Vincent Fohanno

Improvement of upper extremity kinematics estimation using a subject-specific forearm model implemented in a kinematic chain

Human movement reconstruction is still difficult due to noise generated by the use of skin markers. The a priori definition of a kinematic chain associated with a global optimisation method allows reducing these deleterious effects. When dealing with the forearm, this approach can be improved by personalising the two axes of rotation because their common modelling is not representative of joint geometry. The aim of the present study is to evaluate the kinematic effects of personalising these two axes of rotation, determined by a functional method and implemented in a kinematic chain (AXIS model). The AXIS model was compared with a reference model (ISB model), in which the forearm axes of rotation were defined according to the recommendations of the International Society of Biomechanics. The kinematic comparison (15 subjects and 3 tasks) was based on marker residuals (actual versus model-determined), joint kinematic root mean square differences (AXIS versus ISB) and joint amplitudes (AXIS versus ISB). The AXIS model improved the pose of the forearm and hand. The reduction in marker residuals for these segments ranged between 23% and 60%. The use of a functional method was also beneficial in personalising the flexion-extension and pronation-supination axes of the forearm. The contribution of pronation-supination, in terms of joint amplitudes, was increased by 15% during the specific task. The approach developed in this study is all the more interesting since this forearm model could be integrated into a kinematic chain to be used with a global approach becoming increasingly popular in biomechanics.

Computation of the 3D kinematics in a global frame over a 40m-long pathway using a rolling motion analysis system

A rolling motion analysis system has been purpose-built to acquire an accurate three-dimensional kinematics of human motion with large displacement. Using this device, the kinematics is collected in a local frame associated with the rolling motion analysis system. The purpose of this paper is to express the local kinematics of a subject walking on a 40 m-long pathway in a global system of co-ordinates. One participant performed five trials of walking while he was followed by a rolling eight camera optoelectronic motion analysis system. The kinematics of the trials were reconstructed in the global frame using two different algorithms and 82 markers placed on the floor organized in two parallel and horizontal lines. The maximal error ranged from 0.033 to 0.187 m (<0.5% of the volume diagonal). As a result, this device is accurate enough for acquiring the kinematics of cyclic activities with large displacements in ecological environment.

Estimation of the 3D kinematics in kayak using an extended Kalman filter algorithm: a pilot study

During sports activities, acquisition of whole body 3D kinematics in ecological conditions is challenging. Although optoelectronic systems are the most accurate solution, they are not used in these conditions because of experimental constraints (e.g. variation of light conditions, size of the volume calibrated, etc). These difficulties dramatically increase in the case of aquatic sports, such as kayaking and rowing. Recently, Begon et al. (2009a) have solved an important technological lock by placing an optoelectronic system on a rolling frame. In this way, they increased the volume of measurement while keeping a good accuracy of 3D kinematic reconstruction. Estimating joint kinematics of a human chain model using a classical approach such as a global optimisation method (GO; Lu and O’Connor 1999) with a large number of markers remains time consuming. Moreover, in our case where some segments are hidden and closed loops on the feet and the paddle must be respected, the use of kinematic constraints may be helpful. To estimate the joint kinematics of a chain model, the extended Kalman filter with kinematic constraints (EKF), based on the Kalman filter and adapted to nonlinear systems, showed promising results in biomechanics (Halvorsen et al. 2008). The comparison between GO and EKF methods has already been assessed (De Groote et al. 2008). However, these authors did not evaluate the effects of the marker number on kinematics reconstruction. The purpose of this study is to compare an EKF algorithm to a GO algorithm using three sets of skin markers and closed-loop constraints.