Stuart A. Bowyer

Active Constraints/Virtual Fixtures: A Survey

Active constraints, also known as virtual fixtures, are high-level control algorithms which can be used to assist a human in man-machine collaborative manipulation tasks. The active constraint controller monitors the robotic manipulator with respect to the environment and task, and anisotropically regulates the motion to provide assistance. The type of assistance offered by active constraints can vary, but they are typically used to either guide the user along a task-specific pathway or limit the user to within a “safe” region. There are several diverse methods described within the literature for applying active constraints, and these are surveyed within this paper. The active constraint research is described and compared using a simple generalized framework, which consists of three primary processes: 1) constraint definition, 2) constraint evaluation, and 3) constraint enforcement. All relevant research approaches for each of these processes, found using search terms associated to “virtual fixture,” “active constraint” and “motion constraint,” are presented.

Automatic adventitious respiratory sound analysis: A systematic review

Background Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established. Objective To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works. Data sources A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification. Study selection Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated. Data extraction Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved. Data synthesis A total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis. Limitations Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions. Conclusion A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventional non-automatic identification. This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases.

Dynamic frictional constraints in translation and rotation

Active constraints and virtual fixtures are popular control strategies used within human-robot collaborative manipulation tasks, particularly in the field of robot-assisted surgery. Recent research has shown how active constraints, which robotically regulate the motion of a tool that is primarily manipulated by a human, can be implemented in dynamic environments which change and deform throughout a procedure. In a dynamic environment, movement of the constraint boundary can cause active forcing of the surgical tools, potentially reducing the surgeons control and jeopardising patient safety. Dynamic frictional constraints have been proposed as a method for enforcing dynamic active constraints which do not generate energy of their own, and simply dissipate or redirect the energy of the surgeon to provide assistance. In this paper, dynamic frictional constraints are reformulated to allow formal proof that they are indeed dissipative, and hence also passive. This new formulation is then extended such that dynamic frictional constraints can simultaneously constrain the position and orientation of a tool. Experimental results show that the method is of significant benefit in performing a dynamic task when compared to cases without any assistance; with position and orientation constraints individually and with a conventional frictional constraint without energy redirection.

Dissipative Control for Physical Human–Robot Interaction

Physical human-robot interaction is fundamental to exploiting the capabilities of robots in tasks and environments where robots have limited cognition or comprehension and is virtually ubiquitous for robotic manipulation in highly unstructured environments, as are found in surgery. A critical aspect of physical human-robot interaction in these cases is controlling the robot so that the individual human and robot competencies are maximized, while guaranteeing user, task, and environment safety. Dissipative control precludes dangerous forcing of a shared tool by the robot, ensuring safety; however, it typically suffers from poor control fidelity, resulting in reduced task accuracy. In this study, a novel, rigorously formalized, n-dimensional dissipative control strategy is proposed that employs a new technique called “energy redirection” to generate control forces with increased fidelity while remaining dissipative and safe. Experimental validation of the method, for complete pose control, shows that it achieves a 90% reduction in task error compared with the current state of the art in dissipative control for the tested applications. The findings clearly demonstrate that the method significantly increases the fidelity and efficacy of dissipative control during physical human-robot interaction. This advancement expands the number of tasks and environments into which safe physical human-robot interaction can be employed effectively.

Dynamic frictional constraints for robot assisted surgery

Collaborative, as opposed to autonomous, control strategies are used within the majority of commercially available, surgical robotic systems. Amongst these, active constraints and virtual fixtures, where assistance is in the form of regulation applied to the motion of surgical tools, offer an effective means to maximise both user and robot capabilities. Conventional active constraint approaches, however, are likely to result in active forcing of the tools when used within a dynamically changing surgical environment. It is posited that such behaviour inherently reduces a surgeons control over the procedure, and therefore compromises patient safety and clinical acceptance. Utilising a friction model to enforce constraints ensures that energy is never introduced into the system; however frictional constraints suffer from problems once penetration of a constrained region has occurred. A frictional constraint formulation is proposed which eliminates this by redirecting a users motion, guiding him towards the surface. Experimental validation shows that the proposed constraint significantly improves a users path-following performance over unassisted cases, while approaching the performance benchmark of a viscoelastic active constraint.

