Synergetic gait prediction and compliant control of SEA-driven knee exoskeleton for gait rehabilitation.

In recent years, lower limb exoskeletons have achieved satisfactory clinical curative effects in rehabilitating stroke patients. Furthermore, generating individualized trajectories for each patient and avoiding secondary injury in rehabilitation training are important issues. This paper explores the utilization of series elastic actuator (SEA) to deliver compliant force and enhance impact resistance in human-robot interaction, and we present the design of novel knee exoskeleton driven by SEA. Subsequently, the novel gait trajectory prediction method and compliant control method are proposed. The attention-based CNN-LSTM model is established to generate personalized gait trajectories for affected limbs, in which the spatial-temporal attention mechanism is adopted to improve the prediction accuracy. The compliant control strategy is proposed to nonlinearly and adaptively tune impedance parameters based on artificial potential field (APF) method, and active rehabilitation training is carried out in the coordination space to guarantee patient safety. The experimental results based on four healthy subjects demonstrated that synergetic gait prediction model could satisfactorily characterize the coordination movement with higher accuracy. The compliant control could limit the patient's movement in the safe coordination tunnel while considering personalization and flexibility.

Attention-based multi-semantic dynamical graph convolutional network for eeg-based fatigue detection.

Introduction: Establishing a driving fatigue monitoring system is of utmost importance as severe fatigue may lead to unimaginable consequences. Fatigue detection methods based on physiological information have the advantages of reliable and accurate. Among various physiological signals, EEG signals are considered to be the most direct and promising ones. However, most traditional methods overlook the functional connectivity of the brain and fail to meet real-time requirements. Methods: To this end, we propose a novel detection model called Attention-Based Multi-Semantic Dynamical Graph Convolutional Network (AMD-GCN). AMD-GCN consists of a channel attention mechanism based on average pooling and max pooling (AM-CAM), a multi-semantic dynamical graph convolution (MD-GC), and a spatial attention mechanism based on average pooling and max pooling (AM-SAM). AM-CAM allocates weights to the input features, helping the model focus on the important information relevant to fatigue detection. MD-GC can construct intrinsic topological graphs under multi-semantic patterns, allowing GCN to better capture the dependency between physically connected or non-physically connected nodes. AM-SAM can remove redundant spatial node information from the output of MD-GC, thereby reducing interference in fatigue detection. Moreover, we concatenate the DE features extracted from 5 frequency bands and 25 frequency bands as the input of AMD-GCN. Results: Finally, we conduct experiments on the public dataset SEED-VIG, and the accuracy of AMD-GCN model reached 89.94%, surpassing existing algorithms. Discussion: The findings indicate that our proposed strategy performs more effectively for EEG-based driving fatigue detection.

AI-Assisted Disease Monitoring Using Stretchable Polymer-Based Sensors.

Flexible sensors have attracted significant attention for medical applications. Herein, an AI-assisted stretchable polymer-based (AISP) sensor has been developed based on the Beer-Lambert law for disease monitoring and telenursing. Benefiting from the use of superior polymer materials, the AISP sensor features a high tensile strain of up to 100%, durability of >10,000 tests, excellent waterproofness, and no effect of temperature (1.6-60.9 °C). Such advantages support the capability that the AISP can be flexibly pasted on the skin surface as a wearable device for real-time monitoring of multiple physiological parameters. An AISP sensor-based swallowing recognition technique has been proposed with a high accuracy of up to 88.89%. Likewise, it has been expanded to a remote nursing assistance system to meet critical patients' physiological requirements and daily care. The hands-free communication experiment and robot control applications have also been successfully conducted based on the constructed system. Such merits demonstrate its potential as a medical toolkit and indicate promise for intelligent healthcare.

A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model.

