Human-Machine Collaborative Systems for Microsurgical Applications

Kragić, Danica; Marayong, Panadda; Li, M.; Okamura, Allison M.; Hager, Gregory D.

doi:10.1177/0278364905057059

Cited by 99 publications

(67 citation statements)

References 26 publications

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“…Models such as Hidden Markov Model (HMM), Dynamic Bayesian Network (DBN) and HHMM are popular techniques used for human motion modelling and a wide variety of other applications. The endless list includes assistive robotics [15], sign language and gesture modelling [17], robot assisted surgery [18] and many more. These models have found its applicability in the field of robotics due to its ability to handle data noise and capture both the spatial and temporal variability in the movement and the change in variance along the movement.…”

Section: Hierarchical Hidden Markov Model (Hhmm)mentioning

confidence: 99%

Language for learning complex human-object interactions

Patel

Kyriazis

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-In this paper we use a Hierarchical Hidden Markov Model (HHMM) to represent and learn complex activities/task performed by humans/robots in everyday life. Action primitives are used as a grammar to represent complex human behaviour and learn the interactions and behaviour of human/robots with different objects. The main contribution is the use of a probabilistic model capable of representing behaviours at multiple levels of abstraction to support the proposed hypothesis. The hierarchical nature of the model allows decomposition of the complex task into simple action primitives. The framework is evaluated with data collected for tasks of everyday importance performed by a human user.

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Section: Hierarchical Hidden Markov Model (Hhmm)mentioning

confidence: 99%

Language for learning complex human-object interactions

Patel

Kyriazis

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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“…Over the past few years, our group has developed several admittance control guidance methods, often called virtual fixtures [2], [1], [7]. In general, these methods operate as WeB8.5 follows: given sensed handle forces and torques f ∈ (6) exerted by the operator, a tool velocity screw is computed as…”

Section: B Software Implementationmentioning

confidence: 99%

Development and Application of a New Steady-Hand Manipulator for Retinal Surgery

Mitchell¹,

Koo²,

Iordachita³

et al. 2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

142

107

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Abstract-This paper describes the development and initial testing of a new and optimized version of a steady-hand manipulator for retinal microsurgery. In the steady-hand paradigm, the surgeon and the robot share control of a tool attached to the robot through a force sensor. The robot controller senses forces exerted by the operator on the tool and uses this information in various control modes to provide smooth, tremor-free, precise positional control and force scaling. The steady-hand manipulator reported here has been specifically designed with the unique constraints of retinal microsurgery in mind. In particular, the system makes use of a compact wrist design that places the bulk of the robot away from the operating field. The resulting system has high efficacy, flexibility and ergonomics while meeting the accuracy and safety requirements of microsurgery. We have now tested this robot on a biological model system and we report a protocol for reliably cannulating ∼80 µm OD veins (the size of veins in the human retina) using the system.

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“…2 shows typical simulated operator trajectories. These trajectory types may seem simple, but they were choosen because we believe that there exists several relevant tasks in areas such as medical surgery or automotive assembly that can be decomposed into a sequence of linear and circular motions, [5], [7] In section IV we perform similar experiments with a robotic manipulator to verify the simulated results. There, the data is taken from a trajectory-tracking task where the endeffector of the manipulator is force controlled by a human operator.…”

Section: Experimental Evaluation With Synthetic Datamentioning

confidence: 99%

Layered HMM for Motion Intention Recognition

Aarno

Kragić

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Acquiring, representing and modeling human skills is one of the key research areas in teleoperation, programmingby-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several subtasks in order to provide manageable modeling.In this paper we consider the use of a Layered Hidden Markov Model (LHMM) to model human skills. We evaluate a gestem classifier that classifies motions into basic action-primitives, or gestems. The gestem classifiers are then used in a LHMM to model a simulated teleoperated task. We investigate the online and offline classification performance with respect to noise, number of gestems, type of HMM and the available number of training sequences. We also apply the LHMM to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modeling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multi dimensional HMMs for gestem classification is small. It can also be seen that the LHMM is robust w.r.t misclassifications in the underlying gestem classifiers.

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Human-Machine Collaborative Systems for Microsurgical Applications

Cited by 99 publications

References 26 publications

Language for learning complex human-object interactions

Language for learning complex human-object interactions

Development and Application of a New Steady-Hand Manipulator for Retinal Surgery

Layered HMM for Motion Intention Recognition

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