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\"This book provides a comprehensive introduction to human robot interaction, with a focus on safety, standardization, and benchmarking. Featuring contributions from leading experts, the book presents state-of-the-art research and includes real-world applications and use cases. It explores industrial robotics, service robotics, and medical robotics, and features chapters on safety approaches for human robot interaction including physical interactions, collaboration in tasks, work space sharing, human aware motion planning, and existing standards and guidelines\"-- Provided by publisher.

Robot-Assisted Minimally Invasive Surgery (RAMIS) has reshaped the standard clinical practice during the past two decades. Many believe that the next big step in the advancement of RAMIS will be partial autonomy, which may reduce the fatigue and the cognitive load on the surgeon by performing the monotonous, time-consuming subtasks of the surgical procedure autonomously. Although serious research efforts are paid to this area worldwide, standard evaluation methods, metrics, or benchmarking techniques are still not formed. This article aims to fill the void in the research domain of surgical subtask automation by proposing standard methodologies for performance evaluation. For that purpose, a novel characterization model is presented for surgical automation. The current metrics for performance evaluation and comparison are overviewed and analyzed, and a workflow model is presented that can help researchers to identify and apply their choice of metrics. Existing systems and setups that serve or could serve as benchmarks are also introduced and the need for standard benchmarks in the field is articulated. Finally, the matter of Human–Machine Interface (HMI) quality, robustness, and the related legal and ethical issues are presented.

BACKGROUND: Sensor technologies and data collection practices are changing and improving quality metrics across various domains. Surgical skill assessment in Robot-Assisted Minimally Invasive Surgery (RAMIS) is essential for training and quality assurance. The mental workload on the surgeon (such as time criticality, task complexity, distractions) and non-technical surgical skills (including situational awareness, decision making, stress resilience, communication, leadership) may directly influence the clinical outcome of the surgery. METHODS: A literature search in PubMed, Scopus and PsycNet databases was conducted for relevant scientific publications. The standard PRISMA method was followed to filter the search results, including non-technical skill assessment and mental/cognitive load and workload estimation in RAMIS. Publications related to traditional manual Minimally Invasive Surgery were excluded, and also the usability studies on the surgical tools were not assessed. RESULTS: 50 relevant publications were identified for non-technical skill assessment and mental load and workload estimation in the domain of RAMIS. The identified assessment techniques ranged from self-rating questionnaires and expert ratings to autonomous techniques, citing their most important benefits and disadvantages. CONCLUSIONS: Despite the systematic research, only a limited number of articles was found, indicating that non-technical skill and mental load assessment in RAMIS is not a well-studied area. Workload assessment and soft skill measurement do not constitute part of the regular clinical training and practice yet. Meanwhile, the importance of the research domain is clear based on the publicly available surgical error statistics. Questionnaires and expert-rating techniques are widely employed in traditional surgical skill assessment; nevertheless, recent technological development in sensors and Internet of Things-type devices show that skill assessment approaches in RAMIS can be much more profound employing automated solutions. Measurements and especially big data type analysis may introduce more objectivity and transparency to this critical domain as well. SIGNIFICANCE: Non-technical skill assessment and mental load evaluation in Robot-Assisted Minimally Invasive Surgery is not a well-studied area yet; while the importance of this domain from the clinical outcome’s point of view is clearly indicated by the available surgical error statistics.

The current trends in the research and development of self-driving technology aim for Level 3+ autonomy, where the vehicle controls both lateral and longitudinal motions of the dynamic driving task, while the driver is permitted to divert their attention, as long as they are able to react properly to a handover request initiated by the vehicle. At this level of autonomy, situation awareness of the human driver has become one of the most important metrics of safety. This paper presents the results of a user study to evaluate handover performance at Level 3 autonomy. The study investigates whether the level of situation awareness during critical handover situations has a direct impact on task performance, with higher situation awareness expected to lead to better outcomes during emergency interventions. The study is performed in a simulated environment, using the CARLA driving simulator and the master console of the da Vinci Surgical System. The test subjects were asked to answer the questions of a questionnaire during the experiment; the answers for those questions and the measured control signals were analyzed to gain further knowledge on the safety of the handover process.

