The Use of Tactile Sensors in Oral and Maxillofacial Surgery: An Overview.

BACKGROUND: This overview aimed to characterize the type, development, and use of haptic technologies for maxillofacial surgical purposes. The work aim is to summarize and evaluate current advantages, drawbacks, and design choices of presented technologies for each field of application in order to address and promote future research as well as to provide a global view of the issue. METHODS: Relevant manuscripts were searched electronically through Scopus, MEDLINE/PubMed, and Cochrane Library databases until 1 November 2022. RESULTS: After analyzing the available literature, 31 articles regarding tactile sensors and interfaces, sensorized tools, haptic technologies, and integrated platforms in oral and maxillofacial surgery have been included. Moreover, a quality rating is provided for each article following appropriate evaluation metrics. DISCUSSION: Many efforts have been made to overcome the technological limits of computed assistant diagnosis, surgery, and teaching. Nonetheless, a research gap is evident between dental/maxillofacial surgery and other specialties such as endovascular, laparoscopic, and microsurgery; especially for what concerns electrical and optical-based sensors for instrumented tools and sensorized tools for contact forces detection. The application of existing technologies is mainly focused on digital simulation purposes, and the integration into Computer Assisted Surgery (CAS) is far from being widely actuated. Virtual reality, increasingly adopted in various fields of surgery (e.g., sino-nasal, traumatology, implantology) showed interesting results and has the potential to revolutionize teaching and learning. A major concern regarding the actual state of the art is the absence of randomized control trials and the prevalence of case reports, retrospective cohorts, and experimental studies. Nonetheless, as the research is fast growing, we can expect to see many developments be incorporated into maxillofacial surgery practice, after adequate evaluation by the scientific community.

Uneven Terrain Recognition Using Neuromorphic Haptic Feedback.

Recent years have witnessed relevant advancements in the quality of life of persons with lower limb amputations thanks to the technological developments in prosthetics. However, prostheses that provide information about the foot-ground interaction, and in particular about terrain irregularities, are still missing on the market. The lack of tactile feedback from the foot sole might lead subjects to step on uneven terrains, causing an increase in the risk of falling. To address this issue, a biomimetic vibrotactile feedback system that conveys information about gait and terrain features sensed by a dedicated insole has been assessed with intact subjects. After having shortly experienced both even and uneven terrains, the recruited subjects discriminated them with an accuracy of 87.5%, solely relying on the replay of the vibrotactile feedback. With the objective of exploring the human decoding mechanism of the feedback startegy, a KNN classifier was trained to recognize the uneven terrains. The outcome suggested that the subjects achieved such performance with a temporal dynamics of 45 ms. This work is a leap forward to assist lower-limb amputees to appreciate the floor conditions while walking, adapt their gait and promote a more confident use of their artificial limb.

EEG-based methods for recovery prognosis of patients with disorders of consciousness: A systematic review.

OBJECTIVE: Disorders of consciousness (DoC) are acquired conditions of severely altered consciousness. Electroencephalography (EEG)-derived biomarkers have been studied as clinical predictors of consciousness recovery. Therefore, this study aimed to systematically review the methods, features, and models used to derive prognostic EEG markers in patients with DoC in a rehabilitation setting. METHODS: We conducted a systematic literature search of EEG-based strategies for consciousness recovery prognosis in five electronic databases. RESULTS: The search resulted in 2964 papers. After screening, 15 studies were included in the review. Our analyses revealed that simpler experimental settings and similar filtering cut-off frequencies are preferred. The results of studies were categorised by extracting qualitative and quantitative features. The quantitative features were further classified into evoked/event-related potentials, spectral measures, entropy measures, and graph-theory measures. Despite the variety of methods, features from all categories, including qualitative ones, exhibited significant correlations with DoC prognosis. Moreover, no agreement was found on the optimal set of EEG-based features for the multivariate prognosis of patients with DoC, which limits the computational methods applied for outcome prediction and correlation analysis to classical ones. Nevertheless, alpha power, reactivity, and higher complexity metrics were often found to be predictive of consciousness recovery. CONCLUSIONS: This study's findings confirm the essential role of qualitative EEG and suggest an important role for quantitative EEG. Their joint use could compensate for their reciprocal limitations. SIGNIFICANCE: This study emphasises the need for further efforts toward guidelines on standardised EEG analysis pipeline, given the already proven role of EEG markers in the recovery prognosis of patients with DoC.

