Perovskite photodetector-based laser absorption spectroscopy for gas detection.

A gas detection method based on CH3NH3PbI3 (MAPbI3) and poly (3,4-ethylenedioxythiophene): poly (4-styrene sulfonate) (PEDOT:PSS) composite photodetectors (PDs) is proposed. The operation of the PD primarily relies on the photoelectric effect within the visible light band. Our study involves constructing a gas detection system based on tunable diode laser spectroscopy (TDLAS) and MAPbI3/PEDOT:PSS PD, and O2 was selected as the target analyte. The system has achieved a minimum detection limit (MDL) of 0.12% and a normalized noise equivalent absorption coefficient (NNEA) of 8.83 × 10-11 cm-1⋅W⋅Hz-1/2. Furthermore, the Allan deviation analysis results indicate that the system can obtain sensitivity levels as low as 0.058% over an averaging time of 328 seconds. This marks the first use of MAPbI3/PEDOT:PSS PD in gas detection based on TDLAS. Despite the detector's performance leaves much to be desired, this innovation offers a new approach to developing spectral based gas detection system.

Design and implementation of high sampling rate and multichannel wireless recorder for EEG monitoring and SSVEP response detection.

Introduction: The collection and process of human brain activity signals play an essential role in developing brain-computer interface (BCI) systems. A portable electroencephalogram (EEG) device has become an important tool for monitoring brain activity and diagnosing mental diseases. However, the miniaturization, portability, and scalability of EEG recorder are the current bottleneck in the research and application of BCI. Methods: For scalp EEG and other applications, the current study designs a 32-channel EEG recorder with a sampling rate up to 30 kHz and 16-bit accuracy, which can meet both the demands of scalp and intracranial EEG signal recording. A fully integrated electrophysiology microchip RHS2116 controlled by FPGA is employed to build the EEG recorder, and the design meets the requirements of high sampling rate, high transmission rate and channel extensive. Results: The experimental results show that the developed EEG recorder provides a maximum 30 kHz sampling rate and 58 Mbps wireless transmission rate. The electrophysiological experiments were performed on scalp and intracranial EEG collection. An inflatable helmet with adjustable contact impedance was designed, and the pressurization can improve the SNR by approximately 4 times, the average accuracy of steady-state visual evoked potential (SSVEP) was 93.12%. Animal experiments were also performed on rats, and spike activity was captured successfully. Conclusion: The designed multichannel wireless EEG collection system is simple and comfort, the helmet-EEG recorder can capture the bioelectric signals without noticeable interference, and it has high measurement performance and great potential for practical application in BCI systems.

Highly sensitive light-induced thermoelastic spectroscopy oxygen sensor with co-coupling photoelectric and thermoelastic effect of quartz tuning fork.

A light-induced thermoelastic spectroscopy (LITES) gas detection method based on CH3NH3PbI3 perovskite-coated quartz tuning fork (QTF) was proposed. By coating CH3NH3PbI3 thin film on the surface of ordinary QTF, a Schottky junction with silver electrodes was formed. The co-coupling of photoelectric effect and thermoelastic effect of CH3NH3PbI3-QTF results in a significant improvement in detection performance. The oxygen (O2) was select as the target analyte for measurement, and experimental results show that compared with the commercial standard QTF, the introduction of CH3NH3PbI3 perovskite Schottky junction increases the 2f signal amplitude and signal-to-noise ratio (SNR) by ∼106 times and ∼114 times, respectively. The minimum detection limit (MDL) of this LITES system is 260 ppm, and the corresponding normalized noise equivalent absorption coefficient (NNEA) is 9.21 × 10-13 cm-1·W·Hz-1/2. The Allan analysis of variance results indicate that when the average time is 564 s, the detection sensitivity can reach 83 ppm. This is the first time that QTF resonance detection has been combined with perovskite Schottky junctions for highly sensitive optical gas detection.

Quartz tuning fork-based high sensitive photodetector by co-coupling photoelectric and the thermoelastic effect of perovskite.

This paper reports a new strategy for enhancing the photoresponse of a quartz tuning fork (QTF). A deposited light absorbing layer on the surface of QTF could improve the performance only to a certain extent. Herein, a novel strategy is proposed to construct a Schottky junction on the QTF. The Schottky junction presented here consists of a silver-perovskite, which has extremely high light absorption coefficient and dramatically high power conversion efficiency. The co-coupling of the perovskite's photoelectric effect and its related QTF thermoelastic effect leads to a dramatic improvement in the radiation detection performance. Experimental results indicate that the CH3NH3PbI3-QTF obtains two orders of magnitude enhancement in sensitivity and SNR, and the 1σ detection limit was calculated to be 1.9 µW. It was the first time that the QTF resonance detection and perovskite Schottky junction was combined for optical detection. The presented design could be used in photoacoustic spectroscopy and thermoelastic spectroscopy for trace gas sensing.

SE-TCN network for continuous estimation of upper limb joint angles.

