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This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT-UMAP combination stands out in the evaluation metrics.

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In the domain of optical fiber distributed acoustic sensing, the persistent challenge of extending sensing distances while concurrently improving spatial resolution and frequency response range has been a complex endeavor. The amalgamation of pulse compression and frequency division multiplexing methodologies has provided certain advantages. Nevertheless, this approach is accompanied by the drawback of significant bandwidth utilization and amplified hardware investments. This study introduces an innovative distributed optical fiber acoustic sensing system aimed at optimizing the efficient utilization of spectral resources by combining compressed pulses and frequency division multiplexing. The system continuously injects non-linear frequency modulation detection pulses spanning various frequency ranges. The incorporation of non-uniform frequency division multiplexing augments the vibration frequency response spectrum. Additionally, nonlinear frequency modulation adeptly reduces crosstalk and enhances sidelobe suppression, all while maintaining a favorable signal-to-noise ratio. Consequently, this methodology substantially advances the spatial resolution of the sensing system. Experimental validation encompassed the multiplexing of eight frequencies within a 120 MHz bandwidth. The results illustrate a spatial resolution of approximately 5 m and an expanded frequency response range extending from 1 to 20 kHz across a 16.3 km optical fiber. This achievement not only enhances spectral resource utilization but also reduces hardware costs, making the system even more suitable for practical engineering applications.

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Often overlooked, vibration transmission through the entire body of an animal is an important factor in understanding vibration sensing in animals. To investigate the role of dynamic properties and vibration transmission through the body, we used a modal test and lumped parameter modelling for a spider. The modal test used laser vibrometry data on a tarantula, and revealed five modes of the spider in the frequency range of 20-200 Hz. Our developed and calibrated model took into account the bounce, pitch and roll of the spider body and bounce of all the eight legs. We then performed a parametric study using this calibrated model, varying factors such as mass, inertia, leg stiffness, damping, angle and span to study what effect they had on vibration transmission. The results support that some biomechanical parameters can act as physical constraints on vibration sensing. But also, that the spider may actively control some biomechanical parameters to change the signal intensity it can sense. Furthermore, our analysis shows that the parameter changes in front and back legs have a greater influence on whole system dynamics, so may be of particular importance for active control mechanisms to facilitate biological sensing functions.

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Distributed optical fiber sensors (DOFSs) are a promising technology for their unique advantage of long-distance distributed measurements in industrial applications. In recent years, modern industrial monitoring has called for comprehensive multi-parameter measurements to accurately identify fault events. The hybrid DOFS technology, which combines the Rayleigh, Brillouin, and Raman scattering mechanisms and integrates multiple DOFS systems in a single configuration, has attracted growing attention and has been developed rapidly. Compared to a single DOFS system, the multi-parameter measurements based on hybrid DOFS offer multidimensional valuable information to prevent misjudgments and false alarms. The highly integrated sensing structure enables more efficient and cost-effective monitoring in engineering. This review highlights the latest progress of the hybrid DOFS technology for multi-parameter measurements. The basic principles of the light-scattering-based DOFSs are initially introduced, and then the methods and sensing performances of various techniques are successively described. The challenges and prospects of the hybrid DOFS technology are discussed in the end, aiming to pave the way for a vaster range of applications.

