Biorobotic systems increasingly rely on distributed tactile sensing to achieve safe and adaptive interaction with complex environments. Fiber-optic technologies based on Rayleigh backscattering enable continuous, high-resolution strain measurements along standard silica fibers, offering an attractive platform for large-area electronic skins (e-skins). However, these sensors exhibit intrinsic cross-sensitivity to mechanical strain and temperature, and the thermo-mechanical behavior of fibers embedded in compliant polymers remains insufficiently understood. Here, a systematic characterization of the thermal response of Rayleigh backscattering optical fibers embedded in silicone e-skin patches is presented along with a physically grounded interpretation based on shear-lag strain-transfer theory. The results show that the fiber signal depends linearly on temperature, with a position-dependent thermal sensitivity that follows a reproducible bell-shaped spatial profile. The peak thermal sensitivity increases linearly with patch size, demonstrating a predictable scaling of thermal sensitivity with patch size and directly linking geometrical design parameters to the effective thermo-mechanical response of the distributed Rayleigh sensor. Moreover, the bell-shaped profile proved highly stable across repeated heating cycles, with approximately 1% inter-trial variability, and was consistently observed under different thermal excitation protocols, including both gradual ramps and stepwise temperature changes. Beyond clarifying the thermo-mechanical physics of distributed fiber-optic e-skins, this work establishes predictive design principles for thermal sensitivity control and paves the way towards a quantitative framework for developing compensation strategies that robustly decouple mechanical and thermal contributions in real-world collaborative and medical biorobotic systems.

The advent of new industrial paradigms is increasingly promoting the development of human-machine interfaces to allow the cooperation between operators and robots. In this context, the awareness of the surroundings on machines, enabled by precise tactile sensing, is desirable to guarantee safe and effective interactions. Rayleigh backscattering-based optical fiber sensors present high distributed sensitivity to deformations and integration flexibility, making them suitable for the realization of silicone-based e-skins. However, these sensors have responses determined by coupled thermal and mechanical effects and, therefore, the interpretation of their readouts is crucial for the effective implementation of tactile features in e-skins. This study reports the thermal characterization of an e-skin prototype embedding Rayleigh sensors. The results demonstrate a linear relationship between the temperature and the sensor outputs (Pearson's coefficient of 0.99) and a thermal sensitivity depending on the fiber position within the silicone substrate. These outcomes provide relevant information for the further development of enhanced tactile sensors for robotic applications.

The loss of tactile perception in individuals hinders their ability to interact with the environment. The touch sense not only allows humans to recognize textures and shapes, but also to feel pleasantness evoked by the touched surface. The perception of tactile affective sensations is essential for a person's well-being and social interactions. Hence, research in the field of bionic limbs aims at investigating how to restore the sense of touch comprehensively. In this study, the encoding of surface pleasantness by means of an artificial fingertip via neuronal-like spiking patterns is introduced. The proposed sensing system presents biomimetic features, mimicking the behavior and the distribution of the human finger's Merkel mechanoreceptors. The artificial fingertip demonstrated to capture the variations in pressure, shear, and vibration during the exploration of varied surfaces. The tactile information was then converted into spike trains through a neuromorphic approach. The outcomes of this study lay the foundations for restoring pleasantness sensations from textures through neural sensory feedback interfaces.

The loss of tactile perception in individuals hinders their ability to interact with the environment. The touch sense not only allows humans to recognize textures and shapes, but also to feel pleasantness evoked by the touched surface. The perception of tactile affective sensations is essential for a person's well-being and social interactions. Hence, research in the field of bionic limbs aims at investigating how to restore the sense of touch comprehensively. In this study, the encoding of surface pleasantness by means of an artificial fingertip via neuronal-like spiking patterns is introduced. The proposed sensing system presents biomimetic features, mimicking the behavior and the distribution of the human finger's Merkel mechanoreceptors. The artificial fingertip demonstrated to capture the variations in pressure, shear, and vibration during the exploration of varied surfaces. The tactile information was then converted into spike trains through a neuromorphic approach. The outcomes of this study lay the foundations for restoring pleasantness sensations from textures through neural sensory feedback interfaces.