A meta-learning algorithm for respiratory flow prediction from FBG-based wearables in unrestrained conditions.

The continuous monitoring of an individual's breathing can be an instrument for the assessment and enhancement of human wellness. Specific respiratory features are unique markers of the deterioration of a health condition, the onset of a disease, fatigue and stressful circumstances. The early and reliable prediction of high-risk situations can result in the implementation of appropriate intervention strategies that might be lifesaving. Hence, smart wearables for the monitoring of continuous breathing have recently been attracting the interest of many researchers and companies. However, most of the existing approaches do not provide comprehensive respiratory information. For this reason, a meta-learning algorithm based on LSTM neural networks for inferring the respiratory flow from a wearable system embedding FBG sensors and inertial units is herein proposed. Different conventional machine learning approaches were implemented as well to ultimately compare the results. The meta-learning algorithm turned out to be the most accurate in predicting respiratory flow when new subjects are considered. Furthermore, the LSTM model memory capability has been proven to be advantageous for capturing relevant aspects of the breathing pattern. The algorithms were tested under different conditions, both static and dynamic, and with more unobtrusive device configurations. The meta-learning results demonstrated that a short one-time calibration may provide subject-specific models which predict the respiratory flow with high accuracy, even when the number of sensors is reduced. Flow RMS errors on the test set ranged from 22.03 L/min, when the minimum number of sensors was considered, to 9.97 L/min for the complete setting (target flow range: 69.231 ± 21.477 L/min). The correlation coefficient r between the target and the predicted flow changed accordingly, being higher (r = 0.9) for the most comprehensive and heterogeneous wearable device configuration. Similar results were achieved even with simpler settings which included the thoracic sensors (r ranging from 0.84 to 0.88; test flow RMSE = 10.99 L/min, when exclusively using the thoracic FBGs). The further estimation of respiratory parameters, i.e., rate and volume, with low errors across different breathing behaviors and postures proved the potential of such approach. These findings lay the foundation for the implementation of reliable custom solutions and more sophisticated artificial intelligence-based algorithms for daily life health-related applications.

Seedless Hydrothermal Growth of ZnO Nanorods as a Promising Route for Flexible Tactile Sensors.

Hydrothermal growth of ZnO nanorods has been widely used for the development of tactile sensors, with the aid of ZnO seed layers, favoring the growth of dense and vertically aligned nanorods. However, seed layers represent an additional fabrication step in the sensor design. In this study, a seedless hydrothermal growth of ZnO nanorods was carried out on Au-coated Si and polyimide substrates. The effects of both the Au morphology and the growth temperature on the characteristics of the nanorods were investigated, finding that smaller Au grains produced tilted rods, while larger grains provided vertical rods. Highly dense and high-aspect-ratio nanorods with hexagonal prismatic shape were obtained at 75 °C and 85 °C, while pyramid-like rods were grown when the temperature was set to 95 °C. Finite-element simulations demonstrated that prismatic rods produce higher voltage responses than the pyramid-shaped ones. A tactile sensor, with an active area of 1 cm2, was fabricated on flexible polyimide substrate and embedding the nanorods forest in a polydimethylsiloxane matrix as a separation layer between the bottom and the top Au electrodes. The prototype showed clear responses upon applied loads of 2-4 N and vibrations over frequencies in the range of 20-800 Hz.

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.

An Improved Strategy for Detection and Localization of Nodules in Liver Tissues by a 16 MHz Needle Ultrasonic Probe Mounted on a Robotic Platform.