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BACKGROUND: The shift towards minimally invasive surgery is associated with a significant reduction of tactile information available to the surgeon, with compensation strategies ranging from vision-based techniques to the integration of sensing concepts into surgical instruments. Tactile information is vital for palpation tasks such as the differentiation of tissues or the characterisation of surfaces. This work investigates a new sensing approach to derive palpation-related information from vibration signals originating from instrument-tissue-interactions. METHODS: We conducted a feasibility study to differentiate three non-animal and three animal tissue specimens based on palpation of the surface. A sensor configuration was mounted at the proximal end of a standard instrument opposite the tissue-interaction point. Vibro-acoustic signals of 1680 palpation events were acquired, and the time-varying spectrum was computed using Continuous-Wavelet-Transformation. For validation, nine spectral energy-related features were calculated for a subsequent classification using linear Support Vector Machine and k-Nearest-Neighbor. RESULTS: Indicators derived from the vibration signal are highly stable in a set of palpations belonging to the same tissue specimen, regardless of the palpating subject. Differences in the surface texture of the tissue specimens reflect in those indicators and can serve as a basis for differentiation. The classification following a supervised learning approach shows an accuracy of >93.8% for the three-tissue classification tasks and decreases to 78.8% for a combination of all six tissues. CONCLUSIONS: Simple features derived from the vibro-acoustic signals facilitate the differentiation between biological tissues, showing the potential of the presented approach to provide information related to the interacting tissue. The results encourage further investigation of a yet little-exploited source of information in minimally invasive surgery.

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The direct tactile assessment of surface textures during palpation is an essential component of open surgery that is impeded in minimally invasive and robot-assisted surgery. When indirectly palpating with a surgical instrument, the structural vibrations from this interaction contain tactile information that can be extracted and analysed. This study investigates the influence of the parameters contact angle α and velocity v→ on the vibro-acoustic signals from this indirect palpation. A 7-DOF robotic arm, a standard surgical instrument, and a vibration measurement system were used to palpate three different materials with varying α and v→. The signals were processed based on continuous wavelet transformation. They showed material-specific signatures in the time-frequency domain that retained their general characteristic for varying α and v→. Energy-related and statistical features were extracted, and supervised classification was performed, where the testing data comprised only signals acquired with different palpation parameters than for training data. The classifiers support vector machine and k-nearest neighbours provided 99.67% and 96.00% accuracy for the differentiation of the materials. The results indicate the robustness of the features against variations in the palpation parameters. This is a prerequisite for an application in minimally invasive surgery but needs to be confirmed in realistic experiments with biological tissues.

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Objective:To explore vibration, position and motion proprioception of the ankle joints after a stroke.Methods:Twenty-eight stroke survivors with impaired ankle proprioception were divided into a right-side stroke group ( n=18) and a left-side stroke group ( n=8). Twenty-two healthy volunteers constituted a control group. Vibration perception thresholds, passive and active joint angle resetting, and motion minimum thresholds were quantified among the stroke survivors on both the healthy and the affected side. With the controls the dominant and non-dominant sides were used. The differences in proprioception between the healthy volunteers and the stroke patients, between the affected side and the healthy side of the stroke patients, and between left- and right-side stroke patients were analyzed and compared. Results:Among the stroke survivors the vibration perception threshold on the affected side averaged (28.91±22.53)μm. The absolute difference in the perception of passive positioning was (5.49±5.39)° for 15° of plantar flexion and (4.48±3.89)° for 5° of dorsal extension. In active positioning plantar flexion was (5.23±4.34)° and for 30° of plantar flexion it was (3.26±1.73)°. The 5° dorsal extension error was (4.97±3.48)°. The motion perception thresholds between 20° of plantar flexion, 10° of plantar flexion and the neutral position were significantly higher, on average, than among the control group. The stroke group also had significantly higher motion perception thresholds than the control group.Conclusion:The vibration, position, and motion sense of the ankle joint on a stroke survivor′s affected side tend to be impaired, with the impairment of vibration and motion sensing tend to be more substantial. After stroke, there is also mild impairment of vibration, position and motion sensing in the healthy ankle joint. The impairment of proprioception caused by right cerebral hemisphere injury may be more serious than that caused by injury on the left.

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Wearable devices like smartwatches and smart wristbands have gained substantial popularity in recent years. However, their small interfaces create inconvenience and limit computing functionality. To fill this gap, we propose ViWatch, which enables robust finger interactions under deployment variations, and relies on a single IMU sensor that is ubiquitous in COTS smartwatches. To this end, we design an unsupervised Siamese adversarial learning method. We built a real-time system on commodity smartwatches and tested it with over one hundred volunteers. Results show that the system accuracy is about 97% over a week. In addition, it is resistant to deployment variations such as different hand shapes, finger activity strengths, and smartwatch positions on the wrist. We also developed a number of mobile applications using our interactive system and conducted a user study where all participants preferred our un-supervised approach to supervised calibration. The demonstration of ViWatch is shown at https://youtu.be/N5-ggvy2qfI.