The sensing of left ventricular (LV) activity is fundamental in the diagnosis and monitoring of cardiovascular health in high-risk patients after cardiac surgery to achieve better short- and long-term outcome. Conventional approaches rely on noninvasive measurements even if, in the latest years, invasive microelectromechanical systems (MEMS) sensors have emerged as a valuable approach for precise and continuous monitoring of cardiac activity. The main challenges in designing cardiac MEMS sensors are represented by miniaturization, biocompatibility, and long-term stability. Here, we present a MEMS piezoresistive cardiac sensor capable of continuous monitoring of LV activity over time following epicardial implantation with a pericardial patch graft in adult minipigs. In acute and chronic scenarios, the sensor was able to compute heart rate with a root mean square error lower than 2 BPM. Early after up to 1 month of implantation, the device was able to record the heart activity during the most important phases of the cardiac cycle (systole and diastole peaks). The sensor signal waveform, in addition, closely reflected the typical waveforms of pressure signal obtained via intraventricular catheters, offering a safer alternative to heart catheterization. Furthermore, histological analysis of the LV implantation site following sensor retrieval revealed no evidence of myocardial fibrosis. Our results suggest that the epicardial LV implantation of an MEMS sensor is a suitable and reliable approach for direct continuous monitoring of cardiac activity. This work envisions the use of this sensor as a cardiac sensing device in closed-loop applications for patients undergoing heart surgery.

One of the most severe problems of the world is represented by climate change and its relevant negative consequences on the environment. To tackle future environmental changes, it is imperative to modify current technologies of energy generation, since traditional generation techniques, such as coal-burning and the combustion of gases and petroleum derivatives, have detrimental effects on the environment. In this research study, a bio-mimetic system for polarized wind energy harvesting was designed and manufactured, suitable to be installed besides roads and streets to exploit both the wind flow generated by traffic and the natural wind flow. Through an essential kinematic that is devoid of any auxiliary system and that exploits the electromagnetic induction principle, authors aimed at obtaining higher efficiencies with respect to the very low values that characterize rotary turbines. The final product was supposed to resemble a leaf swaying under the action of the wind flow. It is referred to as Smart Leaf. Here, four Leaves are connected in parallel, forming a Four-Leaf Smart Bush. A mathematical model of the Leaf swaying dynamic and of the electromagnetic induction principle is illustrated. The Smart Bush performances are assessed through experimental tests, both at an Eolic generator and on field. The Eolic generator is exploited to reproduce the principal wind profiles due to traffic and to natural air motion in order to identify the most appropriate urban and sub-urban contexts as installation sites for the Smart Bush. Based on the experimental results, an efficiency much lower than rotary axial devices has been estimated. Finally, a technology demonstrator is assembled to monitor air pollution by means of sensors powered by the Smart Bush. When the accumulated electric energy is enough, the software detects air composition and shoes the results on a dedicated display. Future developments will mainly regard the development and implementation of a voltage amplifier circuit and the design of a more refined electronics in general.

In the food and medical packaging industries, clean packaging is crucial to both customer satisfaction and hygiene. An operational Quality Assurance Department (QAD) is necessary for detecting contaminated packages. Manual examination becomes tedious and may lead to instances of contamination being missed along the production line. To address this issue, a system for contamination detection is proposed using an enhanced deep convolutional neural network (CNN) in a human–robot collaboration framework. The proposed system utilizes a CNN to identify and classify the presence of contaminants on product surfaces. A dataset is generated, and augmentation methods are applied to the dataset for nine classes such as coffee, spot, chocolate, tomato paste, jam, cream, conditioner, shaving cream, and toothpaste contaminants. The experiment was conducted using a mechatronic platform with a camera for contamination detection and a time-of-flight sensor for safe machine–environment interaction. The results of the experiment indicate that the reported system can accurately identify contamination with 99.74% mean average precision (mAP).

The breakthrough of Industry 4.0 and the upcoming 5th industrial revolution highly foster the growth of collaborative robotics. Robot companions may help humans in performing heavy and complex tasks and assist them in several daily life frameworks. Safe interactions between robots and humans can be enabled by equipping the formers with sensors to provide them with awareness of the surrounding. In this regard, we developed a touch sensitive artificial skin based on Fiber Bragg Gratings (FBGs) for enabling cooperation in robots and we present a live demo of its functioning.

