Effects of robotic manipulators on movements of novices and surgeons

Nisky, Ilana; Okamura, Allison M.; Hsieh, Michael H.

doi:10.1007/s00464-014-3446-5

Cited by 55 publications

(42 citation statements)

References 48 publications

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“…Finger movements, like eye movements, are a fundamental component of perception and action. Understanding the function and characteristics of finger movements during haptic perception may also be critical for translational research, such as the creation of haptic feedback in robot-assisted surgery, for which hand movement patterns of novice and expert surgeons may inform system design and training (Nisky et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Determining the bias and variance of a deterministic finger-tracking algorithm

Morash

Velden

2015

Behav Res

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Finger tracking has the potential to expand haptic research and applications, as eye tracking has done in vision research. In research applications, it is desirable to know the bias and variance associated with a finger-tracking method. However, assessing the bias and variance of a deterministic method is not straightforward. Multiple measurements of the same finger position data will not produce different results, implying zero variance. Here, we present a method of assessing deterministic finger-tracking variance and bias through comparison to a non-deterministic measure. A proof-of-concept is presented using a videobased finger-tracking algorithm developed for the specific purpose of tracking participant fingers during a psychological research study. The algorithm uses ridge detection on videos of the participant's hand, and estimates the location of the right index fingertip. The algorithm was evaluated using data from four participants, who explored tactile maps using only their right index finger and all right-hand fingers. The algorithm identified the index fingertip in 99.78 % of one-finger video frames and 97.55 % of five-finger video frames. Although the algorithm produced slightly biased Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands and more dispersed estimates relative to a human coder, these differences (x = 0.08 cm, y = 0.04 cm) and standard deviations (σ x = 0.16 cm, σ y = 0.21 cm) were small compared to the size of a fingertip (1.5-2.0 cm). Some example finger-tracking results are provided where corrections are made using the bias and variance estimates.

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Section: Discussionmentioning

confidence: 99%

Determining the bias and variance of a deterministic finger-tracking algorithm

Morash

Velden

2015

Behav Res

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“…The thorax movements are recorded using an optic camera (4) with three retroreflecting markers (5) while the arm kinematics is acquired with an electromagnetic tracker (6) and 6DoFs markers (7). The shoulder reference frame (8) is grossly aligned with the VR one (9). B.…”

Section: Acquisition Frameworkmentioning

confidence: 99%

“…However, teleoperation represents a complex motor control task, and forces the users to acquire new skills to fully take advantage of this technology [9]- [11]. The human motor control system is characterized by multiple degrees of redundancy (kinematic, kinetic, sensory and neuronal).…”

Section: Introductionmentioning

confidence: 99%

An Uncontrolled Manifold Analysis of Arm Joint Variability in Virtual Planar Position and Orientation Telemanipulation

Buzzi

Momi

Nisky³

2019

IEEE Trans. Biomed. Eng.

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Abstract-Objective: In teleoperated robot-assisted tasks, the user interacts with manipulators to finely control remote tools. Manipulation of robotic devices, characterized by specific kinematic and dynamic proprieties, is a complex task for the human sensorimotor system due to the inherent biomechanical and neuronal redundancies that characterize the human arm and its control. We investigate how master devices with different kinematics structures and how different task constraints influence users capabilities in exploiting arm redundancy. Methods: A virtual teleoperation workbench was designed and the arm kinematics of seven users was acquired during the execution of two planar virtual tasks, involving either the control of position only or position-orientation of a tool. Using the UnControlled Manifold Analysis of arm joint variability we estimated the logarithmic ratio between task irrelevant and the task relevant manifolds (Rv). Results: The Rv values obtained in the position-orientation task were higher than in the position only task while no differences were found between the master devices. A modulation of Rv was found through the execution of the position task and a positive correlation was found between task performance and redundancy exploitation. Conclusion: Users exploited additional portions of arm redundancy when dealing with the tool orientation. The Rv modulation seems influenced by the task constraints and by the users possibility of reconfiguring the arm position. Significance: This work advances the general understanding of the exploitation of arm redundancy in complex tasks, and can improve the development of future robotic devices.

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“…3,4 The benefits of robotics seem more evident where a fine dissection and complex surgical reconstruction are required. 5 Even though movement dynamics of the robotic master manipulators are currently still believed to have room for future improvement, 6 current robotic systems show superior handling and ergonomics compared with CLS techniques. 7,8 A clear advantage of CLS over currentgeneration RS is the presence of haptic feedback in CLS, which is absent in RS.…”

Section: Introductionmentioning

confidence: 98%