Spherical Wrist Manipulator Local Planner for Redundant Tasks in Collaborative Environments.

Standard industrial robotic manipulators use well-established high performing technologies. However, such manipulators do not guarantee a safe Human-Robot Interaction (HRI), limiting their usage in industrial and medical applications. This paper proposes a novel local path planner for spherical wrist manipulators to control the execution of tasks where the manipulator number of joints is redundant. Such redundancy is used to optimize robot motion and dexterity. We present an intuitive parametrization of the end-effector (EE) angular motion, which decouples the rotation of the third joint of the wrist from the rest of the angular motions. Manipulator EE motion is controlled through a decentralized linear system with closed-loop architecture. The local planner integrates a novel collision avoidance strategy based on a potential repulsive vector applied to the EE. Contrary to classic potential field approaches, the collision avoidance algorithm considers the entire manipulator surface, enhancing human safety. The local path planner is simulated in three generic scenarios: (i) following a periodic reference, (ii) a random sequence of step signal references, and (iii) avoiding instantly introduced obstacles. Time and frequency domain analysis demonstrated that the developed planner, aside from better parametrizing redundant tasks, is capable of successfully executing the simulated paths (max error = 0.25°) and avoiding obstacles.

Endoluminal Procedures and Devices for Esophageal Tract Investigation: A Critical Review.

Diseases of the esophageal tract represent a heterogeneous class of pathological conditions for which diagnostic paradigms continue to emerge. In the last few decades, innovative diagnostic devices have been developed, and several attempts have been made to advance and standardize diagnostic algorithms to be compliant with medical procedures. To the best of our knowledge, a comprehensive review of the procedures and available technologies to investigate the esophageal tract was missing in the literature. Therefore, the proposed review aims to provide a comprehensive analysis of available endoluminal technologies and procedures to investigate esophagus health conditions. The proposed systematic review was performed using PubMed, Scopus, and Web of Science databases. Studies have been divided into categories based on the type of evaluation and measurement that the investigated technology provides. In detail, three main categories have been identified, i.e., endoluminal technologies for the (i) morphological, (ii) bio-mechanical, and (iii) electro-chemical evaluation of the esophagus.

A Novel Capacitive Measurement Device for Longitudinal Monitoring of Bone Fracture Healing.

The healing process of surgically-stabilised long bone fractures depends on two main factors: (a) the assessment of implant stability, and (b) the knowledge of bone callus stiffness. Currently, X-rays are the main diagnostic tool used for the assessment of bone fractures. However, they are considered unsafe, and the interpretation of the clinical results is highly subjective, depending on the clinician's experience. Hence, there is the need for objective, non-invasive and repeatable methods to allow a longitudinal assessment of implant stability and bone callus stiffness. In this work, we propose a compact and scalable system, based on capacitive sensor technology, able to measure, quantitatively, the relative pins displacements in bone fractures treated with external fixators. The measurement device proved to be easily integrable with the external fixator pins. Smart arrangements of the sensor units were exploited to discriminate relative movements of the external pins in the 3D space with a resolution of 0.5 mm and 0.5°. The proposed capacitive technology was able to detect all of the expected movements of the external pins in the 3D space, providing information on implant stability and bone callus stiffness.

Endoluminal Motion Recognition of a Magnetically-Guided Capsule Endoscope Based on Capsule-Tissue Interaction Force.

A magnetically-guided capsule endoscope, embedding flexible force sensors, is designed to measure the capsule-tissue interaction force. The flexible force sensor is composed of eight force-sensitive elements surrounding the internal permanent magnet (IPM). The control of interaction force acting on the intestinal wall can reduce patient's discomfort and maintain the magnetic coupling between the external permanent magnet (EPM) and the IPM during capsule navigation. A flexible force sensor can achieve this control. In particular, by analyzing the signals of the force sensitive elements, we propose a method to recognize the status of the motion of the magnetic capsule, and provide corresponding formulas to evaluate whether the magnetic capsule follows the motion of the external driving magnet. Accuracy of the motion recognition in Ex Vivo tests reached 94% when the EPM was translated along the longitudinal axis. In addition, a method is proposed to realign the EPM and the IPM before the loss of their magnetic coupling. Its translational error, rotational error, and runtime are 7.04 ± 0.71 mm, 3.13 ± 0.47∘, and 11.4 ± 0.39 s, respectively. Finally, a control strategy is proposed to prevent the magnetic capsule endoscope from losing control during the magnetically-guided capsule colonoscopy.