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Microsystems play an important role in the Internet of Things (IoT). In many unattended IoT applications, microsystems with small size, lightweight, and long life are urgently needed to achieve covert, large-scale, and long-term distribution for target detection and recognition. This paper presents for the first time a low-power, long-life microsystem that integrates self-power supply, event wake-up, continuous vibration sensing, and target recognition. The microsystem is mainly used for unattended long-term target perception and recognition. A composite energy source of solar energy and battery is designed to achieve self-powering. The microsystem's sensing module, circuit module, signal processing module, and transceiver module are optimized to further realize the small size and low-power consumption. A low-computational recognition algorithm based on support vector machine learning is designed and ported into the microsystem. Taking the pedestrian, wheeled vehicle, and tracked vehicle as targets, the proposed microsystem of 15 cm3 and 35 g successfully realizes target recognitions both indoors and outdoors with an accuracy rate of over 84% and 65%, respectively. Self-powering of the microsystem is up to 22.7 mW under the midday sunlight, and 11 min self-powering can maintain 24 h operation of the microsystem in sleep mode.

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Accurate and fast contact detection between a robot manipulator and objects is crucial for safe robot-object and human-robot interactions. Traditional collision detection techniques relied on force-torque sensors and Columb friction cone estimation. However, the strain gauges used in the conventional force sensors require low-noise and high-precision electronics to deliver the signal to the final user. The Signal-to-Noise Ratio (SNR) in these devices is still an issue in light contact detection. On the other hand, the Eccentric Rotating Mass (ERM) motors are very sensitive to subtle touch as their vibrating resonant state loses immediately. The vibration, in this case, plays a core role in triggering the tactile event. This project's primary goal is to use generated and received vibrations to establish the scope of object properties that can be obtained through low-frequency generation on one end and Fourier analysis of the accelerometer data on the other end. The main idea behind the system is the phenomenon of change in vibration propagation patterns depending on the grip properties. Moreover, the project's original aim is to gather enough information on vibration feedback on objects of various properties and compare them. These data sets are further analyzed in terms of frequency and applied grip force correlations in order to prepare the ground for pattern extraction and recognition based on the physical properties of an object.

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Functional wood materials often rely on active additives due to the weak piezoelectric response of wood itself. Here, we chemically modify wood to form functionalized, eco-friendly wood veneer for self-powered vibration sensors. Only the piezoelectricity of the cellulose microfibrils is used, where the drastic improvement comes only from molecular and nanoscale wood structure tuning. Sequential wood modifications (delignification, oxidation, and model fluorination) are performed, and effects on vibration sensing abilities are investigated. Wood veneer piezoelectricity is characterized by the piezoresponse force microscopy mode in atomic force microscopy. Delignification, oxidation, and model fluorination of wood-based sensors provide output voltages of 11.4, 23.2, and 60 mV by facilitating cellulose microfibril deformation. The vibration sensing ability correlates with improved piezoelectricity and increased cellulose deformation, most likely by large, local cell wall bending. This shows that nanostructural wood materials design can tailor the functional properties of wood devices with potential in sustainable nanotechnology.

