An arc-length warping algorithm for gesture recognition using quaternion representation

Cifuentes, Jenny; Pham, Minh Tu; Moreau, Richard; Prieto, Flavio; Boulanger, Pierre

doi:10.1109/embc.2013.6610981

Cited by 2 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, solely leveraging 3D positions as input channels led to poor segmentation and classification performance. In the same way, Cifuentes et al [41] used only quaternion information to retrieve operator gestures (i.e., intention could be correlated to orientation as well). Based on this analysis, we concluded that the operators intentions could be determined using rigid transformation data.…”

Section: Discussion a Kinematic Channels Impactmentioning

confidence: 99%

Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training

Despinoy

Bouget

Forestier

et al. 2016

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

Abstract-Dexterity and procedural knowledge are two critical skills surgeons need to master to perform accurate and safe surgical interventions. However, current training systems do not allow to provide an in-depth analysis of surgical gestures to precisely assess these skills. Our objective is to develop a method for the automatic and quantitative assessment of surgical gestures. To reach this goal, we propose a new unsupervised algorithm that can automatically segment kinematic data from robotic training sessions. Without relying on any prior information or model, this algorithm detects critical points in the kinematic data which define relevant spatio-temporal segments. Based on the association of these segments, we obtain an accurate recognition of the gestures involved in the surgical training task. We then perform an advanced analysis and assess our algorithm using datasets recorded during real expert training sessions. After comparing our approach with the manual annotations of the surgical gestures, we observe 97.4% accuracy for the learning purpose and an average matching score of 81.9% for the fullyautomated gesture recognition process. Our results show that trainees workflow can be followed and surgical gestures may be automatically evaluated according to an expert database. This approach tends towards improving training efficiency by minimizing the learning curve.

show abstract

Section: Discussion a Kinematic Channels Impactmentioning

confidence: 99%

Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training

Despinoy

Bouget

Forestier

et al. 2016

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

show abstract

Surgical gesture classification using Dynamic Time Warping and affine velocity

Cifuentes¹,

Pham²,

Moreau³

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

Minimally Invasive Surgery (MIS) has become widespread as an important surgical technique due to its advantages related to pain relief and short recovery time periods. However, this approach implies the acquisition of special surgical skills, which represents a challenge in the objective assessment of surgical gestures. In this way, several studies shown that kinematics and kinetic analysis of hand movement is a valuable assessment tool of basic surgical skills in MIS. In addition, recent researches proved that human motion performed during surgery can be described as a sequence of constant affine velocity movements. In this paper, we present a novel method to classify gestures based on an affine velocity analysis of 3D motion and an implementation of the Dynamic Time Warping algorithm. In particular, affine velocity calculation correlates kinematics and geometrical variables such as curvature, torsion, and euclidean velocity, reducing the dimension of the conventional 3D problem. In this way, using the simplicity of dynamic time warping algorithm allows us to perform an accurate classification, easier to implement and understand. Experimental validation of the algorithm is presented based on the position and orientation data of a laparoscope instrument, determiMinimally Invasive Surgery (MIS) has become widespread as an important surgical technique due to its advantages related to pain relief and short recovery time periods. However, this approach implies the acquisition of special surgical skills, which represents a challenge in the objective assessment of surgical gestures. In this way, several studies shown that kinematics and kinetic analysis of hand movement is a valuable assessment tool of basic surgical skills in MIS. In addition, recent researches proved that human motion performed during surgery can be described as a sequence of constant affine velocity movements. In this paper, we present a novel method to classify gestures based on an affine velocity analysis of 3D motion and an implementation of the Dynamic Time Warping algorithm. In particular, affine velocity calculation correlates kinematics and geometrical variables such as curvature, torsion, and euclidean velocity, reducing the dimension of the conventional 3D problem. In this way, using the simplicity of dynamic time warping algorithm allows us to perform an accurate classification, easier to implement and understand. Experimental validation of the algorithm is presented based on the position and orientation data of a laparoscope instrument, determined by six cameras. Results show the advantages of the proposed method compared to conventional Multidimensional Dynamic Time Warping to classify surgical gestures in MIS.ned by six cameras. Results show the advantages of the proposed method compared to conventional Multidimensional Dynamic Time Warping to classify surgical gestures in MIS.

show abstract

An arc-length warping algorithm for gesture recognition using quaternion representation

Cited by 2 publications

References 13 publications

Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training

Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training

Surgical gesture classification using Dynamic Time Warping and affine velocity

Contact Info

Product

Resources

About