The most important classification of spinal cord injury is the International Standard for the Neurological Classification of SCI (ISNCSCI) created by the American Spinal Injury Association (ASIA). In particular, 50% of them are due to road accident, 24% to fall, 17% to other causes, 6% to sports and 3% to extreme sports. Inside the spinal cord, there is a complex network of neural and vascular connections which can be affected by pathologies and traumatic injuries. Spinal cord injury is caused by one or more lesions of the spinal cord. The measurements computed by the SCI-APP show a good reliability for analyzing the movements of SCI patients’ propulsion wheelchair. For each group, the average value and the standard deviation were computed and a comparison with similar acquisitions performed with a high-end solution is shown.
To evaluate the reliability of the data computed with SCI APPlication (APP) for the pushing cycle analysis, the patients were subdivided in four groups according to the level of the spinal cord injury (i.e., high paraplegia, low paraplegia, C7 tetraplegia and C6 tetraplegia). More than 60 volunteers with a spinal cord injury were enrolled for testing the solution. Five movements have been considered to evaluate the wheelchair propulsion: the humeral elevation, the horizontal abduction of the humerus, the humeral rotation, the elbow flexion and the trunk extension along the sagittal plane. Data elaboration is focused on the automatic recognition of the pushing cycles and on plotting any kinematic parameter that may be interesting in the assessment. The configuration of the full-body motion capturing system is based on an asymmetric 3 Microsoft Kinect v2 sensor layout that provides a path of up to 6 m, which is required to properly track the wheelchair. This paper proposes a solution including customized motion capture and data elaboration with the aim of supporting medical personnel in the assessment of spinal cord-injured (SCI) patients using a wheelchair.
The main limit to their diffusion is not related to cost or usability but to the fact that the data generated when tracking a person must be elaborated according to the specific context and aim. In physical rehabilitation, motion capture solutions are well-known but not as widespread as they could be.
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