This study presents an improved strategy for the detection and localization of small size nodules (down to few mm) of agar in excised pork liver tissues via pulse-echo ultrasound measurements performed with a 16 MHz needle probe. This work contributes to the development of a new generation of medical instruments to support robotic surgery decision processes that need information about cancerous tissues in a short time (minutes). The developed ultrasonic probe is part of a scanning platform designed for the automation of surgery-associated histological analyses. It was coupled with a force sensor to control the indentation of tissue samples placed on a steel plate. For the detection of nodules, we took advantage of the property of nodules of altering not only the acoustical properties of tissues producing ultrasound attenuation, but also of developing patterns at their boundary that can modify the shape and the amplitude of the received echo signals from the steel plate supporting the tissues. Besides the Correlation Index Amplitude (CIA), which is linked to the overall amplitude changes of the ultrasonic signals, we introduced the Correlation Index Shape (CIS) linked to their shape changes. Furthermore, we applied AND-OR logical operators to these correlation indices. The results were found particularly helpful in the localization of the irregular masses of agar we inserted into some excised liver tissues, and in the individuation of the regions of major interest over which perform the vertical dissections of tissues in an automated analysis finalized to histopathology. We correctly identified up to 89% of inclusions, with an improvement of about 14% with respect to the result obtained (78%) from the analysis performed with the CIA parameter only.

Fluidic Haptic Interface for Mechano-Tactile Feedback.

Notable advancements have been achieved in providing amputees with sensation through invasive and non-invasive haptic feedback systems such as mechano-, vibro-, electro-tactile and hybrid systems. Purely mechanical-driven feedback approaches, however, have been little explored. In this paper, we now created a haptic feedback system that does not require any external power source (such as batteries) or other electronic components (see Fig. 1 ). The system is low-cost, lightweight, adaptable and robust against external impact (such as water). Hence, it will be sustainable in many aspects. We have made use of latest multi-material 3D printing technology (Stratasys Objet500 Connex3) being able to fabricate a soft sensor and a mechano-tactile feedback actuator made of a rubber (TangoBlack Plus) and plastic (VeroClear) material. When forces are applied to the fingertip sensor, fluidic pressure inside the system acts on the membrane of the feedback actuator resulting in mechano-tactile sensation. Our [Formula: see text] feedback actuator is able to transmit a force range between 0.2 N (the median touch threshold) and 2.1 N (the maximum force transmitted by the feedback actuator at a 3 mm indentation) corresponding to force range exerted to the fingertip sensor of 1.2-18.49 N.

A Machine-Learning-Based Approach to Solve Both Contact Location and Force in Soft Material Tactile Sensors.

This study addresses a design and calibration methodology based on numerical finite element method (FEM) modeling for the development of a soft tactile sensor able to simultaneously solve the magnitude and the application location of a normal load exerted onto its surface. The sensor entails the integration of a Bragg grating fiber optic sensor in a Dragon Skin 10 polymer brick (110 mm length, 24 mm width). The soft polymer mediates the transmission of the applied load to the buried fiber Bragg gratings (FBGs), and we also investigated the effect of sensor thickness on receptive field and sensitivity, both with the developed model and experimentally. Force-controlled indentations of the sensor (up to 2.5 N) were carried out through a cylindrical probe applied along the direction of the optical fiber (over an ∼90 mm span in length). A finite element model of the sensor was built and experimentally validated for 1 and 6 mm thicknesses of the soft polymeric encapsulation material, considering that the latter thickness resulted from numerical simulations as leading to optimal cross talk and sensitivity, given the chosen soft material. The FEM model was also used to train a neural network so as to obtain the inverse sensor function. Using four FBG transducers embedded in the 6-mm-thick soft polymer, the proposed machine learning approach managed to accurately detect both load magnitude (R = 0.97) and location (R = 0.99) over the whole experimental range. The proposed system could be used for developing tactile sensors that can be effectively used for a broad range of applications.

Cardio-Respiratory Monitoring in Archery Using a Smart Textile Based on Flexible Fiber Bragg Grating Sensors.