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Termites sense tiny substrate-borne vibrations through subgenual organs (SGOs) located within their legs' tibiae. Little is known about the SGOs' structure and physical properties. We applied high-resolution (voxel size 0.45 µm) micro-computed tomography (µCT) to Australian termites, Coptotermes lacteus and Nasutitermes exitiosus (Hill) to test two staining techniques. We compared the effectiveness of a single stain of Lugol's iodine solution (LS) to LS followed by Phosphotungstic acid (PTA) solutions (1% and 2%). We then present results of a soldier of Nasutitermes exitiosus combining µCT with LS + 2%PTS stains and scanning electron microscopy to exemplify the visualisation of their SGOs. The termite's SGO due to its approximately oval shape was shown to have a maximum diameter of 60 µm and a minimum of 48 µm, covering 60 ± 4% of the leg's cross-section and 90.4 ± 5% of the residual haemolymph channel. Additionally, the leg and residual haemolymph channel cross-sectional area decreased around the SGO by 33% and 73%, respectively. We hypothesise that this change in cross-sectional area amplifies the vibrations for the SGO. Since SGOs are directly connected to the cuticle, their mechanical properties and the geometric details identified here may enable new approaches to determine how termites sense micro-vibrations.

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This paper aims to evaluate detection algorithms for perimeter security systems based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). Our own designed and developed sensor system was used for the measurement. The main application of the system is in the area the perimeter fencing intrusion detection. The system is unique thanks to the developed motherboard, which contains a field-programmable gate array (FPGA) that takes care of signal processing. This allows the entire system to be integrated into a 1U rack chassis. A polygon containing two different fence types and also cable laid underground in a plastic tube was used for testing. Edge detection algorithms using the Sobel and Prewitt operators are considered for post-processing. The comparison is made based on the signal-to-noise ratio (SNR) values calculated for each event. Results of algorithms based on edge detection methods are compared with the conventional differential method commonly used in Φ-OTDR systems.

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In this work, graphene oxide (GO) particles were modified with a nano-sized poly(butyl acrylate) (PBA) layer to improve the hydrophobicity of the GO and improve compatibility with PVDF. The improved hydrophobicity was elucidated using contact angle investigations, and exhibit nearly 0° for neat GO and 102° for GO-PBA. Then, the neat GO and GO-PBA particles were mixed with PVDF using a twin screw laboratory extruder. It was clearly shown that nano-sized PBA layer acts as plasticizer and shifts glass transition temperature from -38.7 °C for neat PVDF to 45.2 °C for PVDF/GO-PBA. Finally, the sensitivity to the vibrations of various frequencies was performed and the piezoelectric constant in the thickness mode, d33, was calculated and its electrical load independency were confirmed. Received values of the d33 were for neat PVDF 14.7 pC/N, for PVDF/GO 20.6 pC/N and for PVDF/GO-PBA 26.2 pC/N showing significant improvement of the vibration sensing and thus providing very promising systems for structural health monitoring and data harvesting.

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Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data and achieve high-accuracy event recognition fully. We conducted experiments outdoors and collected more than 10,000 sets of vibration data. Through training, the CNN acquired the features of the raw DVS data and achieved the accurate classification of multiple vibration events. The recognition accuracy reached 99.9% based on the time-space data, a higher than used time-domain, frequency-domain, and time-frequency domain data. Moreover, considering that the performance of the DVS and the testing environment would change over time, we experimented again after one week to verify the method's generalization performance. The classification accuracy using the previously trained CNN is 99.2%, which is of great value in practical applications.

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Hundreds of kilometers of optical fibers are installed for optical meshes (OMs) to transmit data over long distances. The visualization of these deployed optical fibers is a highlighted issue because the conventional procedure can only measure the optical losses. Thus, this paper presents distributed vibration sensing (DVS) estimation mechanisms to visualize the optical fiber behavior installed for OMs which is not possible by conventional measurements. The proposed technique will detect the power of light inside the optical fiber, as well as different physical parameters such as the phase of transmitted light inside the thread, the frequency of vibration, and optical losses. The applicability of optical frequency domain reflectometry (OFDR) and optical time-domain reflectometry (OTDR) DVS techniques are validated theoretically for various state detection procedures in optical fibers. The simulation model is investigated in terms of elapsed time, the spectrum of a light signal, frequency, and the impact of many external physical accidents with optical fibers.

