Cognitive-Motor Integration in Assessing Bimanual Motor Skills

Biomedical Engineering Department, Rensselaer Polytechnic Institute, NY, USA Accurate assessment of bimanual motor skills is essential across various professions, yet, traditional methods often rely on subjective assessments or focus solely on motor actions, overlooking the integral role of cognitive processes. This study introduces a novel approach by leveraging deep neural networks (DNNs) to analyze and integrate both cognitive decision-making and motor execution. We tested this methodology by assessing laparoscopic surgery skills within the Fundamentals of Laparoscopic Surgery program, which is a prerequisite for general surgery certification. Utilizing video capture of motor actions and non-invasive functional near-infrared spectroscopy (fNIRS) for measuring neural activations, our approach precisely classifies subjects by expertise level and predicts FLS behavioral performance scores, significantly surpassing traditional single-modality assessments. In this study, we introduce a novel approach by conducting a direct statistical comparative analysis between neural activations and motor actions for assessing bimanual motor skills using DNNs. We explore the synergy of these modalities in multimodal analysis, applied to precision and cognitive-demanding tasks, particularly within the Fundamentals of Laparoscopic Surgery (FLS) program (Figure 1).