The maturity of human-computer interaction technology has made it possible to use surface electromyographic signals (sEMG) to control exoskeleton robots and intelligent prostheses. However, the available upper limb rehabilitation robots controlled by sEMG have the shortcoming of inflexible joints. This paper proposes a method based on a temporal convolutional network (TCN) to predict upper limb joint angles by sEMG. The raw TCN depth was expanded to extract the temporal features and save the original information. The timing sequence characteristics of the muscle blocks that dominate the upper limb movement are not apparent, leading to low accuracy of the joint angle estimation. Therefore, this study squeeze-and-excitation networks (SE-Net) to improve the network model of the TCN. Finally, seven movements of the human upper limb were selected for ten human subjects, recording elbow angle (EA), shoulder vertical angle (SVA), and shoulder horizontal angle (SHA) values during their movements. The designed experiment compared the proposed SE-TCN model with the backpropagation (BP) and long short-term memory (LSTM) networks. The proposed SE-TCN systematically outperformed the BP network and LSTM model by the mean RMSE values: by 25.0 and 36.8% for EA, by 38.6 and 43.6% for SHA, and by 45.6 and 49.5% for SVA, respectively. Consequently, its R2 values exceeded those of BP and LSTM by 13.6 and 39.20% for EA, 19.01 and 31.72% for SHA, and 29.22 and 31.89% for SVA, respectively. This indicates that the proposed SE-TCN model has good accuracy and can be used to estimate the angles of upper limb rehabilitation robots in the future.

[Follow control of upper limb rehabilitation training based on Kinect and NAO robot].

Gesture imitation is a common rehabilitation strategy in limb rehabilitation training. In traditional rehabilitation training, patients need to complete training actions under the guidance of rehabilitation physicians. However, due to the limited resources of the hospital, it cannot meet the training and guidance needs of all patients. In this paper, we proposed a following control method based on Kinect and NAO robot for the gesture imitation task in rehabilitation training. The method realized the joint angles mapping from Kinect coordination to NAO robot coordination through inverse kinematics algorithm. Aiming at the deflection angle estimation problem of the elbow joint, a virtual space plane was constructed and realized the accurate estimation of deflection angle. Finally, a comparative experiment for deflection angle of the elbow joint angle was conducted. The experimental results showed that the root mean square error of the angle estimation value of this method in right elbow transverse deflection and vertical deflection directions was 2.734° and 2.159°, respectively. It demonstrates that the method can follow the human movement in real time and stably using the NAO robot to show the rehabilitation training program for patients.

Real-Time Control of Intelligent Prosthetic Hand Based on the Improved TCN.

Intelligent prosthetic hand is an important branch of intelligent robotics. It can remotely replace humans to complete various complex tasks and also help humans to complete rehabilitation training. In human-computer interaction technology, the prosthetic hand can be accurately controlled by surface electromyography (sEMG). This paper proposes a new multichannel fusion scheme (MSFS) to extend the virtual channels of sEMG and improve the accuracy of gesture recognition. In addition, the Temporal Convolutional Network (TCN) in deep learning has been improved to enhance the performance of the network. Finally, the sEMG is collected by the Myo armband and the prosthetic hand is controlled in real time to validate the new method. The experimental results show that the method proposed in this paper can improve the accuracy of the control intelligent prosthetic hand, and the accuracy rate is 93.69%.

Ultra-broadband optical detection from the visible to the terahertz range using a miniature quartz tuning fork.

We report and experimentally demonstrate a novel, to the best of our knowledge, sensitive and wideband optical detection strategy based on the light-induced thermoelastic effect in a miniature quartz tuning fork (mQTF) with low stiffness prongs. Compared with a traditional QTF, the soft prongs of the mQTF result in improved sensitivity. Experimental results demonstrate that the mQTF exhibits ∼54-fold superior sensitivity compared to a QTF, and the mQTF sensor has an ultra-broadband optical response, ranging from visible light to terahertz wavelengths. Its response time reaches 11.7 ms, and the minimum noise equivalent power (NEP) is measured to be 2.2 × 10-9 W Hz-1/2 at room temperature. The mQTF exhibits advantages in its cost-effectiveness, sensitivity, and ultra-broadband response, and provides a promising approach for the detection of low-dose optical and terahertz-wave radiation.

Highly Stretchable and Self-Adhesive Elastomers Based on Polymer Chain Rearrangement for High-Performance Strain Sensors.

