoM estimation feasible in a patients home. This paper focuses on: 1) improving the SESC identification quality and speed and 2) using the estimated CoM to determine stability. Identification time is reduced by creating a visual adaptive interface where the subjects limbs are colored based on the convergence of the SESC parameters. A study was conducted on eight subjects and showed a faster convergence and lower root mean square error (RMSE) when the adaptive interface was used. We found that a model capable of estimating the CoM position with an RMSE of 27 mm could be obtained after only 90 s of identification when the interface was used, whereas twice as much time was needed when the interface was not used. The interface that was developed can be used by a subject to track his/her CoM position in a self-directed way. Stability was determined for a squat task using a dynamic index obtained from the estimated CoM trajectory and using only Kinect measurements. This shows one potential application for home rehabilitation and monitoring.

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.

Subject-specific Center of Mass Estimation for In-home Rehabilitation - Kinect-Wii board vs. Vicon-Force plate

An increasingly aging society creates the need for a reliable evaluation of postural stability, specially for rehabilitation. Estimation of a subjects center of mass (CoM) is important for the assessment of unsupported, stable standing. A portable, in-home estimation of CoM can be used as a rehabilitation tool and could be achieved using a Microsofts Kinect. To validate this approach we compare the performance of two statically equivalent serial chains. One of them was identified using a Kinect and a Wii board, while the other one was obtained from measurements performed with a motion capture system and a force plate. Their similar performance on a validation set indicates that it is feasible to perform subject specific center of mass estimation in the home environment.

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).

Online identification and visualization of the statically equivalent serial chain via constrained Kalman filter

A humans center of mass (CoM) trajectory is useful to evaluate the dynamic stability during daily life activities such as walking and standing up. To estimate the subject-specific CoM position in the home environment, we make use of a statically equivalent serial chain (SESC) developed with a portable measurement system. In this paper we implement a constrained Kalman filter to achieve an online estimation of the SESC parameters while accounting for the human bodys bilateral symmetry. This results in constraining SESC parameters to be consistent with the human skeletal model used. The proposed identification method can inform the subject or the therapist, in real-time, about the quality of the on-going CoM estimation. This information can be helpful to reduce the identification time and establish a personalized protocol. A Kinect is used as a markerless motion capture system for measuring limb orientations while the Wii board is used to measure the subjects center of pressure (CoP) during the identification phase. CoP measurements and Kinect data were recorded for four able-bodied subjects. The recorded data was then given to the proposed recursive algorithm to identify the parameters of the SESC online. A cross-validation test was performed to verify the identification performance. The results for these subjects are shown and discussed.

Three dimensional visualization of the statically equivalent serial chain from kinect recording

We develop and present a portable tool intended for real time estimation of the center of mass (CoM) in human subjects. Using the statically equivalent serial chain (SESC) method we can account for subject specificity after identification of the models parameters. CoM position estimates are then available from measurements of the subjects limbs orientations. For portability, we make use of widely accessible sensors such as the Kinect and Wii balance board for identification. Use of the Kinect as a measurement device allows us to establish the SESC outside of the laboratory, without many special considerations on the environment. Only Kinect is used for CoM tracking after identification was performed. We present here an overview of the SESC concept and the identification procedure. The aspects involved in the visualization tool are discussed and results are shown in order to verify the performance.

A Low-Cost Data Acquisition System for Automobile Dynamics Applications

This project addresses the need for the implementation of low-cost acquisition technology in the field of vehicle engineering: the design, development, manufacture, and verification of a low-cost Arduino-based data acquisition platform to be used in <80 Hz data acquisition in vehicle dynamics, using low-cost accelerometers. In addition to this, a comparative study is carried out of professional vibration acquisition technologies and low-cost systems, obtaining optimum results for low- and medium-frequency operations with an error of 2.19% on road tests. It is therefore concluded that these technologies are applicable to the automobile industry, thereby allowing the project costs to be reduced and thus facilitating access to this kind of research that requires limited resources.

A personalized balance measurement for home-based rehabilitation

Accurate and real time balance estimation can be used to improve home based rehabilitation systems. We developed a personalized balance measurement, making use of the subject-specific center of mass (CoM) estimate offered by the statically equivalent serial chain (SESC) method and the zero rate of change of angular momentum (ZRAM) concept to evaluate balance during a series of dynamic motions. Two healthy subjects were asked to stand on a Wii balance board and their SESC parameters were identified. A set of dynamic motions was then recorded and the rate of change of centroidal angular momentum and the distance of the ZRAM point to the center line of the support polygon were obtained. A good match between both metrics was found. Additionally, we developed a real time application based on Kinect measurements that determines the ZRAM position, in real time, and displays it to the subject in the form of visual feedback. In this way the ZRAM can be used to evaluate balance in home-rehabilitation for any motion.