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Interest in arm movements has increased tremendously in recent years. This interest has been motivated by different goals: the desire for a more scientific approach to replacement or support of the joints of the upper limb, the need for input to biomechanical computer models, and the clinical interest in comparing normal movements with pathological movements. The availability of commercial marker-tracking systems has facilitated achieving these goals. However, the complex nature of arm movements and the lack of standardized movements raises many challenges. In comparison with gait analysis, few arm motion analyses have been conducted. The purpose of this review is to aid researchers and clinicians interested in conducting an arm motion study in choosing the appropriate methodology. This is accomplished both by describing the methods used in past investigations and by highlighting important findings. Due to the variety of research goals, there is sometimes more than one appropriate method and the choice is left to the reader. Nevertheless, since it is extremely desirable to record and express the data in a standardized way, standardization proposals are described. This review, which focuses on methodology rather than results, addresses the following topics: motivations and tasks studied, tracking methods, the shoulder complex, joint centres and rotation axes, marker positions, coordinate system definitions, terminology and rotations, accuracy, and presentation methods.

Tekscan pressure sensors are used in biomechanics research to measure joint contact loads. While the overall accuracy of these sensors has been reported previously, the effects of different calibration algorithms on sensor accuracy have not been compared. The objectives of this validation study were to determine the most appropriate calibration method supplied in the Tekscan program software and to compare its accuracy to the accuracy obtained with two user-defined calibration protocols. We evaluated the calibration accuracies for test loads within the low range, high range, and full range of the sensor. Our experimental setup used materials representing those found in standard prosthetic joints, i.e., metal against plastic. The Tekscan power calibration was the most accurate of the algorithms provided with the system software, with an overall rms error of 2.7% of the tested sensor range, whereas the linear calibrations resulted in an overall rms error of up to 24% of the tested range. The user-defined ten-point cubic calibration was almost five times more accurate, on average, than the power calibration over the full range, with an overall rms error of 0.6% of the tested range. The user-defined three-point quadratic calibration was almost twice as accurate as the Tekscan power calibration, but was sensitive to the calibration loads used. We recommend that investigators design their own calibration curves not only to improve accuracy but also to understand the range(s) of highest error and to choose the optimal points within the expected sensing range for calibration. Since output and sensor nonlinearity depend on the experimental protocol (sensor type, interface shape and materials, sensor range in use, loading method, etc.), sensor behavior should be investigated for each different application.

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