Development of an automated method to detect sitting pivot transfer phases using biomechanical variables: toward a standardized method.

BACKGROUND

Sitting pivot transfer (SPT) is one of the most important, but at the same time strenuous at the upper extremity, functional task for spinal cord injured individuals. In order to better teach this task to those individuals and to improve performance, a better biomechanical understanding during the different SPT phases is a prerequisite. However, no consensus has yet been reached on how to depict the different phases of the SPT. The definition of the phases of the SPT, along with the events characterizing these phases, will facilitate the interpretation of biomechanical outcome measures related to the performance of SPTs as well as strengthen the evidence generated across studies.

METHODS

Thirty-five individuals with a spinal cord injury performed two SPTs between seats of similar height using their usual SPT technique. Kinematics and kinetics were recorded using an instrumented transfer assessment system. Based on kinetic and kinematic measurements, a relative threshold-based algorithm was developed to identify four distinct phases: pre-lift, upper arm loading, lift-pivot and post-lift phases. To determine the stability of the algorithm between the two SPTs, Student t-tests for dependent samples were performed on the absolute duration of each phase.

RESULTS

The mean total duration of the SPT was 2.00 ± 0.49 s. The mean duration of the pre-lift, upper arm loading, lift-pivot and post-lift phases were 0.74 ± 0.29 s, 0.28 ± 0.13 s, 0.72 ± 0.24 s, 0.27 ± 0.14 s whereas their relative contributions represented approximately 35%, 15%, 35% and 15% of the overall SPT cycle, respectively. No significant differences were found between the trials (p = 0.480-0.891).

CONCLUSION

The relative threshold-based algorithm used to automatically detect the four distinct phases of the SPT, is rapid, accurate and repeatable. A quantitative and thorough description of the precise phases of the SPT is prerequisite to better interpret biomechanical findings and measure task performance. The algorithm could also become clinically useful to refine the assessment and training of SPTs.