Photometric Stereo-Based Depth Map Reconstruction for Monocular Capsule Endoscopy.

The capsule endoscopy robot can only use monocular vision due to the dimensional limit. To improve the depth perception of the monocular capsule endoscopy robot, this paper proposes a photometric stereo-based depth map reconstruction method. First, based on the characteristics of the capsule endoscopy robot system, a photometric stereo framework is established. Then, by combining the specular property and Lambertian property of the object surface, the depth of the specular highlight point is estimated, and the depth map of the whole object surface is reconstructed by a forward upwind scheme. To evaluate the precision of the depth estimation of the specular highlight region and the depth map reconstruction of the object surface, simulations and experiments are implemented with synthetic images and pig colon tissue, respectively. The results of the simulations and experiments show that the proposed method provides good precision for depth map reconstruction in monocular capsule endoscopy.

Visual-Based Defect Detection and Classification Approaches for Industrial Applications-A SURVEY.

This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

A Mechatronic Platform for Computer Aided Detection of Nodules in Anatomopathological Analyses via Stiffness and Ultrasound Measurements.

This study presents a platform for ex-vivo detection of cancer nodules, addressing automation of medical diagnoses in surgery and associated histological analyses. The proposed approach takes advantage of the property of cancer to alter the mechanical and acoustical properties of tissues, because of changes in stiffness and density. A force sensor and an ultrasound probe were combined to detect such alterations during force-regulated indentations. To explore the specimens, regardless of their orientation and shape, a scanned area of the test sample was defined using shape recognition applying optical background subtraction to the images captured by a camera. The motorized platform was validated using seven phantom tissues, simulating the mechanical and acoustical properties of ex-vivo diseased tissues, including stiffer nodules that can be encountered in pathological conditions during histological analyses. Results demonstrated the platform's ability to automatically explore and identify the inclusions in the phantom. Overall, the system was able to correctly identify up to 90.3% of the inclusions by means of stiffness in combination with ultrasound measurements, paving pathways towards robotic palpation during intraoperative examinations.

Soft Robotic Manipulator for Improving Dexterity in Minimally Invasive Surgery.

BACKGROUND: Combining the strengths of surgical robotics and minimally invasive surgery (MIS) holds the potential to revolutionize surgical interventions. The MIS advantages for the patients are obvious, but the use of instrumentation suitable for MIS often translates in limiting the surgeon capabilities (eg, reduction of dexterity and maneuverability and demanding navigation around organs). To overcome these shortcomings, the application of soft robotics technologies and approaches can be beneficial. The use of devices based on soft materials is already demonstrating several advantages in all the exploitation areas where dexterity and safe interaction are needed. In this article, the authors demonstrate that soft robotics can be synergistically used with traditional rigid tools to improve the robotic system capabilities and without affecting the usability of the robotic platform. MATERIALS AND METHODS: A bioinspired soft manipulator equipped with a miniaturized camera has been integrated with the Endoscopic Camera Manipulator arm of the da Vinci Research Kit both from hardware and software viewpoints. Usability of the integrated system has been evaluated with nonexpert users through a standard protocol to highlight difficulties in controlling the soft manipulator. RESULTS AND CONCLUSION: This is the first time that an endoscopic tool based on soft materials has been integrated into a surgical robot. The soft endoscopic camera can be easily operated through the da Vinci Research Kit master console, thus increasing the workspace and the dexterity, and without limiting intuitive and friendly use.

A novel magnetic-driven tissue retraction device for minimally invasive surgery.

INTRODUCTION: The purpose of this work is to design and validate an innovative magnetic-based device for tissue retraction for minimally invasive surgery. MATERIAL AND METHODS: An intra-abdominal magnetic module is coupled with an extracorporeal system for establishing a stable attraction, and consequently a reliable tissue retraction. Once the retractor has been inserted into the abdomen, tissue retraction is not constrained by a fixed access port, thus guaranteeing a more flexible, safer and less invasive operation. The intra-abdominal unit is composed of an axial permanent magnet attached to a stainless-steel non-magnetic alligator clip by a traditional suturing thread. A miniaturized mechanism to adjust the length of the suturing thread for lengthening or shortening the distance between the tissue grasper and the internal magnetic unit is included. A multiphysics approach assured a dedicated design that thoroughly fulfills anatomical, physiological and engineering constraints. RESULTS: System functionalities were demonstrated both in in-vitro and ex-vivo conditions, reaching good results and promising outcomes in terms of effectiveness and maneuverability. The retractor was successfully tested in an animal model, carrying out a whole retraction procedure. CONCLUSION: The proposed retraction system resulted to be intuitive, reliable, robust and easy to use, representing a suitable device for MIS procedures.

MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: a review on research activities in Italy.

Over the past few decades the increased level of public awareness concerning healthcare, physical activities, safety and environmental sensing has created an emerging need for smart sensor technologies and monitoring devices able to sense, classify, and provide feedbacks to users' health status and physical activities, as well as to evaluate environmental and safety conditions in a pervasive, accurate and reliable fashion. Monitoring and precisely quantifying users' physical activity with inertial measurement unit-based devices, for instance, has also proven to be important in health management of patients affected by chronic diseases, e.g., Parkinson's disease, many of which are becoming highly prevalent in Italy and in the Western world. This review paper will focus on MEMS sensor technologies developed in Italy in the last three years describing research achievements for healthcare and physical activity, safety and environmental sensing, in addition to smart systems integration. Innovative and smart integrated solutions for sensing devices, pursued and implemented in Italian research centres, will be highlighted, together with specific applications of such technologies. Finally, the paper will depict the future perspective of sensor technologies and corresponding exploitation opportunities, again with a specific focus on Italy.

Electromagnetic Control System for Capsule Navigation: Novel Concept for Magnetic Capsule Maneuvering and Preliminary Study.

The gastrointestinal tract is home of some of the most deadly human diseases. The main problems are related to the difficulty of accessing it for diagnosis or intervention and concomitant patient discomfort. The flexible endoscopy technique has established itself in medical practice due to its high diagnostic accuracy and reliability; however, several technical limitations still remain and the procedure is poorly tolerated by patients. The use of magnetic fields to control and steer endoscopic capsules is increasing in minimally invasive procedures. In fact, magnetic coupling is one of the few physical phenomena capable of transmitting motion beyond a physical barrier, allowing for the compact design of the device itself. In this framework, the authors present the preliminary design and assessment of a magnetic coupling for magnetic endoscopic capsules considering an electromagnetic approach. In particular, a novel toroidal electromagnet is proposed as the control and driving system. The system concept, design, and preliminary results are reported.

A sensorized Nuss bar for patient-specific treatment of Pectus Excavatum.

Pectus Excavatum is an anatomical deformation characterized by a depression of the anterior chest wall. Nuss technique (representing the current clinical golden standard) consists in the introduction of a corrective metal bar, in order to raise the sternum in its anatomic natural position. Nowadays, the bar plays purely a mechanical/corrective action and is kept implanted for about three years, supporting up to a maximum force of 250 N. Our study aims at optimizing the procedure of correction, in terms of monitoring the bar effect, minimizing the body response, and facilitating the bar removal. The sensorized Nuss bar prototype inserted in a platform for telemedicine described in this article is able to monitor in vitro pressure data variations, with more than 150 discrete measurements during the operating period. This behavior is promising for future clinical applications, in which the device could be exploited to monitor the forces at work, thus, providing a customized therapeutic protocol, which in turn may optimize the period of implant. The sensorized bar was also provided with a polymeric coating, able to influence human dermal fibroblast behavior in vitro. This highlights the possibility to minimize, in future in vivo applications, tissue fibrotic responses.

Searching for the perfect wave: the effect of radiofrequency electromagnetic fields on cells.

There is a growing concern in the population about the effects that environmental exposure to any source of "uncontrolled" radiation may have on public health. Anxiety arises from the controversial knowledge about the effect of electromagnetic field (EMF) exposure to cells and organisms but most of all concerning the possible causal relation to human diseases. Here we reviewed those in vitro and in vivo and epidemiological works that gave a new insight about the effect of radio frequency (RF) exposure, relating to intracellular molecular pathways that lead to biological and functional outcomes. It appears that a thorough application of standardized protocols is the key to reliable data acquisition and interpretation that could contribute a clearer picture for scientists and lay public. Moreover, specific tuning of experimental and clinical RF exposure might lead to beneficial health effects.