Objective skill assessment-based personal performance feedback is a vital part of surgical training. Either kinematic—acquired through surgical robotic systems, mounted sensors on tooltips or wearable sensors—or visual input data can be employed to perform objective algorithm-driven skill assessment. Kinematic data have been successfully linked with the expertise of surgeons performing Robot-Assisted Minimally Invasive Surgery (RAMIS) procedures, but for traditional, manual Minimally Invasive Surgery (MIS), they are not readily available as a method. 3D visual features-based evaluation methods tend to outperform 2D methods, but their utility is limited and not suited to MIS training, therefore our proposed solution relies on 2D features. The application of additional sensors potentially enhances the performance of either approach. This paper introduces a general 2D image-based solution that enables the creation and application of surgical skill assessment in any training environment. The 2D features were processed using the feature extraction techniques of a previously published benchmark to assess the attainable accuracy. We relied on the JHU–ISI Gesture and Skill Assessment Working Set dataset—co-developed by the Johns Hopkins University and Intuitive Surgical Inc. Using this well-established set gives us the opportunity to comparatively evaluate different feature extraction techniques. The algorithm reached up to 95.74% accuracy in individual trials. The highest mean accuracy—averaged over five cross-validation trials—for the surgical subtask of Knot-Tying was 83.54%, for Needle-Passing 84.23% and for Suturing 81.58%. The proposed method measured well against the state of the art in 2D visual-based skill assessment, with more than 80% accuracy for all three surgical subtasks available in JIGSAWS (Knot-Tying, Suturing and Needle-Passing). By introducing new visual features—such as image-based orientation and image-based collision detection—or, from the evaluation side, utilising other Support Vector Machine kernel methods, tuning the hyperparameters or using other classification methods (e.g., the boosted trees algorithm) instead, classification accuracy can be further improved. We showed the potential use of optical flow as an input for RAMIS skill assessment, highlighting the maximum accuracy achievable with these data by evaluating it with an established skill assessment benchmark, by evaluating its methods independently. The highest performing method, the Residual Neural Network, reached means of 81.89%, 84.23% and 83.54% accuracy for the skills of Suturing, Needle-Passing and Knot-Tying, respectively.

Strokes are one of the leading causes of adult disability. There are a wide range of therapies available in stroke care for people with stroke, but there can be wide variations in the effectiveness of these therapies, so it is essential to review and compare them from time to time. In our study, we measured and compared the effectiveness of three high-intensity therapies: an agility training program without technological tools, a virtual reality exergaming training program with a low-cost device, and a high-cost robotic training program using augmented and virtual reality. All three therapies helped to improve the patients' functional abilities, balance, and gait. On average, endurance increased by 104-177%, balance scores by 36-53%, and gait speed by 5-10% depending on the intervention. Robotic therapy and exergaming facilitate greater improvements in walking speed, step length, and balance-related gait metrics. These findings have profound implications for stroke rehabilitation, advocating for the prioritization of robotic and exergaming interventions over conventional functional therapies, like agility training. Given the limited sample size, the results should be interpreted as preliminary, highlighting the need for further studies with larger cohorts.

Robot-Assisted Minimally Invasive Surgery (RAMIS) marks a paradigm shift in surgical procedures, enhancing precision and ergonomics. Concurrently it introduces complex stress dynamics and ergonomic challenges regarding the human–robot interface and interaction. This study explores the stress-related aspects of RAMIS, using the da Vinci XI Surgical System and the Sea Spikes model as a standard skill training phantom to establish a link between technological advancement and human factors in RAMIS environments. By employing different physiological and kinematic sensors for heart rate variability, hand movement tracking, and posture analysis, this research aims to develop a framework for quantifying the stress and ergonomic loads applied to surgeons. Preliminary findings reveal significant correlations between stress levels and several of the skill-related metrics measured by external sensors or the SURG-TLX questionnaire. Furthermore, early analysis of this preliminary dataset suggests the potential benefits of applying machine learning for surgeon skill classification and stress analysis. This paper presents the initial findings, identified correlations, and the lessons learned from the clinical setup, aiming to lay down the cornerstones for wider studies in the fields of clinical situation awareness and attention computing.

