Hese tasks. The purpose of this study was to determine elbow range of motion, including flexion-extension, pronation-supination, and varus-valgus angulation, with use of three-dimensional optical tracking technology for several previously described positional and functional tasks along with various contemporary tasks. Methods: Twenty-five patients performed six positional and eleven functional tasks (both historical and contemporary). Elbow flexion-extension, varus-valgus, and forearm rotation (pronation and supination) ranges of motion were measured. Results: Positional tasks required a minimum (mean and standard deviation) of 27° ± 7° of flexion and a maximum of 149° ± 5° of flexion. Forearm rotation ranged from 20.0° ± 18° of pronation to 104° ± 10° of supination. Varus and valgus angulations ranged between 2° ± 5° of varus to 9° ± 5° of valgus. For functional tasks, the maximum flexion arc was 130° ± 7°, with a minimum value recorded as 23° ± 6° and a maximum value recorded as 142° ± 3°. All of these were for the cellular telephone task. The maximum pronation-supination arc (103° ± 34°) was found with using a fork. Maximum pronation was found with typing on a keyboard (65° ± 8°). Maximum supination was found with opening a door (77° ± 13°). Maximum varus-valgus arc of motion was 11° ± 4°. Minimum valgus (0° ± 6°) was found with cutting with a knife, while maximum valgus (13° ± 6°) was found with opening a door. Conclusions: Functional elbow range of motion necessary for activities of daily living may be greater than previously reported. Contemporary tasks, such as using a computer mouse and keyboard, appear to require greater pronation than other tasks, and using a cellular telephone usually requires greater flexion than other tasks....

This paper introduces a general optimisation-based method for identification of biomechanically relevant parameters in kinematically determinate and over-determinate systems from a given motion. The method is designed to find a set of parameters that is constant over the time frame of interest as well as the time-varying system coordinates, and it is particularly relevant for biomechanical motion analysis where the system parameters can be difficult to accurately determine by direct measurements. Although the parameter identification problem results in a large-scale optimisation problem, we show that, due to a special structure in the linearised Karush–Kuhn–Tucker optimality conditions, the solution can be found very efficiently. The method is applied to a set of test problems relevant for gait analysis. These involve determining the local coordinates of markers placed on the model, segment lengths and joint axes of rotation from both gait and range of motion experiments.