Mass and Friction Optimization for Natural Motion in Hands-On Robotic Surgery

In hands-on robotic surgery, the surgical tool is mounted on the end-effector of a robot and is directly manipulated by the surgeon. This simultaneously exploits the strengths of both humans and robots, such that the surgeon directly feels tool-tissue interactions and remains in control of the procedure, while taking advantage of the robots higher precision and accuracy. A crucial challenge in hands-on robotics for delicate manipulation tasks, such as surgery, is that the user must interact with the dynamics of the robot at the end-effector, which can reduce dexterity and increase fatigue. This paper presents a null-space-based optimization technique for simultaneously minimizing the mass and friction of the robot that is experienced by the surgeon. By defining a novel optimization technique for minimizing the projection of the joint friction onto the end-effector, and integrating this with our previous techniques for minimizing the belted mass/inertia as perceived by the hand, a significant reduction in dynamics felt by the user is achieved. Experimental analyses in both simulation and human user trials demonstrate that the presented method can reduce the user-experienced dynamic mass and friction by, on average, 44% and 41%, respectively. The results presented robustly demonstrate that optimizing a robots pose can result in a more natural tool motion, potentially allowing future surgical robots to operate with increased usability, improved surgical outcomes, and wider clinical uptake.

Deformation invariant bounding spheres for dynamic active constraints in surgery

Active constraints are collaborative robot control strategies, which can be used to guide a surgeon or protect delicate tissue structures during robot-assisted surgery. Tissue structures of interest often move and deform throughout a surgical intervention, and therefore, dynamic active constraints, which adapt and conform to these changes, are required. A fundamental element of an active constraint controller is the computation of the geometric relationship between the constraint geometry and the surgical instrument. For a static active constraint, there are a variety of computationally efficient methods for computing this relative configuration; however, for a dynamic active constraint, it becomes significantly more challenging. Deformation invariant bounding spheres are a novel bounding volume formulation, which can be used within a hierarchy to allow efficient proximity queries within dynamic active constraints. These bounding spheres are constructed in such a way that as the surface deforms, they do not require time-consuming rebuilds or updates, rather they are implicitly updated and continue to represent the underlying geometry as it changes. Experimental results show that performing proximity queries with deformation invariant bounding sphere hierarchies is faster than common methods from the literature when the deformation rate is within the range expected from conventional imaging systems.

TaiNi: Maximizing research output whilst improving animals' welfare in neurophysiology experiments

Understanding brain function at the cell and circuit level requires representation of neuronal activity through multiple recording sites and at high sampling rates. Traditional tethered recording systems restrict movement and limit the environments suitable for testing, while existing wireless technology is still too heavy for extended recording in mice. Here we tested TaiNi, a novel ultra-lightweight (<2 g) low power wireless system allowing 72-hours of recording from 16 channels sampled at ~19.5 KHz (9.7 KHz bandwidth). We captured local field potentials and action-potentials while mice engaged in unrestricted behaviour in a variety of environments and while performing tasks. Data was synchronized to behaviour with sub-second precision. Comparisons with a state-of-the-art wireless system demonstrated a significant improvement in behaviour owing to reduced weight. Parallel recordings with a tethered system revealed similar spike detection and clustering. TaiNi represents a significant advance in both animal welfare in electrophysiological experiments, and the scope for continuously recording large amounts of data from small animals.

A hybrid constraint-penalty proxy method for six degree-of-freedom haptic display of deforming objects

There are many applications and tasks in which the precise, high-fidelity haptic display of deforming objects is required. A crucial element in haptic rendering is the definition of a proxy pose that follows the motion of the user, while respecting the geometry of the object being displayed. Conventional methods for computing the dynamics of a proxy interacting with a deforming object suffer from several issues relating to numerical instabilities when the proxy becomes over-constrained and high computational demands. This paper presents a novel hybrid proxy that combines modified versions of constraint-based and penalty-based proxies together to give high fidelity rendering with reduced computational requirements and enhanced robustness to situations where the proxy becomes enclosed. Experimental analysis of the proposed method shows that it can efficiently compute proxy dynamics that faithfully render the required object. This research forms a basis for further development of novel hybrid dynamic proxies for haptics and allows for increasingly complex deforming geometries to be rendered.

Low-complexity prediction of frequency-rich biosignals for lossless compression in wearable technologies

Wearable technologies that store, monitor and analyse a range of biosignals are an area of significant growth and interest for both industry and academia. The rate of data generation in these devices poses a considerable challenge with regards to the bandwidths of wireless transmission protocols, local storage capacities and the on-board power consumption requirements. This issue is particularly acute for frequency-rich biosignals containing significant higher frequency components that are un-served by conventional compression techniques. This paper proposes a low-complexity predictor, based on a low-order infinite impulse response bandpass filter, to accurately predict such biosignals for use in lossless compression. Experimental evaluation of the method demonstrates that it outperforms conventional predictors with an average 25 % reduction in predictor residual standard deviation. The predictor described here enables high-bandwidth wearable sensors that can be employed in systems with reduced power consumption for transmission, storage and compression leading to considerable improvements in user experience by reducing device mass and increasing battery life.