NDLI: Motion generation of robotic surgical tasks: Learning from expert demonstrations

Content Provider	IEEE Xplore Digital Library
Author	Reiley, C.E. Plaku, E. Hager, G.D.
Copyright Year	2010
Description	Author affiliation: Department of Computer Science, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA (Reiley, C.E.; Plaku, E.; Hager, G.D.)
Abstract	Robotic surgical assistants offer the possibility of automating portions of a task that are time consuming and tedious in order to reduce the cognitive workload of a surgeon. This paper proposes using programming by demonstration to build generative models and generate smooth trajectories that capture the underlying structure of the motion data recorded from expert demonstrations. Specifically, motion data from Intuitive Surgical's da Vinci Surgical System of a panel of expert surgeons performing three surgical tasks are recorded. The trials are decomposed into subtasks or surgemes, which are then temporally aligned through dynamic time warping. Next, a Gaussian Mixture Model (GMM) encodes the experts' underlying motion structure. Gaussian Mixture Regression (GMR) is then used to extract a smooth reference trajectory to reproduce a trajectory of the task. The approach is evaluated through an automated skill assessment measurement. Results suggest that this paper presents a means to (i) extract important features of the task, (ii) create a metric to evaluate robot imitative performance (iii) generate smoother trajectories for reproduction of three common medical tasks.
Starting Page	967
Ending Page	970
File Size	1410965
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5627594
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Surgery Hidden Markov models Trajectory Surges Robots Feature extraction Training
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Recognition of user's activity for adaptive cooperative assistance in robotic surgery

Automatic sleep staging based on ECG signals using hidden Markov models

Analysis of EEG to quantify depth of anesthesia using Hidden Markov Model

Cardiac arrhythmia classification using wavelets and hidden markov models – a comparative approach

A modified multi-channel EMG feature for upper limb motion pattern recognition

Segmenting human motion for automated rehabilitation exercise analysis

Motion trajectory analysis for evaluating the performance of functional upper extremity tasks in daily living: a pilot study

On-line apnea-bradycardia detection using hidden semi-Markov models

Motion Part Regularization: Improving action recognition via trajectory group selection

Motion generation of robotic surgical tasks: Learning from expert demonstrations

Similar Documents

Recognition of user's activity for adaptive cooperative assistance in robotic surgery

Automatic sleep staging based on ECG signals using hidden Markov models

Analysis of EEG to quantify depth of anesthesia using Hidden Markov Model

Cardiac arrhythmia classification using wavelets and hidden markov models – a comparative approach

A modified multi-channel EMG feature for upper limb motion pattern recognition

Segmenting human motion for automated rehabilitation exercise analysis

Motion trajectory analysis for evaluating the performance of functional upper extremity tasks in daily living: a pilot study

On-line apnea-bradycardia detection using hidden semi-Markov models

Motion Part Regularization: Improving action recognition via trajectory group selection

Motion generation of robotic surgical tasks: Learning from expert demonstrations