This article presents a tuned control algorithm for the speed and course of a four-wheeled automobile-type robot as a single nonlinear object, developed by the analytical approach of compensation for the object’s dynamics and additive effects. The method is based on assessment of external effects and as a result new, advanced feedback features may appear in the control system. This approach ensures automatic movement of the object with accuracy up to a given reference filter, which is important for stable and accurate control under various conditions. In the process of the synthesis control algorithm, an inverse mathematical model of the robot was built, and reference filters were developed for a closed-loop control system through external effect channels, providing the possibility of physical implementation of the control algorithm and compensation of external effects through feedback. This combined approach allows us to take into account various effects on the robot and ensure its stable control. The developed algorithm provides control of the robot both when moving forward and backward, which expands the capabilities of maneuvering and planning motion trajectories and is especially important for robots working in confined spaces or requiring precise movement into various directions. The efficiency of the algorithm is demonstrated using a computer simulation of a closed-loop control system under various external effects. It is planned to further develop a digital algorithm for implementation on an onboard microcontroller, in order to use the new algorithm in the overall motion control system of a four-wheeled mobile robot.

This paper presents a new method of path planning for an industrial robot manipulator that performs thermal plasma spraying of coatings. Path planning and automatic generation of the manipulator motion program are performed using preliminary 3D surface scanning data from a laser triangulation distance sensor installed on the same robot arm. The new path planning algorithm is based on constructing a function of the geodesic distance from the starting curve. A new method for constructing a geodesic distance function on a surface is proposed, based on the application of Discrete Exterior calculus methods, which is characterized by a high computational efficiency. The developed algorithms and their software implementation were experimentally tested with the robotic microplasma spraying of a protective coating on the surface of a jaw crusher plate, which was then successfully operated for crushing mineral-based raw materials.

Background: This article provides a scoping review on the current status of Image-Guided Navigation with various forms of digital technologies, including Extended Reality, Augmented Reality Head-Mounted Displays (AR–HMDs) and Robot-Assisted Surgery (RAS) for Pedicle Screw Placement in orthopedics and spine surgery. Methods: A scoping literature review was performed in the PubMed, Scopus, Embase, Web of Science, Google Scholar and IEEE Xplore databases to collect clinical and user satisfaction data on AR–HMDs and compare those with RAS outcomes. In vivo patient, cadaver and phantom trial accuracy data reports were identified and grouped through the analysis. Over the past two years, 14 publications were retrieved and analyzed. Pedicle screw placement accuracy was described with Linear Tip Error (LTE), Angular Trajectory Error (ATE) and Gertzbein–Robbins Scale (GRS) outcomes. Results: The Pedicle Screw Placement accuracy was seen to increase in the in vivo, cadaver and phantom model groups using AR-HMD compared to the Free-Hand insertion technique. User experience and satisfaction data were limited; however, a clear advantage for the operative results was described when it was added. RAS screwing showed similar accuracy outcomes. The need for benchmarking and quantified situation awareness for AR–HMDs is recognizable. The authors present a method for standardized scoring and visualization of surgical navigation technologies, based on measurements of the surgeon (as the end-users) user satisfaction, clinical accuracy and operation time. Conclusions: computer-technology driven support for spine surgery is well-established and efficient for certain procedures. As a more affordable option next to RAS, AR–HMD navigation has reached technological readiness for surgical use. Ergonomics and usability improvements are needed to match the potential of RAS/XR in human surgeries.