Objective: This work presents a device for non-invasive wound parameters assessment, designed to overcome the drawbacks of traditional methods, which are mostly rough, inaccurate, and painful for the patient. The device estimates the morphological parameters of the wound and provides augmented reality (AR) visual feedback on the wound healing status by projecting the wound border acquired during the last examination, thus improving doctor-patient communication. Methods: An accurate 3D model of the wound is created by stereophotogrammetry and refined through self-organizing maps. The 3D model is used to estimate physical parameters for wound healing assessment and integrates AR functionalities based on a miniaturized projector. The physical parameter estimation functionalities are evaluated in terms of precision, accuracy, inter-operator variability, and repeatability, whereas AR wound border projection is evaluated in terms of accuracy on the same phantom. Results: The accuracy and precision of the device are respectively 2% and 1.2% for linear parameters, and 1.7% and 1.3% for area and volume. The AR projection shows an error distance <1 mm. No statistical difference was found between the measurements of different operators. Conclusion: The device has proven to be an objective and non-operator-dependent tool for assessing the morphological parameters of the wound. Comparison with non-contact devices shows improved accuracy, offering reliable and rigorous measurements. Clinical Impact: Chronic wounds represent a significant health problem with high recurrence rates due to the ageing of the population and diseases such as diabetes and obesity. The device presented in this work provides an easy-to-use non-invasive tool to obtain useful information for treatment.

In recent years, the spreading of visual augmented reality as an effective tool in image-guided surgery, has stimulated the research community to investigate the use of commercial augmented reality headsets a broad range of potential applications. This aroused enthusiasm among clinicians for the potential of augmented reality, but also revealed some technological and human-factor limitations that still hinder its routine adoption in the operating room. In this work, we propose an alternative to head-mounted displays, based on projected augmented reality. Projected augmented reality completely preserves the surgeon’s natural view of the operating field, because it requires no perspective conversion and/or optical mediation. We selected a cranio-maxillofacial surgery application as a benchmark to test the proposed system and compare its accuracy with the one obtained with a video see-through system. The augmented reality overlay accuracy was evaluated by measuring the distance between a virtual osteotomy line and its real counterpart. The experimental tests showed that the accuracy of the two augmented reality modes is similar, with a median error discrepancy of about 0.3 mm for the projected augmented reality mode. Results suggest that projected augmented reality can be a valuable alternative to standard see-through head-mounted displays to support in-situ visualization of medical imaging data as surgical guidance.

The increasing capability of computing power and mobile graphics has made possible the release of self-contained augmented reality (AR) headsets featuring efficient head-anchored tracking solutions. Ego motion estimation based on well-established infrared tracking of markers ensures sufficient accuracy and robustness. Unfortunately, wearable visible-light stereo cameras with short baseline and operating under uncontrolled lighting conditions suffer from tracking failures and ambiguities in pose estimation. To improve the accuracy of optical self-tracking and its resiliency to marker occlusions, degraded camera calibrations, and inconsistent lighting, in this work we propose a sensor fusion approach based on Kalman filtering that integrates optical tracking data with inertial tracking data when computing motion correlation. In order to measure improvements in AR overlay accuracy, experiments are performed with a custom-made AR headset designed for supporting complex manual tasks performed under direct vision. Experimental results show that the proposed solution improves the head-mounted display (HMD) tracking accuracy by one third and improves the robustness by also capturing the orientation of the target scene when some of the markers are occluded and when the optical tracking yields unstable and/or ambiguous results due to the limitations of using head-anchored stereo tracking cameras under uncontrollable lighting conditions.

Aortic valve surgery is the preferred procedure for replacing a damaged valve with an artificial one. The ValveTech robotic platform comprises a flexible articulated manipulator and surgical interface supporting the effective delivery of an artificial valve by teleoperation and endoscopic vision. This article presents our recent work on force-perceptive, safe, semiautonomous navigation of the ValveTech platform prior to valve implantation. First, we present a force observer that transfers forces from the manipulator body and tip to a haptic interface. Second, we demonstrate how hybrid forward/inverse mechanics, together with endoscopic visual servoing, lead to autonomous valve positioning. Benchtop experiments and an artificial phantom quantify the performance of the developed robot controller and navigator. Valves can be autonomously delivered with a 2.0±0.5 mm position error and a minimal misalignment of 3.4±0.9°. The hybrid force/shape observer (FSO) algorithm was able to predict distributed external forces on the articulated manipulator body with an average error of 0.09 N. FSO can also estimate loads on the tip with an average accuracy of 3.3%. The presented system can lead to better patient care, delivery outcome, and surgeon comfort during aortic valve surgery, without requiring sensorization of the robot tip, and therefore obviating miniaturization constraints.

Research studies show that serious games can increase patient motivation regardless of age or illness and be an affordable and promising solution with respect to conventional physiotherapy. In this paper, we present the latest evolution of our system for shoulder rehabilitation based on hand-finger tracking and projected augmented reality. This version integrates metrics to assess patient performance, monitors the game progress, and allows the selection of the game visualization mode (standard on-screen or projected augmented reality). Additionally, the new software tracks the velocity, acceleration, and normalized jerk of the arm-hand movements of the user. Specifically, sixteen healthy volunteers (eight technical and eight rehabilitation experts) tested our current prototype. The results showed that the serious game is engaging, its design is ergonomically sound, and the overall system could be a useful tool in shoulder rehabilitation. However, clinical validation is needed to assess that the serious game has the same effects as the selected therapy. This is the preliminary step toward laying the foundation for future studies that investigate abnormalities in shoulder movements by using hand-finger tracking.