In precision sports, the control of breathing and heart rate is crucial to help the body to remain stable in the shooting position. To improve stability, archers try to adopt similar breathing patterns and to have a low heartbeat during each shot. We proposed an easy-to-use and unobtrusive smart textile (ST) which is able to detect chest wall excursions due to breathing and heart beating. The sensing part is based on two FBGs housed into a soft polymer matrix to optimize the adherence to the chest wall and the system robustness. The ST was assessed on volunteers to figure out its performance in the estimation of respiratory frequency (fR) and heart rate (HR). Then, the system was tested on two archers during four shooting sessions. This is the first study to monitor cardio-respiratory activity on archers during shooting. The good performance of the ST is supported by the low mean absolute percentage error for fR and HR estimation (≤1.97% and ≤5.74%, respectively), calculated with respect to reference signals (flow sensor for fR, photopletismography sensor for HR). Moreover, results showed the capability of the ST to estimate fR and HR during different phases of shooting action. The promising results motivate future investigations to speculate about the influence of fR and HR on archers' performance.

Tactile Decoding of Edge Orientation With Artificial Cuneate Neurons in Dynamic Conditions.

Generalization ability in tactile sensing for robotic manipulation is a prerequisite to effectively perform tasks in ever-changing environments. In particular, performing dynamic tactile perception is currently beyond the ability of robotic devices. A biomimetic approach to achieve this dexterity is to develop machines combining compliant robotic manipulators with neuroinspired architectures displaying computational adaptation. Here we demonstrate the feasibility of this approach for dynamic touch tasks experimented by integrating our sensing apparatus in a 6 degrees of freedom robotic arm via a soft wrist. We embodied in the system a model of spike-based neuromorphic encoding of tactile stimuli, emulating the discrimination properties of cuneate nucleus neurons based on pathways with differential delay lines. These strategies allowed the system to correctly perform a dynamic touch protocol of edge orientation recognition (ridges from 0 to 40°, with a step of 5°). Crucially, the task was robust to contact noise and was performed with high performance irrespectively of sensing conditions (sensing forces and velocities). These results are a step forward toward the development of robotic arms able to physically interact in real-world environments with tactile sensing.

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.

Ubiquitous Neocortical Decoding of Tactile Input Patterns.

Whereas functional localization historically has been a key concept in neuroscience, direct neuronal recordings show that input of a particular modality can be recorded well outside its primary receiving areas in the neocortex. Here, we wanted to explore if such spatially unbounded inputs potentially contain any information about the quality of the input received. We utilized a recently introduced approach to study the neuronal decoding capacity at a high resolution by delivering a set of electrical, highly reproducible spatiotemporal tactile afferent activation patterns to the skin of the contralateral second digit of the forepaw of the anesthetized rat. Surprisingly, we found that neurons in all areas recorded from, across all cortical depths tested, could decode the tactile input patterns, including neurons of the primary visual cortex. Within both somatosensory and visual cortical areas, the combined decoding accuracy of a population of neurons was higher than for the best performing single neuron within the respective area. Such cooperative decoding indicates that not only did individual neurons decode the input, they also did so by generating responses with different temporal profiles compared to other neurons, which suggests that each neuron could have unique contributions to the tactile information processing. These findings suggest that tactile processing in principle could be globally distributed in the neocortex, possibly for comparison with internal expectations and disambiguation processes relying on other modalities.

Haptic Glove and Platform with Gestural Control For Neuromorphic Tactile Sensory Feedback In Medical Telepresence †.

Advancements in the study of the human sense of touch are fueling the field of haptics. This is paving the way for augmenting sensory perception during object palpation in tele-surgery and reproducing the sensed information through tactile feedback. Here, we present a novel tele-palpation apparatus that enables the user to detect nodules with various distinct stiffness buried in an ad-hoc polymeric phantom. The contact force measured by the platform was encoded using a neuromorphic model and reproduced on the index fingertip of a remote user through a haptic glove embedding a piezoelectric disk. We assessed the effectiveness of this feedback in allowing nodule identification under two experimental conditions of real-time telepresence: In Line of Sight (ILS), where the platform was placed in the visible range of a user; and the more demanding Not In Line of Sight (NILS), with the platform and the user being 50 km apart. We found that the entailed percentage of identification was higher for stiffer inclusions with respect to the softer ones (average of 74% within the duration of the task), in both telepresence conditions evaluated. These promising results call for further exploration of tactile augmentation technology for telepresence in medical interventions.