For remanufacturing to be more economically attractive, there is a need to develop automatic disassembly and automated visual detection methods. Screw removal is a common step in end-of-life product disassembly for remanufacturing. This paper presents a two-stage detection framework for structurally damaged screws and a linear regression model of reflection features that allows the detection framework to be conducted under uneven illumination conditions. The first stage employs reflection features to extract screws together with the reflection feature regression model. The second stage uses texture features to filter out false areas that have reflection features similar to those of screws. A self-optimisation strategy and weighted fusion are employed to connect the two stages. The detection framework was implemented on a robotic platform designed for disassembling electric vehicle batteries. This method allows screw removal to be conducted automatically in complex disassembly tasks, and the utilisation of the reflection feature and data learning provides new ideas for further research.

Bioinspired Stretchable Fiber-Based Sensor toward Intelligent Human-Machine Interactions.

Wearable integrated sensing devices with flexible electronic elements exhibit enormous potential in human-machine interfaces (HMI), but they have limitations such as complex structures, poor waterproofness, and electromagnetic interference. Herein, inspired by the profile of Lindernia nummularifolia (LN), a bionic stretchable optical strain (BSOS) sensor composed of an LN-shaped optical fiber incorporated with a stretchable substrate is developed for intelligent HMI. Such a sensor enables large strain and bending angle measurements with temperature self-compensation by the intensity difference of two fiber Bragg gratings' (FBGs') center wavelength. Such configurations enable an excellent tensile strain range of up to 80%, moreover, leading to ultrasensitivity, durability (≥20,000 cycles), and waterproofness. The sensor is also capable of measuring different human activities and achieving HMI control, including immersive virtual reality, robot remote interactive control, and personal hands-free communication. Combined with the machine learning technique, gesture classification can be achieved using muscle activity signals captured from the BSOS sensor, which can be employed to obtain the motion intention of the prosthetic. These merits effectively indicate its potential as a solution for medical care HMI and show promise in smart medical and rehabilitation medicine.

Micro-Bubble F-P Cavity and FBG Cascade Structure-Based Pressure Sensor With Temperature Self-Compensation for Minimally Invasive Surgery.

This paper presents a high-sensitivity optical fiber pressure sensor with temperature self-compensation for pressure measurement in minimally invasive surgery through a cascade structure of Fabry-Perot (F-P) interferometer and fiber Bragg grating (FBG). A micro-bubble is configured at the tip of the fiber to form an F-P cavity that is sensitive to pressure. A loose optical fiber inscribed with an FBG element is cascaded with the F-P cavity leading to temperature compensation for the designed sensor. The sensing theoretical model has been derived and combined with the finite element method (FEM) simulation the sensor structure has been determined as well. Fabrication processing of the designed sensor has been optimized and explored by experiments. Calibration experiment results indicate that the pressure sensitivity of the designed sensor is 8.93 pm/kPa, which is consistent with the simulated value. The temperature coupled error is less than 3.89% leading to a capability for temperature self-compensation. Several heart-vascular simulation experiments have been carried out to investigate the dynamic performance of the designed sensor, which shows the measured pressure errors within this confidence interval of [-2.56%, 2.54%] correspond to high confidence of 0.95. An in-vivo intracranial pressure (ICP) measurement experiment on the rat brain has been conducted to further validate the feasibility and effectiveness of the designed sensor.

A Composite Fabry-Perot Interferometric Sensor with the Dual-Cavity Structure for Simultaneous Measurement of High Temperature and Strain.

In this paper, an optical fiber composite Fabry-Perot interferometric (CFPI) sensor capable of simultaneous measurement of high temperature and strain is presented. The CFPI sensor consists of a silica-cavity intrinsic Fabry-Perot interferometer (IFPI) cascading an air-cavity extrinsic Fabry-Perot interferometer (EFPI). The IFPI is constructed at the end of the transmission single-mode fiber (SMF) by splicing a short piece of photonic crystal fiber (PCF) to SMF and then the IFPI is inserted into a quartz capillary with a reflective surface to form a single-ended sliding EFPI. In such a configuration, the IFPI is only sensitive to temperature and the EFPI is sensitive to strain, which allows the achieving of temperature-compensated strain measurement. The experimental results show that the proposed sensor has good high-temperature resistance up to 1000 °C. Strain measurement under high temperatures is demonstrated for high-temperature suitability and stable strain response. Featuring intrinsic safety, compact structure and small size, the proposed CFPI sensor may find important applications in the high-temperature harsh environment.

