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.

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.

A Multi-Parametric Wearable System to Monitor Neck Movements and Respiratory Frequency of Computer Workers.

Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These factors may also alter respiratory functions. Health and safety interventions can reduce neck pain and, more generally, the symptoms of musculoskeletal disorders and reduce the consequent economic burden. In this work, a multi-parametric wearable system based on two fiber Bragg grating sensors is proposed for monitoring neck movements and breathing activity of computer workers. The sensing elements were positioned on the neck, in the frontal and sagittal planes, to monitor: (i) flexion-extension and axial rotation repetitions, and (ii) respiratory frequency. In this pilot study, five volunteers were enrolled and performed five repetitions of both flexion-extension and axial rotation, and ten breaths of both quite breathing and tachypnea. Results showed the good performances of the proposed system in monitoring the aforementioned parameters when compared to optical reference systems. The wearable system is able to well-match the trend in time of the neck movements (both flexion-extension and axial rotation) and to estimate mean and breath-by-breath respiratory frequency values with percentage errors ≤6.09% and ≤1.90%, during quiet breathing and tachypnea, respectively.

Morphological Neural Computation Restores Discrimination of Naturalistic Textures in Trans-radial Amputees.

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.

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.

Tactile Sensing and Control of Robotic Manipulator Integrating Fiber Bragg Grating Strain-Sensor.

Tactile sensing is an instrumental modality of robotic manipulation, as it provides information that is not accessible via remote sensors such as cameras or lidars. Touch is particularly crucial in unstructured environments, where the robot's internal representation of manipulated objects is uncertain. In this study we present the sensorization of an existing artificial hand, with the aim to achieve fine control of robotic limbs and perception of object's physical properties. Tactile feedback is conveyed by means of a soft sensor integrated at the fingertip of a robotic hand. The sensor consists of an optical fiber, housing Fiber Bragg Gratings (FBGs) transducers, embedded into a soft polymeric material integrated on a rigid hand. Through several tasks involving grasps of different objects in various conditions, the ability of the system to acquire information is assessed. Results show that a classifier based on the sensor outputs of the robotic hand is capable of accurately detecting both size and rigidity of the operated objects (99.36 and 100% accuracy, respectively). Furthermore, the outputs provide evidence of the ability to grab fragile objects without breakage or slippage e and to perform dynamic manipulative tasks, that involve the adaptation of fingers position based on the grasped objects' condition.

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.

Neuromorphic Vibrotactile Stimulation of Fingertips for Encoding Object Stiffness in Telepresence Sensory Substitution and Augmentation Applications.

We present a tactile telepresence system for real-time transmission of information about object stiffness to the human fingertips. Experimental tests were performed across two laboratories (Italy and Ireland). In the Italian laboratory, a mechatronic sensing platform indented different rubber samples. Information about rubber stiffness was converted into on-off events using a neuronal spiking model and sent to a vibrotactile glove in the Irish laboratory. Participants discriminated the variation of the stiffness of stimuli according to a two-alternative forced choice protocol. Stiffness discrimination was based on the variation of the temporal pattern of spikes generated during the indentation of the rubber samples. The results suggest that vibrotactile stimulation can effectively simulate surface stiffness when using neuronal spiking models to trigger vibrations in the haptic interface. Specifically, fractional variations of stiffness down to 0.67 were significantly discriminated with the developed neuromorphic haptic interface. This is a performance comparable, though slightly worse, to the threshold obtained in a benchmark experiment evaluating the same set of stimuli naturally with the own hand. Our paper presents a bioinspired method for delivering sensory feedback about object properties to human skin based on contingency-mimetic neuronal models, and can be useful for the design of high performance haptic devices.

Encapsulation of Piezoelectric Transducers for Sensory Augmentation and Substitution with Wearable Haptic Devices.

The integration of polymeric actuators in haptic displays is widespread nowadays, especially in virtual reality and rehabilitation applications. However, we are still far from optimizing the transducer ability in conveying sensory information. Here, we present a vibrotactile actuator characterized by a piezoelectric disk embedded in a polydimethylsiloxane (PDMS) shell. An original encapsulation technique was performed to provide the stiff active element with a compliant cover as an interface towards the soft human skin. The interface stiffness, together with the new geometry, generated an effective transmission of vibrotactile stimulation and made the encapsulated transducer a performant component for the development of wearable tactile displays. The mechanical behavior of the developed transducer was numerically modeled as a function of the driving voltage and frequency, and the exerted normal forces were experimentally measured with a load cell. The actuator was then tested for the integration in a haptic glove in single-finger and bi-finger condition, in a 2-AFC tactile stimulus recognition test. Psychophysical results across all the tested sensory conditions confirmed that the developed integrated haptic system was effective in delivering vibrotactile information when the frequency applied to the skin is within the 200⁻700 Hz range and the stimulus variation is larger than 100 Hz.