Interest in tactile sensing technologies is advancing due to the growing adoption of robots in daily life activities. Human-machine interaction has thus to be safe, and collaborative robotics is becoming increasingly important. The present work features the design, development and preliminary validation of a soft large area sensor for tactile and proprioceptive sensing in a collaborative robotic manipulator. Such a manipulator is shaped to resemble the human hand and within this paper we focused on the index finger. The finger architecture has a design which allows setting up a structured 3D model, with flexible parametrization and fast prototyping. An optical fiber embedding 12 Fiber Bragg Gratings (FBGs) has been integrated in a soft polymeric matrix to mimic human sense of touch abilities of a whole finger. In order to assess the sensorized robotic manipulator, a mechatronic validation platform has been developed and employed. Preliminary results show a mechanical decoupling between exteroceptive and proprioceptive functions, and among the spatially distributed outputs of the sensor array. These results demonstrate the potential of the proposed approach towards achieving dexterous and fine capabilities in the manipulation of objects.

Continuous and reliable cardiac function monitoring could improve medication adherence in patients at risk of heart failure.This work presents an innovative implantable Fiber Bragg Grating-based soft sensor designed to sense mechanical cardiac activity.The sensor was tested in an isolated beating ovine heart platform, with 3 different hearts operated in wide-ranging conditions.In order to investigate the sensor capability to track the ventricular beats in real-time, two causal algorithms were proposed for detecting the beats from sensor data and to discriminate artifacts.The first based on dynamic thresholds while the second is a hybrid convolutional and recurrent Neural Network.An error of 2.7 ± 0.7 beats per minute was achieved in tracking the heart rate.Finally, we have confirmed the sensor reliability in monitoring the heart activity of healthy adult minipig with an error systematically lower than 1 Bpm.

An endoscopic tactile robotic capsule, embedding miniaturized MEMS force sensors, is presented. The capsule is conceived to provide automatic palpation of non-polypoid colorectal tumours during colonoscopy, since it is characterized by high degree of dysplasia, higher invasiveness and lower detection rates with respect to polyps. A first test was performed employing a silicone phantom that embedded inclusions with variable hardness and curvature. A hardness-based classification was implemented, demonstrating detection robustness to curvature variation. By comparing a set of supervised classification algorithms, a weighted 3-nearest neighbor classifier was selected. A bias force normalization model was introduced in order to make different acquisition sets consistent. Parameters of this model were chosen through a particle swarm optimization method. Additionally, an ex-vivo test was performed to assess the capsule detection performance when magnetically-driven along a colonic tissue. Lumps were identified as voltage peaks with a prominence depending on the total magnetic force applied to the capsule. Accuracy of 94 % in hardness classification was achieved, while a 100 % accuracy is obtained for the lump detection within a tolerance of 5 mm from the central path described by the capsule. In real application scenario, we foresee our device aiding physicians to detect tumorous tissues.

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.

A pneumatic haptic display for collaborative robotics applications is presented within this paper. The 3×3 tactor array consists of a polymeric parallelepiped with 9 cylindrical chambers (3mm diameter) providing haptic feedback by means of pneumatic actuation. A preliminary study, on four subjects, has been performed to identify the optimal pressure to deliver the tactile stimulation and assess system functionality. Four different spatial stimuli, based on the activation of different chambers of the tactor array, were provided at three different pressure levels (35, 70, 105 kPa). Results suggest that a pressure of 70 kPa leads to a detection accuracy of 96.9%. Further studies will focus on a deeper assessment of the tactile display using different configurations.

Humans rely on their sense of touch to interact with the environment. Thus, restoring lost tactile sensory capabilities in amputees would advance their quality of life. In particular, texture discrimination is an important component for the interaction with the environment, but its restoration in amputees has been so far limited to simplified gratings. Here we show that naturalistic textures can be discriminated by trans-radial amputees using intraneural peripheral stimulation and tactile sensors located close to the outer layer of the artificial skin. These sensors exploit the morphological neural computation (MNC) approach, i.e., the embodiment of neural computational functions into the physical structure of the device, encoding normal and shear stress to guarantee a faithful neural temporal representation of stimulus spatial structure. Two trans-radial amputees successfully discriminated naturalistic textures via the MNC-based tactile feedback. The results also allowed to shed light on the relevance of spike temporal encoding in the mechanisms used to discriminate naturalistic textures. Our findings pave the way to the development of more natural bionic limbs.