A Framework for the Evaluation of Human Machine Interfaces of Robot-Assisted Colonoscopy.

The Human Machine Interface (HMI) of intraluminal robots has a crucial impact on the clinician's performance. It increases or decreases the difficulty of the tasks, and is connected to the users' physical and mental stress. OBJECTIVE: This article presents a framework to compare and evaluate different HMIs for robotic colonoscopy, with the objective of identifying the optimal HMI that minimises the clinician's effort and maximises the clinical outcomes. METHODS: The framework comprises a 1) a virtual simulator (clinically validated), 2) wearable sensors measuring the cognitive load, 3) a data collection unit of metrics correlated to the clinical performance, and 4) questionnaires exploring the users' impressions and perceived stress. The framework was tested with 42 clinicians investigating the optimal device for tele-operated control of robotic colonoscopes. Two control devices were selected and compared: a haptic serial-kinematic device and a standard videogame joypad. RESULTS: The haptic device was preferred by the endoscopists, but the joypad enabled better clinical performance and reduced cognitive and physical load. CONCLUSION: The framework can be used to evaluate different aspects of a HMI, both hardware and software, and determine the optimal HMI that can reduce the burden on clinicians while improving the clinical outcome. SIGNIFICANCE: The findings of this study, and of future studies performed with this framework, can inform the design and development of HMIs for intraluminal robots, leading to improved clinical performance, reduced physical and mental stress for clinicians, and ultimately better patient outcomes.

From P4 medicine to P5 medicine: transitional times for a more human-centric approach to AI-based tools for hospitals of tomorrow.

Within the debate on shaping future clinical services, where different robotics and artificial intelligence (AI) based technologies are integrated to perform tasks, the authors take the chance to provide an interdisciplinary analysis required to validate a tool aiming at supporting the melanoma cancer diagnosis. In particular, they focus on the ethical-legal and technical requirements needed to address the Assessment List on Trustworthy AI (ALTAI), highlighting some pros and cons of the adopted self-assessment checklist. The dialogue stimulates additionally remarks on the EU regulatory initiatives on AI in the healthcare systems.

Decision-Making Algorithm and Predictive Model to Assess the Impact of Infectious Disease Epidemics on the Healthcare System: The COVID-19 Case Study in Italy.

To improve decision-making strategies and prediction based on epidemiological data, so far biased by highly-variable criteria, algorithms using unbiased morbidity parameters, i.e. Intensive Care Units (ICU) and Ordinary Hospitalizations (OH), are proposed. ICU/OH acceleration and velocities are mathematically modeled using available and official data to derive two thresholds, alerting on 30 % ICU and 40 % OH of COVID-19 daily occupancy settled by the Italian Minister of Health, as a case of study. A predictive model is also proposed to estimate the daily occupancy of ICU and OH in hospitals for each region, using a Susceptible-Infected-Recovered-Death (SIRD) epidemic model to further extend occupancy prediction in each regional district. Computed data validated the proposed models in Italy after almost two years of pandemic, obtaining agreements with the Italian Presidential Decree regardless of the different regional trends of epidemic waves. Therefore, the decision-making algorithm and prediction model resulted valuable tools, retrospectively, to be tested prospectively in sustainable strategies to curb the impact of COVID-19, or of any other pandemic threats with any aggregate of data, on local healthcare systems.

Flipping food during grilling tasks, a dataset of utensils kinematics and dynamics, food pose and subject gaze.

This paper presents a multivariate dataset of 2866 food flipping movements, performed by 4 chefs and 5 home cooks, with different grilled food and two utensils (spatula and tweezers). The 3D trajectories of strategic points in the utensils were tracked using optoelectronic motion capture. The pinching force of the tweezers, the bending force and torsion torque of the spatula were also recorded, as well as videos and the subject gaze. These data were collected using a custom experimental setup that allowed the execution of flipping movements with freshly cooked food, without having the sensors near the dangerous cooking area. Complementary, the 2D position of food was computed from the videos. The action of flipping food is, indeed, gaining the attention of both researchers and manufacturers of foodservice technology. The reported dataset contains valuable measurements (1) to characterize and model flipping movements as performed by humans, (2) to develop bio-inspired methods to control a cooking robot, or (3) to study new algorithms for human actions recognition.

Sleep Deprivation-Induced Changes in Baseline Brain Activity and Vigilant Attention Performance.

