Search

This paper presents a new, simple model to evaluate the instrumental random errors in kinematic analysis of human movements using stereophotogrammetry. By means of equations analogous to that relate linear or angular momentum with linear or angular velocities, a direct measurement of instantaneous motion can be made without previous finite displacement analysis. Single explicit expressions can be obtained to evaluate the influence of instrumental random errors in the accuracy of the kinematic variables. From these expressions, some conclusions about the effect of marker cluster design on the experimental errors are obtained. An experiment has been carried out in order to validate the proposed technique and to assess the experimental errors in linear and angular velocity measurement and its influence in instantaneous helical axis determination. [ABSTRACT FROM AUTHOR]

This paper presents an inertial based sensing system for real-time three-dimensional measurement of human spinal motion, in a portable and non-invasive manner. Applications of the proposed system range from diagnosis of spine injury to postural monitoring, on-field as well as in the lab setting. The system is comprised of three inertial measurement sensors, respectively attached and calibrated to the head, torso and hips, based on the subject's anatomical planes. Sensor output is transformed into meaningful clinical parameters of rotation (twist), flexion-extension and lateral bending of each body segment, with respect to calibrated global reference space. Modeling the spine as a compound flexible pole model allows dynamic measurement of three-dimensional spine motion, which can be animated and monitored in real-time using our interactive GUI. The accuracy of the proposed sensing system has been verified with subject trials using a VICON optical motion measurement system. Experimental results indicate an error of less than 3.1 degrees in segment orientation tracking. [ABSTRACT FROM AUTHOR]

Research has focused on improvement of the quality of life of stroke patients. Gait detection, kinematics and kinetics analysis, home-based rehabilitation and telerehabilitation are the areas where there has been increasing research interest. The paper reviews position-sensing technologies and their application for human movement tracking and stroke rehabilitation. The review suggests that it is feasible to build a home-based telerehabilitation system for sensing and tracking the motion of stroke patients. [ABSTRACT FROM AUTHOR]

Muscle modelling is an important component of body segmental motion analysis. Although many studies had focused on static conditions, the relationship between electromyographic (EMG) signals and joint torque under voluntary dynamic situations has not been well investigated. The aim of this study was to investigate the performance of a recurrent artificial neural network (RANN) under voluntary dynamic situations for torque estimation of the elbow complex. EMG signals together with kinematic data, which included angle and angular velocity, were used as the inputs to estimate the expected torque during movement. Moreover, the roles of angle and angular velocity in the accuracy of prediction were investigated, and two models were compared. One model used EMG and joint kinematic inputs and the other model used only EMG inputs without kinematic data. Six healthy subjects were recruited, and two average angular velocities (60 degrees s(-1) and 90 degrees s(-1)) with three different loads (0 kg, 1 kg, 2 kg) in the hand position were selected to train and test the RANN between 90 degrees elbow flexion and full elbow extension (0 degrees). After training, the root mean squared error (RMSE) between expected torque and predicted torque of the model, with EMG and joint kinematic inputs in the training data set and the test data set, were 0.17 +/- 0.03 Nm and 0.35 +/- 0.06 Nm, respectively. The RMSE values between expected torque and predicted torque of the model, with only EMG inputs in the training data set and the test set, were 0.57 +/- 0.07 Nm and 0.73 +/- 0.11 Nm, respectively. The results showed that EMG signals together with kinematic data gave significantly better performance in the joint torque prediction; joint angle and angular velocity provided important information in the estimation of joint torque in voluntary dynamic movement. [ABSTRACT FROM AUTHOR]