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DTSTART:20180311T030000
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DTSTAMP:20180305T235555Z
UID:A4C1F5B7-D05F-11E7-A02F-0050568D7F66
DTSTART;TZID=Canada/Central:20171207T130000
DTEND;TZID=Canada/Central:20171207T150000
DESCRIPTION:In this talk\, I will overview our recent work on gesture-based
  haptic guidance for physical human-robot interaction (pHRI) that can be u
 sed in virtual minimally invasive surgery (MIS) training. I will present a
 n approach in which the knowledge and experience of experts have been mode
 led and used to improve the unpredictable motions of novice trainees. Two 
 statistical models\, hidden Markov model (HMM) and hidden Conditional Rand
 om Fields (HCRF)\, have been used to train gesture models for a virtual MI
 S related task. These models have also been used for automatic gesture seg
 mentation and recognition as well as generating guidance forces. The force
 s are adaptively calculated in real time with respect to gestural similari
 ties among user motions and the gesture models. If time permits\, I will p
 resent the current project to create an autonomous car for Auto Drive Chal
 lenge™ competition at Kettering University\, MI.\n\nWindsor\, Ontario\, 
 Canada\, N9B 2X4
LOCATION:Windsor\, Ontario\, Canada\, N9B 2X4
ORGANIZER:ejedari@ieee.org
SEQUENCE:1
SUMMARY:Towards Skill Transfer via Learning-Based Guidance:Adaptive Haptic 
 Guidance for Virtual Reality based Training 
URL;VALUE=URI:https://events.vtools.ieee.org/m/48686
X-ALT-DESC:Description: <br /><p>In this talk\, I will overview our recent 
 work on gesture-based haptic guidance for physical human-robot interaction
  (pHRI) that can be used in virtual minimally invasive surgery (MIS) train
 ing. I will present an approach in which the knowledge and experience of e
 xperts have been modeled and used to improve the unpredictable motions of 
 novice trainees. Two statistical models\, hidden Markov model (HMM) and hi
 dden Conditional Random Fields (HCRF)\, have been used to train gesture mo
 dels for a virtual MIS related task. These models have also been used for 
 automatic gesture segmentation and recognition as well as generating guida
 nce forces. The forces are adaptively calculated in real time with respect
  to gestural similarities among user motions and the gesture models. If ti
 me permits\, I will present the current project to create an autonomous ca
 r for Auto Drive Challenge&trade\; competition at Kettering University\, M
 I.</p>
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