Multi-Modal Gesture Recognition from Video and Surgical Tool Pose Information via Motion Invariants
This work addresses surgical gesture recognition for robotic surgery applications, representing an incremental improvement by integrating geometric-aware modeling of kinematics.
The paper tackled the problem of recognizing surgical gestures from multi-modal data by incorporating motion invariant measures (curvature and torsion) with vision and kinematics using a relational graph network, achieving 90.3% frame-wise accuracy on the JIGSAWS suturing dataset.
Recognizing surgical gestures in real-time is a stepping stone towards automated activity recognition, skill assessment, intra-operative assistance, and eventually surgical automation. The current robotic surgical systems provide us with rich multi-modal data such as video and kinematics. While some recent works in multi-modal neural networks learn the relationships between vision and kinematics data, current approaches treat kinematics information as independent signals, with no underlying relation between tool-tip poses. However, instrument poses are geometrically related, and the underlying geometry can aid neural networks in learning gesture representation. Therefore, we propose combining motion invariant measures (curvature and torsion) with vision and kinematics data using a relational graph network to capture the underlying relations between different data streams. We show that gesture recognition improves when combining invariant signals with tool position, achieving 90.3\% frame-wise accuracy on the JIGSAWS suturing dataset. Our results show that motion invariant signals coupled with position are better representations of gesture motion compared to traditional position and quaternion representations. Our results highlight the need for geometric-aware modeling of kinematics for gesture recognition.