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Matthew N. Dailey

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3 papers

2 author rows

AIIM Journal 2011 Journal Article

Intelligent dental training simulator with objective skill assessment and feedback

Phattanapon Rhienmora
Peter Haddawy
Siriwan Suebnukarn
Matthew N. Dailey

Objective We present a dental training simulator that provides a virtual reality (VR) environment with haptic feedback for dental students to practice dental surgical skills in the context of a crown preparation procedure. The simulator addresses challenges in traditional training such as the subjective nature of surgical skill assessment and the limited availability of expert supervision. Methods and materials We identified important features for characterizing the quality of a procedure based on interviews with experienced dentists. The features are patterns combining tool position, tool orientation, and applied force. The simulator monitors these features during the procedure, objectively assesses the quality of the performed procedure using hidden Markov models (HMMs), and provides objective feedback on the user's performance in each stage of the procedure. We recruited five dental students and five experienced dentists to evaluate the accuracy of our skill assessment method and the quality of the system's generated feedback. Results The experimental results show that HMMs with selected features can correctly classify all test sequences into novice and expert categories. The evaluation also indicates a high acceptance rate from experts for the system's generated feedback. Conclusion In this work, we introduce our VR dental training simulator and describe a mechanism for providing objective skill assessment and feedback. The HMM is demonstrated as an effective tool for classifying a particular operator as novice-level or expert-level. The simulator can generate tutoring feedback with quality comparable to the feedback provided by human tutors.

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IROS Conference 2009 Conference Paper

Intelligent vehicle localization using GPS, compass, and machine vision

Somphop Limsoonthrakul
Matthew N. Dailey
Manukid Parnichkun

Intelligent vehicles require accurate localization relative to a map to ensure safe travel. GPS sensors are among the most useful sensors for outdoor localization, but they still suffer from noise due to weather conditions, tree cover, and surrounding buildings or other structures. In this paper, to improve localization accuracy when GPS fails, we propose a sequential state estimation method that fuses data from a GPS device, an electronic compass, a video camera, and wheel encoders using a particle filter. We process images from the camera using a color histogram-based method to identify the road and non-road regions in the field of view in front of the vehicle. In two experiments, in simulation and on a real vehicle, we demonstrate that, compared to a standard extended Kalman filter not using image data, our method significantly improves lateral localization error during periods of GPS inaccuracy.

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AIIM Journal 2007 Journal Article

Anatomical sketch understanding: Recognizing explicit and implicit structure

Peter Haddawy
Matthew N. Dailey
Ploen Kaewruen
Natapope Sarakhette
Le Hong Hai

Objective Sketching is ubiquitous in medicine. Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applications ranging from automated patient records to medical education software could benefit greatly from the richer and more natural interfaces that would be enabled by the ability to understand sketches. In this paper we take the first steps toward developing a system that can understand anatomical sketches. Methods Understanding an anatomical sketch requires the ability to recognize what anatomical structure has been sketched and from what view (e. g. parietal view of the brain), as well as to identify the anatomical parts and their locations in the sketch (e. g. parts of the brain), even if they have not been explicitly drawn. We present novel algorithms for sketch recognition and for part identification. We evaluate the accuracy of the recognition algorithm on sketches obtained from medical students. We evaluate the part identification algorithm by comparing its results to the judgment of an experienced physician. Results The sketch recognition algorithm achieves a recognition accuracy of 75. 5%, far above the baseline random classification accuracy of 6. 7%. Comparison of the results of the part identification algorithm with the judgment of an experienced physician shows close agreement in terms of location, orientation, size, and shape of the identified parts. Conclusions The performance of our prototype in terms of accuracy and running time provides strong evidence that development of robust sketch understanding systems for medical domains is an attainable goal. Further work needs to be done to extend the approach to sketches containing multiple and partial anatomical structures, as well as to be able to interpret sketch annotations.

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