The Learning Curve of People with Complete Spinal Cord Injury Using a NES_s-FES_s Interface in the Sitting Position: Pilot Study

The use of assistive technologies, such as a non-invasive interface for neuroelectrical signal and functional electrical stimulation (NES<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>s</mi></msub></semantics></math></inline-formula>-FES<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>s</mi></msub></semantics></math></inline-formula>), can mitigate the effects of spinal cord injury (SCI), including impairment of motor, sensory, and autonomic functions. However, it requires an adaptation process to enhance the user’s performance by tuning the learning curve to a point of extreme relevance. Therefore, in this pilot study, the learning curves of two people with complete SCI (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>P</mi><mi>A</mi></msub></semantics></math></inline-formula>: paraplegic-T<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>6</mn></msub></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>P</mi><mi>B</mi></msub></semantics></math></inline-formula>: quadriplegic-C<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>4</mn></msub></semantics></math></inline-formula>) were analyzed, with results obtained on the accuracy of the classifier (Ac<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mi>C</mi><mi>S</mi><mi>P</mi><mo>−</mo><mi>L</mi><mi>D</mi><mi>A</mi></mrow></msub></semantics></math></inline-formula>), repetitions of intra-day training, and number of hits and misses in the activation of FES<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>s</mi></msub></semantics></math></inline-formula> for sixteen interventions using the NES<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>s</mi></msub></semantics></math></inline-formula>-FES<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>s</mi></msub></semantics></math></inline-formula> interface. We assumed that the data were non-parametric and performed the Spearman’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ρ</mi></semantics></math></inline-formula> test (and <i>p</i>-value) for correlations between the data. There was variation between the learning curves resulting from the training of the NES<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>s</mi></msub></semantics></math></inline-formula>-FES<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>s</mi></msub></semantics></math></inline-formula> interface for the two participants, and the variation was influenced by factors both related and unrelated to the individual users. Regardless of these factors, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>P</mi><mi>A</mi></msub></semantics></math></inline-formula> improved significantly in its learning curve, as it presented lower values in all variables in the first interventions compared to the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>P</mi><mi>B</mi></msub></semantics></math></inline-formula>, although only <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>P</mi><mi>A</mi></msub></semantics></math></inline-formula> showed statistical correlation (on Ac<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mi>C</mi><mi>S</mi><mi>P</mi><mo>−</mo><mi>L</mi><mi>D</mi><mi>A</mi></mrow></msub></semantics></math></inline-formula> values in RLL). It was concluded that despite the variations according to factors intrinsic to the user and the functioning of the equipment used, sixteen interventions were sufficient to achieve a good learning effect to control the NES<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>s</mi></msub></semantics></math></inline-formula>-FES<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>s</mi></msub></semantics></math></inline-formula> interface.