The purpose of this study was to quantitatively compare the difference in voluntary upper extremity movements between subjects with and without spasticity. Eight normal subjects (mean 26.7 6 2.8 years, four males and four females) and seven subjects with spasticity (mean 25.9 6 4.3 years, three males and four females) were involved in this study. The subjects sat in an adjustable chair and performed two voluntary tasks involving the elbow joint. Task A was to move the hand between two touch-plates which were mounted 28 cm apart on the surface of the table. Task B was to flex and extend the elbow joint in the sagittal plane with the forearm in neutral position. Reflective markers were attached on the shoulder, the elbow and the wrist. A Peak5 video-based motion analysis system was used to record the positions of the markers in the three-dimensional (3-D) space during the movement tasks. A set of quantitative parameters were used to document the elbow movement. The results revealed that in comparison to normal subjects, subjects with spasticity exhibited a higher average jerk, a larger standard deviation of the coordinates of the markers along the movement path, a larger standard deviation of the angle between the plane of the elbow joint and the horizontal plane, and a longer 3-D path length. The characteristics of spastic elbow movement and the usage of quantitative parameters were discussed.

Spasticity often interferes with function, limits independence and may cause considerable disability. Elbow joint movement is involved in many daily living activities. A surface EMG driven musculoskeletal model was developed to predict joint trajectory and to compare the differences in the model parameters between the normal and spastic subjects. Three musculotendon actuators whose EMG could be assessed by surface electrodes (biceps, brachioradialis and triceps) were included in this musculoskeletal model. The proposed model took several sets of parameters (anthropometric parameters of the skeleton and muscle parameters) as inputs. Surface EMG signals of the three muscle groups were rectified, moving-averaged, scaled and converted to active states. These active states together with the initial angular position and velocity of the joint were also used as inputs for the model. The outputs were muscle forces and the trajectory of the elbow joint. Two groups of parameters, namely, maximal isometric muscle stress and electromechanical delay were estimated using the trajectory fitting algorithm. Results indicated that the model was successful in using the surface EMG as input signals in the prediction of elbow joint trajectory. The spastic subjects showed a lower maximum isometric muscle stress and longer electromechanical delay.

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