Cricothyroidotomy (CCT) is a critical, life-saving procedure requiring the identification of key neck landmarks through palpation. Interactive virtual simulation offers a promising, cost-effective approach to CCT training with high visual realism. However, developing the palpation skills necessary for CCT requires a haptic interface with tactile sensitivity comparable to human fingers. Such interfaces are often represented by plastic partial mannequins, which require further adaptation to integrate into virtual environments. This study introduces an instrumented physical palpation interface for CCT, integrated into a virtual surgical simulator, and tested on ten surgeons who practiced the procedure over a training period. Data on haptic interactions collected during the training was analyzed to evaluate participants' palpation skills and explore their force modulation strategies about landmark identification scores. Our findings suggest that trainees become more precise in their exploration over time, apply greater normal forces around target areas. Initial landmark identification performance influences adjustments in the overall applied pressure.

Training Deep neural networks (DNNs) on noisy labeled datasets is a challenging problem, because learning on mislabeled examples deteriorates the performance of the network. As the ground truth availability is limited with real-world noisy datasets, previous papers created synthetic noisy datasets by randomly modifying the labels of training examples of clean datasets. However, no final conclusions can be derived by just using this random noise, since it excludes feature-dependent noise. Thus, it is imperative to generate feature-dependent noisy datasets that additionally provide ground truth. Therefore, we propose an intuitive approach to creating feature-dependent noisy datasets by utilizing the training predictions of DNNs on clean datasets that also retain true label information. We refer to these datasets as "Pseudo Noisy datasets". We conduct several experiments to establish that Pseudo noisy datasets resemble feature-dependent noisy datasets across different conditions. We further randomly generate synthetic noisy datasets with the same noise distribution as that of Pseudo noise (referred as "Randomized Noise") to empirically show that i) learning is easier with feature-dependent label noise compared to random noise, ii) irrespective of noise distribution, Pseudo noisy datasets mimic feature-dependent label noise and iii) current training methods are not generalizable to feature-dependent label noise. Therefore, we believe that Pseudo noisy datasets will be quite helpful to study and develop robust training methods.

Despite the great diversity of teleoperator designs and applications, their underlying control systems have many similarities. These similarities can be exploited to enable inter-operability between heterogeneous systems. We have developed a network data specification, the Interoperable Telerobotics Protocol, that can be used for Internet based control of a wide range of teleoperators. In this work we test interoperable telerobotics on the global Internet, focusing on the telesurgery application domain. Fourteen globally dispersed telerobotic master and slave systems were connected in thirty trials in one twenty four hour period. Users performed common manipulation tasks to demonstrate effective master-slave operation. With twenty eight (93%) successful, unique connections the results show a high potential for standardizing telerobotic operation. Furthermore, new paradigms for telesurgical operation and training are presented, including a networked surgery trainer and upper-limb exoskeleton control of micro-manipulators.

Force-reflecting teleoperators in which the remote environment is kinesthetically coupled to the operator can considerably increase task performance. Wave-variable-based controllers can support the stable operation of force-reflecting teleoperators under arbitrary communication delays. Transparency in such systems is compromised in order to maintain stability. We had previously proposed a modified wave variable controller that implemented additional wave impedance in the wave variable transformations in order to focus more closely on force tracking. In this paper, we present a new controller for bilateral teleoperators based on the modified wave variable control method which provides superior position and force tracking performance compared to the traditional wave-variable-based method. Moreover, the method has high stability. Theoretical investigation and experimental results confirm the performance of this new controller.

In this paper we analyzed the performance of a peer-to-peer haptic collaboration system with two users jointly manipulating an object with mass and damping properties. We used objective measures to compare tuned PD, wave variables and time domain passivity controllers subject to real time delays from the Internet through similar experimental parameters. We set up a packet reflector network at our collaborators' servers in order to able to perform the experiment with subjects located in the same laboratory. Subjects were blinded to which controller was used and received them in a randomized sequence. UDP data packets were used for haptic data communication and the packet transmission rate was maintained at 1000 Hz. Our experimental results show that the tuned PD controller gave the best performance in terms of position error and wave variables in terms of force.

Force-reflecting teleoperators in which the remote environment is kinesthetically coupled to the operator can considerably increase task performance. However, wave-variable-based controllers can support the stable operation of force-reflecting teleoperators under arbitrary communication delays. Transparency in such systems is compromised in order to maintain stability. In this paper, we present a new controller for bilateral teleoperators based on the wave variable control method which provides superior force tracking performance compared to the traditional wave-variable-based method. Additionally, this method also improves the phase delay induced by the latency in the communication network. Both simulation and experimental results confirm the performance of this new controller.

In minimally invasive telesurgical systems, displaying the forces measured at the slave side is an important issue. Latency in communication lines limits the transmission of vivid tactile sensations and drives the system unstable. In this paper, we propose a new model-based approach for the bilateral control of a telesurgical robot using time-domain passivity control. This method consists of a virtual slave model implemented at the master side to estimate the force on the slave side. During the operation, the estimated force from the virtual slave is added to the actual measured force transmitted from the slave in order to maintain the passivity of the system. Both simulation and experimental results confirm the performance of this new controller.