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.

The effort toward replicating human skills into artificial systems is growing constantly. While artificial vision has reached a certain reliability, the sense of touch is still hard to introduce into robotic devices. Human manipulation comprises a sequence of static and dynamic actions, which may include unforeseen events, such as variation of object position, movement of the fingers, and modification of the object dimensions and shape (e.g., with soft objects) due to inappropriate force levels. These circumstances are likely to produce the slippage of the object being manipulated. Artificial manipulators are not yet able to be effective in dynamic environments. This paper intends to provide a method for the identification and prevention of slippage with tactile sensors. The method is based on filtering the tactile signals to extract slippage information. The filtering has been executed by means of the stationary wavelet transform that consists of recursive filtering operations. Then, the transformed signal has been rectified and its root mean square has been computed. Finally, an on/off signal has been generated according to a threshold logic. Eight natural surfaces, featuring diverse tactile properties, have been used with the aim of validating the ability of the method to be applied regardless the surface properties. To evaluate repeatability and generalization ability, a total of 2000 experiments have been performed, 250 per each stimulus, with a mechatronic platform: five velocities combined with five indentation force levels, repeating each combination 10 times. Results are provided in terms of true positive detection and of delay between onset of slippage and algorithm output.

A major challenge in upper limb neuroprostheses is to reproduce the tactile feedback as the one provided by the spiking activity of human mechanoreceptors. In this paper, we aim at emulating the firing behavior of Merkel mechanoreceptors, innervated by slowly adapting type I (SA1) receptors, in silico by means of a custom implementation of the Izhikevich spiking neuron model with a porting function of the sensors output. We matched the neuron model output with the sustained firing observed in neurophysiological experiments in response to constant stimuli to the skin. We compared different input transformation functions to find the proper trade-off between agreement with biological spiking activity and model leanness. We identified a porting function that converts the output of physical sensors into the input to artificial spiking models of mechanoreceptors. The porting function was then validated by comparing the firing rate-indentation curve of real mechanoreceptors with the one obtained from an MEMS-based biomimetic tactile sensor platform. From the analysis of the adjusted-residual variance, the two curves result coherent. Therefore, having applied the calibration inverse function to a sensor output, the proposed porting function allows obtaining the proper input to an artificial neuron model, enabling the generation of neuromorphic signals.

To implement safety measures and to increase effectiveness of navigation in gastrointestinal endoscopy, we have integrated a dense array of MEMS tactile sensors in a magnetically-driven robotic endoscopic capsule. With traditional endoscopic probes, the force applied to the gastrointestinal wall cannot be directly measured and controlled. The presented sensorized capsule will allow implementing strategies for preventing tissue damages and thus pain, discomfort or even perforation during colonoscopy. The sensors embedded onto the here presented capsule will allow for online force readout and a closed-loop force control for real-time safety strategy has been implemented. Moreover, fine force readout will enhance capsule navigation, allowing optimization of the contact condition and thus diminishing friction with the endoluminal wall. The presented system will be tested in the future to prove capabilities as tactile tool for remote palpation, providing the physician with texture and stiffness information during endoscopic procedure.

Colorectal cancer (CRC) is a significant medical threat and it represents in Europe the second and third most common cancer for women and men, respectively. Early detection of CRC is a key-aspect to improve survival rate. Specifically, the survival rate at five years decreases with the pathological progress of the cancer, up to 11% in case of cancer stage IV. Common diagnostic techniques for the detection of cancerous tissue in CRC screening have a miss rate in the order of 14% to 32%, thus resulting as not reliable and accurate solutions for screening. In this paper, we present the design and preliminary results of an innovative force-sensing instrument for remote palpation of the inner colonic wall. The instrument is equipped with three optical fibres having each one a fibre Bragg grating sensor with a dimension compatible with standard colonoscope operating channels.