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Deformable 3D structures have emerged to revolutionize next-generation flexible electronics. In this study, a large out-of-plane deformable kirigami-based structure integrated with traditional functional materials has been successfully applied to wirelessly sense mechanical vibration and pressure. Unlike spiral inductor coils that lack mechanical stability, the inductor coils supported with polymer kirigami designs, comprising concentric circles with alternately connected hinges among the consecutive layers, offer exceptional mechanical stability. The wireless sensor shows a good linear response (Adj. R2 = 0.99) between the shift in resonant frequency as a function of extension. Moreover, the sensor device exhibits excellent cycling mechanical stability and minimal hysteresis, as confirmed by the experiments performed for over 5 d. An acceleration sensor (0-20 ms-2) with high linearity (Adj. R2 = 0.99) is introduced. Furthermore, a highly sensitive low-pressure sensor is demonstrated wirelessly in real time. Thus, the sensor can wirelessly monitor mechanical vibration and pressure. It can be applied for motion tracking, health monitoring, soft robotics, and deformation detection in battery-free deformable electronic devices.

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This paper is focused on the comparative study of the vibration sensing capabilities of poly(vinylidene fluoride) (PVDF) sheets. The main parameters such as molecular weight, initial sample thickness, stretching and poling were systematically applied, and their impact on sensing behavior was examined. The mechanical properties of prepared sheets were investigated via tensile testing on the samples with various initial thicknesses. The transformation of the α-phase to the electro-active ß-phase was analyzed using FTIR after applying stretching and poling procedures as crucial post-processing techniques. As a complementary method, the XRD was applied, and it confirmed the crystallinity data resulting from the FTIR analysis. The highest degree of phase transformation was found in the PVDF sheet with a moderate molecular weight (Mw of 275 kDa) after being subjected to the highest axial elongation (500%); in this case, the ß-phase content reached approximately 90%. Finally, the vibration sensing capability was systematically determined, and all the mentioned processing/molecular parameters were taken into consideration. The whole range of the elongations (from 50 to 500%) applied on the PVDF sheets with an Mw of 180 and 275 kDa and an initial thickness of 0.5 mm appeared to be sufficient for vibration sensing purposes, showing a d33 piezoelectric charge coefficient from 7 pC N-1 to 9.9 pC N-1. In terms of the d33, the PVDF sheets were suitable regardless of their Mw only after applying the elongation of 500%. Among all the investigated samples, those with an initial thickness of 1.0 mm did not seem to be suitable for vibration sensing purposes.

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The Pacinian corpuscle is a highly sensitive mammalian sensor cell that exhibits a unique band-pass sensitivity to vibrations. The cell achieves this band-pass response through the use of 20 to 70 elastic layers entrapping layers of viscous fluid. This paper develops and explores a scalable mechanical model of the Pacinian corpuscle and uses the model to predict the response of synthetic corpuscles, which could be the basis for future vibration sensors. The -3dB point of the biological cell is accurately mimicked using the geometries and materials available with off-the-shelf 3D printers. The artificial corpuscles here are constructed using uncured photoresist within structures printed in a commercial stereolithography (SLA) 3D printer, allowing the creation of trapped fluid layers analogous to the biological cell. Multi-layer artificial Pacinian corpuscles are vibration tested over the range of 20-3000 Hz and the response is in good agreement with the model.

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A vibration fiber sensor based on a fiber ring cavity laser and an interferometer based single-mode-multimode-single-mode (SMS) fiber structure is proposed and experimentally demonstrated. The SMS fiber sensor is positioned within the laser cavity, where the ring laser lasing wavelength can be swept to an optimized wavelength using a simple fiber loop design. To obtain a better signal-to-noise ratio, the ring laser lasing wavelength is tuned to the maximum gain region biasing point of the SMS transmission spectrum. A wide range of vibration frequencies from 10 Hz to 400 kHz are experimentally demonstrated. In addition, the proposed highly sensitive vibration sensor system was deployed in a field-test scenario for pipeline acoustic emission monitoring. An SMS fiber sensor is mounted on an 18" diameter pipeline, and vibrations were induced at different locations using a piezoelectric transducer. The proposed method was shown to be capable of real-time pipeline vibration monitoring.