Performance of 3D-Printed Continuous-Carbon-Fiber-Reinforced Plastics with Pressure.

Fused Deposition Modeling (FDM) has been investigated as a low-cost manufacturing method for fiber-reinforced composites. The traditional and mature technology for manufacturing continuous-carbon-fiber-reinforced plastics is Automated Fiber Placement (AFP), which uses a consolidation roller and an autoclave process to improve the quality of parts. Compared to AFP, FDM is simple in design and operation but lacks the ability to pressurize and heat the model. In this work, a novel method for printing continuous carbon-fiber-reinforced plastics with a pressure roller was investigated. First, the path processing of the pressure roller was researched, which will reduce the number of rotations of the pressure roller and increase the service life of the equipment and the efficiency of printing. Thereafter, three specimens were printed under different pressures and the tensile and bending strength of specimens were tested. The tensile strength and bending strength of specimens were enhanced to 644.8 MPa and 401.24 MPa by increasing the pressure, compared to the tensile strength and bending strength of specimens without pressure of 109.9 MPa and 163.13 MPa. However, excessive pressure will destroy the path of the continuous carbon fiber (CCF) and the surface quality of the model, and may even lead to printing failure.

The Detection of the Pipe Crack Utilizing the Operational Modal Strain Identified from Fiber Bragg Grating.

The small and light-weight pipeline is widely used in hydraulic system for aerospace engineering. The crack is one of the most common failures in the pipelines so that its incipient detection can further avoid the catastrophic damage of the piping system. The electrical and piezoelectric sensors are conventionally used for the structural health monitoring (SHM), while these are not suitable for the cascaded pipelines in harsh environment because the added mass will change the modal characteristics of the cascaded pipelines. The Fiber Bragg Grating (FBG) sensor with light-weight, multiplexed, and anti-electromagnetic interference properties, are employed to obtain the modal strain transmissibility with a novel diagram of the operational modal analysis (OMA). Based on the OMA an enhanced damage indicator is proposed to detect the crack. After going through analytical modeling, finite element modeling (FEM) and its corresponding experiments, it is concluded that the presented method is effective and accurate to detect and locate the crack.

Inverse Finite Element Method for Reconstruction of Deformation in the Gantry Structure of Heavy-Duty Machine Tool Using FBG Sensors.

The deformation of the gantry structure in heavy-duty machine tools is an important factor that affects machining accuracy. In order to realize real-time monitoring of the deformation of the gantry structure, which is statically indeterminate and complex in shape, the reconstruction algorithm based on inverse Finite Element Method (iFEM) is proposed and fiber Bragg grating (FBG) sensors are used to measure strain data. The elements of the gantry structure are divided and the displacement functions of each element are determined. The shape function is obtained by substituting degree of freedoms (DOF) of element nodes into displacement functions. Through a differential method, the relation between strain and DOF of element nodes is established by the strain matrices. Subsequently, the DOF of element nodes are obtained by minimizing an error functional defined as the least-squares error between the analytic strain data and the corresponding experimental strains. Considering coordinate transformation and problem-specific displacement boundary conditions, the total deformation of the gantry structure is obtained. Following this, the experiment was carried out. The deformation simulated by ANSYS was used to replace the experimentally measured deformation and then compared with the deformation reconstructed by iFEM under the same loading condition. The accuracy of iFEM for reconstructing deformation of the gantry structure in heavy-duty machine tools is verified. It provides a new view for improving the machining precision of heavy-duty machine tools.

Sensitivity Enhancement of FBG-Based Strain Sensor.

