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MS-TCRNet: Multi-Stage Temporal Convolutional Recurrent Networks for Action Segmentation Using Sensor-Augmented

Adam Goldbraikh¹, Omer Shubi², Or Rubin²

¹Applied Mathematics Department at the Technion - Israel Institute of Technology, Haifa, 3200003, Israel.

Pattern Recognition

|November 4, 2024

Summary

This summary is machine-generated.

This study introduces novel Multi-Stage Temporal Convolutional Recurrent Networks (MS-TCRNet) and data augmentation methods for action segmentation using kinematic data. These advancements achieve state-of-the-art results in surgical skill assessment.

Keywords:

Action segmentation Data augmentation Deep learning Kinematic data

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Area of Science:

Computer Science
Robotics
Biomedical Engineering

Background:

Action segmentation is crucial for high-level process analysis using sensor data.
Kinematic data analysis presents unique challenges for accurate action segmentation.
Existing methods may not fully leverage the geometric properties of kinematic data.

Purpose of the Study:

To develop advanced deep learning models for action segmentation on kinematic data.
To introduce novel data augmentation techniques tailored for kinematic data.
To improve the performance and robustness of action segmentation algorithms in surgical tasks.

Main Methods:

Introduction of two versions of Multi-Stage Temporal Convolutional Recurrent Networks (MS-TCRNet).
MS-TCRNet architectures feature a prediction generator with intra-stage regularization and Bidirectional LSTM/GRU refinement stages.
Proposal of two new data augmentation techniques: World Frame Rotation and Hand Inversion.

Main Results:

State-of-the-art performance achieved on three surgical suturing datasets (VTS, BRS, JIGSAWS).
Demonstrated improved algorithm performance and robustness through proposed data augmentation techniques.
Validation of MS-TCRNet effectiveness on complex surgical simulation tasks.

Conclusions:

The proposed MS-TCRNet models and data augmentation techniques significantly advance action segmentation on kinematic data.
These methods offer a robust solution for analyzing surgical skills and other complex human actions.
The open-source code facilitates further research and application in related fields.