Haptic-assistive technologies for audition and vision sensory disabilities.

PURPOSE: The aim of this review is to analyze haptic sensory substitution technologies for deaf, blind and deaf-blind individuals. METHOD: The literature search has been performed in Scopus, PubMed and Google Scholar databases using selected keywords, analyzing studies from 1960s to present. Search on databases for scientific publications has been accompanied by web search for commercial devices. Results have been classified by sensory disability and functionality, and analyzed by assistive technology. Complementary analyses have also been carried out on websites of public international agencies, such as the World Health Organization (WHO), and of associations representing sensory disabled persons. RESULTS: The reviewed literature provides evidences that sensory substitution aids are able to mitigate in part the deficits in language learning, communication and navigation for deaf, blind and deaf-blind individuals, and that the tactile sense can be a means of communication to provide some kind of information to sensory disabled individuals. CONCLUSIONS: A lack of acceptance emerged from the discussion of capabilities and limitations of haptic assistive technologies. Future researches shall go towards miniaturized, custom-designed and low-cost haptic interfaces and integration with personal devices such as smartphones for a major diffusion of sensory aids among disabled. Implications for rehabilitation Systematic review of state of the art of haptic assistive technologies for vision and audition sensory disabilities. Sensory substitution systems for visual and hearing disabilities have a central role in the transmission of information for patients with sensory impairments, enabling users to interact with the not disabled community in daily activities. Visual and auditory inputs are converted in haptic feedback via different actuation technologies. The information is presented in the form of static or dynamic stimulation of the skin. Their effectiveness and ease of use make haptic sensory substitution systems suitable for patients with different levels of disabilities. They constitute a cheaper and less invasive alternative to implantable partial sensory restitution systems. Future researches are oriented towards the optimization of the stimulation parameters together with the development of miniaturized, custom-designed and low-cost aids operating in synergy in networks, aiming to increase patients' acceptability of these technologies.

An innovative robotic platform for magnetically-driven painless colonoscopy.

Colorectal cancer (CRC) represents a significant medical threat with a dramatic impact on the healthcare system with around 1.3 million patients worldwide, causing more than 700 thousand deaths annually. A key-aspect to successful and cost-effective disease management is represented by the early detection of CRC at asymptomatic stage. For this reason, population screening is highly recommended for patients older than 50 years or at high risk for familiarity. Currently, the standard endoscopic techniques do not meet this need. In recent years, innovative endoscopic robotic techniques and active locomotion devices have been developed as alternatives to conventional colonoscopy. The magnetically-driven robotic platform, presented by the authors, is conceived to perform less invasive and more comfortable colonoscopy with the aim to promote screening campaigns for detection of early colorectal neoplasm.

Neuromorphic Artificial Touch for Categorization of Naturalistic Textures.

We implemented neuromorphic artificial touch and emulated the firing behavior of mechanoreceptors by injecting the raw outputs of a biomimetic tactile sensor into an Izhikevich neuronal model. Naturalistic textures were evaluated with a passive touch protocol. The resulting neuromorphic spike trains were able to classify ten naturalistic textures ranging from textiles to glass to BioSkin, with accuracy as high as 97%. Remarkably, rather than on firing rate features calculated over the stimulation window, the highest achieved decoding performance was based on the precise spike timing of the neuromorphic output as captured by Victor Purpura distance. We also systematically varied the sliding velocity and the contact force to investigate the role of sensing conditions in categorizing the stimuli via the artificial sensory system. We found that the decoding performance based on the timing of neuromorphic spike events was robust for a broad range of sensing conditions. Being able to categorize naturalistic textures in different sensing conditions, these neurorobotic results pave the way to the use of neuromorphic tactile sensors in future real-life neuroprosthetic applications.