A novel fiber Bragg grating (FBG)-based strain sensor with a high-sensitivity is presented in this paper. The proposed FBG-based strain sensor enhances sensitivity by pasting the FBG on a substrate with a lever structure. This typical mechanical configuration mechanically amplifies the strain of the FBG to enhance overall sensitivity. As this mechanical configuration has a high stiffness, the proposed sensor can achieve a high resonant frequency and a wide dynamic working range. The sensing principle is presented, and the corresponding theoretical model is derived and validated. Experimental results demonstrate that the developed FBG-based strain sensor achieves an enhanced strain sensitivity of 6.2 pm/µÎµ, which is consistent with the theoretical analysis result. The strain sensitivity of the developed sensor is 5.2 times of the strain sensitivity of a bare fiber Bragg grating strain sensor. The dynamic characteristics of this sensor are investigated through the finite element method (FEM) and experimental tests. The developed sensor exhibits an excellent strain-sensitivity-enhancing property in a wide frequency range. The proposed high-sensitivity FBG-based strain sensor can be used for small-amplitude micro-strain measurement in harsh industrial environments.

Diaphragm Based Fiber Bragg Grating Acceleration Sensor with Temperature Compensation.

A novel fiber Bragg grating (FBG) sensing-based acceleration sensor has been proposed to simultaneously decouple and measure temperature and acceleration in real-time. This design applied a diaphragm structure and utilized the axial property of a tightly suspended optical fiber, enabling improvement in its sensitivity and resonant frequency and achieve a low cross-sensitivity. The theoretical vibrational model of the sensor has been built, and its design parameters and sensing properties have been analyzed through the numerical analysis. A decoupling method has been presented with consideration of the thermal expansion of the sensor structure to realize temperature compensation. Experimental results show that the temperature sensitivity is 8.66 pm/°C within the range of 30-90 °C. The acceleration sensitivity is 20.189 pm/g with a linearity of 0.764% within the range of 5~65 m/s². The corresponding working bandwidth is 10~200 Hz and its resonant frequency is 600 Hz. This sensor possesses an excellent impact resistance for the cross direction, and the cross-axis sensitivity is below 3.31%. This implementation can avoid the FBG-pasting procedure and overcome its associated shortcomings. The performance of the proposed acceleration sensor can be easily adjusted by modifying their corresponding physical parameters to satisfy requirements from different vibration measurements.

Strain Modal Analysis of Small and Light Pipes Using Distributed Fibre Bragg Grating Sensors.

Vibration fatigue failure is a critical problem of hydraulic pipes under severe working conditions. Strain modal testing of small and light pipes is a good option for dynamic characteristic evaluation, structural health monitoring and damage identification. Unique features such as small size, light weight, and high multiplexing capability enable Fibre Bragg Grating (FBG) sensors to measure structural dynamic responses where sensor size and placement are critical. In this paper, experimental strain modal analysis of pipes using distributed FBG sensors ispresented. Strain modal analysis and parameter identification methods are introduced. Experimental strain modal testing and finite element analysis for a cantilever pipe have been carried out. The analysis results indicate that the natural frequencies and strain mode shapes of the tested pipe acquired by FBG sensors are in good agreement with the results obtained by a reference accelerometer and simulation outputs. The strain modal parameters of a hydraulic pipe were obtained by the proposed strain modal testing method. FBG sensors have been shown to be useful in the experimental strain modal analysis of small and light pipes in mechanical, aeronautic and aerospace applications.

A Fiber Bragg Grating Sensing-Based Micro-Vibration Sensor and Its Application.

This paper proposes a fiber Bragg grating sensing-based micro-vibration sensor. The optical fiber has been directly treated as an elastomer to design the micro-vibration sensor, which possesses two FBGs. The mass is fixed on the middle of the fiber, and the vertical vibration of the mass has been converted into the axial tension/compression of the fiber. The principle of the sensor has been introduced, and the experiment conclusions show that the sensor sensitivity is 2362 pm/g within the range of 200-1200 mm/s², which is consistent with theoretical analysis sensitivity of 2532.6 pm/g, and it shows an excellent linearity of 1.376%, while the resonant frequency of the sensor is 34 Hz, and the flat frequency range resides in the 0-22 Hz range. When used to measure micro-vibrations, its measured frequency relative error is less than 1.69% compared with the values acquired with a MEMS accelerometer, and the amplitude values of its measured vibration signal are consistent with the MEMS accelerometer under different excitation conditions too, so it can effectively realize the micro-vibration measurements.