Sleep deprivation (SD) negatively affects several aspects of cognitive performance, and one of the most widely-used tools to evaluate these effects is the Psychomotor Vigilance Test (PVT). The present study investigated the possibility of predicting changes induced by SD in vigilant attention performance by evaluating the baseline electroencephalographic (EEG) activity immediately preceding the PVT stimuli onset. All participants (n = 10) underwent EEG recordings during 10 min of PVT before and after a night of SD. For each participant, the root mean square (RMS) of the baseline EEG signal was evaluated for each 1 s time window, and the respective average value was computed. After SD, participants showed slower (and less accurate) performance in the PVT task. Moreover, a close relationship between the changes in the baseline activity with those in cognitive performance was identified at several electrodes (Fp2, F7, F8, P3, T6, O1, Oz, O2), with the highest predictive power at the occipital derivations. These results indicate that vigilant attention impairments induced by SD can be predicted by the pre-stimulus baseline activity changes.

Comparative Analysis of Diagnostic Techniques for Melanoma Detection: A Systematic Review of Diagnostic Test Accuracy Studies and Meta-Analysis.

Melanoma has the highest mortality rate among skin cancers, and early-diagnosis is essential to maximize survival rate. The current procedure for melanoma diagnosis is based on dermoscopy, i.e., a qualitative visual inspection of lesions with intrinsic limited diagnostic reliability and reproducibility. Other non-invasive diagnostic techniques may represent valuable solutions to retrieve additional objective information of a lesion. This review aims to compare the diagnostic performance of non-invasive techniques, alternative to dermoscopy, for melanoma detection in clinical settings. A systematic review of the available literature was performed using PubMed, Scopus and Google scholar databases (2010-September 2020). All human, in-vivo, non-invasive studies using techniques, alternative to dermoscopy, for melanoma diagnosis were included with no restriction on the recruited population. The reference standard was histology but dermoscopy was accepted only in case of benign lesions. Attributes of the analyzed studies were compared, and the quality was evaluated using CASP Checklist. For studies in which the investigated technique was implemented as a diagnostic tool (DTA studies), the QUADAS-2 tool was applied. For DTA studies that implemented a melanoma vs. other skin lesions classification task, a meta-analysis was performed reporting the SROC curves. Sixty-two references were included in the review, of which thirty-eight were analyzed using QUADAS-2. Study designs were: clinical trials (13), retrospective studies (10), prospective studies (8), pilot studies (10), multitiered study (1); the remain studies were proof of concept or had undefined study type. Studies were divided in categories based on the physical principle employed by each diagnostic technique. Twenty-nine out of thirty-eight DTA studies were included in the meta-analysis. Heterogeneity of studies' types, testing strategy, and diagnostic task limited the systematic comparison of the techniques. Based on the SROC curves, spectroscopy achieved the best performance in terms of sensitivity (93%, 95% CI 92.8-93.2%) and specificity (85.2%, 95%CI 84.9-85.5%), even though there was high concern regarding robustness of metrics. Reflectance-confocal-microscopy, instead, demonstrated higher robustness and a good diagnostic performance (sensitivity 88.2%, 80.3-93.1%; specificity 65.2%, 55-74.2%). Best practice recommendations were proposed to reduce bias in future DTA studies. Particular attention should be dedicated to widen the use of alternative techniques to conventional dermoscopy.

Efficient embedded sleep wake classification for open-source actigraphy.

This study presents a thorough analysis of sleep/wake detection algorithms for efficient on-device sleep tracking using wearable accelerometric devices. It develops a novel end-to-end algorithm using convolutional neural network applied to raw accelerometric signals recorded by an open-source wrist-worn actigraph. The aim of the study is to develop an automatic classifier that: (1) is highly generalizable to heterogenous subjects, (2) would not require manual features' extraction, (3) is computationally lightweight, embeddable on a sleep tracking device, and (4) is suitable for a wide assortment of actigraphs. Hereby, authors analyze sleep parameters, such as total sleep time, waking after sleep onset and sleep efficiency, by comparing the outcomes of the proposed algorithm to the gold standard polysomnographic concurrent recordings. The relatively substantial agreement (Cohen's kappa coefficient, median, equal to 0.78 ± 0.07) and the low-computational cost (2727 floating-point operations) make this solution suitable for an on-board sleep-detection approach.