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Cannulation is a routine yet challenging medical procedure resulting in a direct impact on patient outcomes. While current training programs provide guidelines to learn this complex procedure, the lack of objective and quantitative feedback impedes learning this skill more effectively. In this paper, we present a simulator for performing hemodialysis cannulation that captures the process using multiple sensing modalities that provide a multi-faceted assessment of cannulation. Further, we describe an algorithm towards segmenting the cannulation process using specific events in the sensor data for detailed analysis. Results from three participants with varying levels of clinical cannulation expertise are presented along with a metric that successfully differentiates the three participants. This work could lead to sensor-based cannulation skill assessment and training in the future potentially resulting in improved patient outcomes.

A Method for Segmenting the Process of Needle Insertion during Simulated Cannulation using Sensor Data