A MUSIC-based method for SSVEP signal processing.

The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.

A Multiuser Manufacturing Resource Service Composition Method Based on the Bees Algorithm.

In order to realize an optimal resource service allocation in current open and service-oriented manufacturing model, multiuser resource service composition (RSC) is modeled as a combinational and constrained multiobjective problem. The model takes into account both subjective and objective quality of service (QoS) properties as representatives to evaluate a solution. The QoS properties aggregation and evaluation techniques are based on existing researches. The basic Bees Algorithm is tailored for finding a near optimal solution to the model, since the basic version is only proposed to find a desired solution in continuous domain and thus not suitable for solving the problem modeled in our study. Particular rules are designed for handling the constraints and finding Pareto optimality. In addition, the established model introduces a trusted service set to each user so that the algorithm could start by searching in the neighbor of more reliable service chains (known as seeds) than those randomly generated. The advantages of these techniques are validated by experiments in terms of success rate, searching speed, ability of avoiding ingenuity, and so forth. The results demonstrate the effectiveness of the proposed method in handling multiuser RSC problems.

A Fiber Bragg Grating Sensing Based Triaxial Vibration Sensor.

A fiber Bragg grating (FBG) sensing based triaxial vibration sensor has been presented in this paper. The optical fiber is directly employed as elastomer, and the triaxial vibration of a measured body can be obtained by two pairs of FBGs. A model of a triaxial vibration sensor as well as decoupling principles of triaxial vibration and experimental analyses are proposed. Experimental results show that: sensitivities of 86.9 pm/g, 971.8 pm/g and 154.7 pm/g for each orthogonal sensitive direction with linearity are separately 3.64%, 1.50% and 3.01%. The flat frequency ranges reside in 20-200 Hz, 3-20 Hz and 4-50 Hz, respectively; in addition, the resonant frequencies are separately 700 Hz, 40 Hz and 110 Hz in the x/y/z direction. When the sensor is excited in a single direction vibration, the outputs of sensor in the other two directions are consistent with the outputs in the non-working state. Therefore, it is effectively demonstrated that it can be used for three-dimensional vibration measurement.

Pasted type distributed two-dimensional fiber Bragg grating vibration sensor.

A pasted type distributed two-dimensional fiber Bragg grating (FBG) vibration sensor has been proposed and studied in this paper. The optical fiber is directly considered as an elastomer. The two-dimensional vibration can be separated by subtraction/addition of two FBGs' center wavelength shift. The principle of the sensor as well as numerical simulation and experimental analyses are presented. Experimental results show that the resonant frequencies of the sensor x/y main vibration direction are separately 1300/20.51 Hz, which are consistent with the numerical simulation analysis result. The flat frequency range resides in 10-750 Hz and 3-12 Hz, respectively; dynamic range is 28.63 dB; in the x main vibration direction, the sensor's sensitivity is 32.84 pm/g, with linearity 3.91% in the range of 10-60 m/s(2), while in the y main vibration direction, the sensor's sensitivity is 451.3 pm/g, with linearity 1.92% in the range of 1.5-8 m/s(2). The cross sensitivity is 3.91%. Benefitting from the two dimensional sensing properties, it can be used in distributed two-dimensional vibration measurement.

A non-contact fiber Bragg grating vibration sensor.

A non-contact vibration sensor based on fiber Bragg grating (FBG) sensing has been proposed and studied in this paper. The principle of the sensor as well as simulation and experimental analyses are introduced. When the distance between the movable head and the measured shaft changed, the diaphragm deformed under magnetic coupling of the permanent magnet on the measured magnetic shaft. As a result, the center wavelength of the FBG connected to the diaphragm changed, based on which the vibration displacement of the rotating shaft could be obtained. Experimental results show that the resonant frequency of the sensor is about 1500 Hz and the working band ranges within 0-1300 Hz, which is consistent with the simulation analysis result; the sensitivity is -1.694 pm/µm and the linearity is 2.92% within a range of 2-2.4 mm. It can be used to conduct non-contact measurement on the vibration of the rotating shaft system.