Polydimethylsiloxane (PDMS) has been widely used in many fields. However, the polymerization process of the siloxane chain is highly complex, and it is challenging to enhance the mechanical properties of PDMS elastomers significantly. We found that adding a small amount of polyoxyethylene lauryl ether (Brij-35) into siloxane polymers can result in B-PDMS elastomers with high tensile properties and strong adhesion. It is worth noting that this is the first study to improve the mechanical properties of PDMS using Brij-35. Here, we intensely studied a variety of process conditions that influence the cross-linking of PDMS, emphasizing the modification mechanism of the polymer chain. The hydroxyl groups in Brij-35 and the platinum catalyst in PDMS form a complex, which inhibits the cross-linking process of PDMS, not only forming a heterogeneous cross-linking network in the B-PDMS but also disentangling the strongly wound siloxane polymer chain, thereby rearranging the PDMS polymer chains. Furthermore, in order to prepare a strain sensor based on the B-PDMS elastomer under safe and convenient conditions, we prepared laser-scribed graphene powder (LSGP) by laser-scribing of graphene oxide (GO) films, and the LSGP and carbon nanotubes (CNTs) endowed the B-PDMS elastomers with excellent electrical properties. The sensor could firmly adhere to the skin and generate a high-quality response to a variety of human motions, and it could drive the robotic hand to grasp and lift objects accurately. The high-performance strain sensors based on B-PDMS have broad applications in medical sensing and biopotential measurement.

A lightweight double-channel depthwise separable convolutional neural network for multimodal fusion gait recognition.

Gait recognition is an emerging biometric technology that can be used to protect the privacy of wearable device owners. To improve the performance of the existing gait recognition method based on wearable devices and to reduce the memory size of the model and increase its robustness, a new identification method based on multimodal fusion of gait cycle data is proposed. In addition, to preserve the time-dependence and correlation of the data, we convert the time-series data into two-dimensional images using the Gramian angular field (GAF) algorithm. To address the problem of high model complexity in existing methods, we propose a lightweight double-channel depthwise separable convolutional neural network (DC-DSCNN) model for gait recognition for wearable devices. Specifically, the time series data of gait cycles and GAF images are first transferred to the upper and lower layers of the DC-DSCNN model. The gait features are then extracted with a three-layer depthwise separable convolutional neural network (DSCNN) module. Next, the extracted features are transferred to a softmax classifier to implement gait recognition. To evaluate the performance of the proposed method, the gait dataset of 24 subjects were collected. Experimental results show that the recognition accuracy of the DC-DSCNN algorithm is 99.58%, and the memory usage of the model is only 972 KB, which verifies that the proposed method can enable gait recognition for wearable devices with lower power consumption and higher real-time performance.

Graphene oxide and polydimethylsiloxane coated quartz tuning fork for improved sensitive near- and mid-infrared detection.

Sensitive and broadband infrared sensors are required for security and medical applications, as few can rapidly and sensitively detect infrared without uncooled devices. Here, we report a wideband optical-detection strategy based on the thermoelastic effect of a coating-enhanced quartz tuning fork (QTF) and study the feasibility of using an atomic force probe operating in contact mode to monitor the vibration. Graphene oxide (GO) and polydimethylsiloxane (PDMS) coating were applied on the QTF's surface to improve the light absorption and the thermal-mechanical conversion efficiency. Experimental results showed that the bi-layer coatings yielded a maximum gain factor of 8 in response amplitude and signal-to-noise ratio (SNR) than that of a bare QTF, respectively. Lasers with wavelengths of 1512 nm and 10.6 µm were used as the typical representative light source to test the photoresponse of the QTF detector. The device displays a broadband photoresponse covering the near-infrared to mid-infrared range at room temperature, high performance with the maximum photoresponsivity of 85.76 V·mW-1, and 1σ detection limit of 0.056 µW; the lowest noise equivalent power (NEP) of 1.35 nW·Hz-1/2 and 43.9 ms response speed is also achieved. The preparation process of detector is simple and easy to implement; the resulting device exhibits high responsivity and wide wavelength response ranging at least from 1512 to 10600 nm, compared with custom QTF; and the surface coating strategy potentially enables the construction of a new class of low-cost photodetection sensors operated at room temperature.

Polymer-coated quartz tuning fork for enhancing the sensitivity of laser-induced thermoelastic spectroscopy.

A novel laser-induced thermoelastic spectroscopy (LITES) sensor based on a polymer-coated quartz tuning fork (QTF) is reported. Two types of polymer films with different thicknesses are deposited on commercially available QTF to improve the conversion efficiency of laser energy deposition into vibration. CO2 was selected as the target analyte for validation measurements. The experimental results indicate that by introducing a polymer coating, a maximum gain factor of 3.46 and 3.21 is attained for the signal amplitude and signal-to-noise ratio (SNR), respectively, when compared to traditional LITES that using only a bare QTF. A minimum detectable concentration of 0.181% can be obtained, corresponding to a normalized noise equivalent absorption coefficient (NNEA) of 1.74×10-11 cm-1·W·Hz-1/2, and the measurement precision is approximately 0.06% with an averaging time of 200 s. Here, we show what we believe is the first demonstration of polymer coated QTF for LITES sensing, compared with custom QTF, the design has the virtues of lower cost, simple and easy-to-operate, is a promising new strategy for sensitive trace gas analysis.

Time-Frequency Maximal Information Coefficient Method and its Application to Functional Corticomuscular Coupling.

An important challenge in the study of functional corticomuscular coupling (FCMC) is an accurate capture of the coupling relationship between the cerebral cortex and the effector muscle. The coherence method is a linear analysis method, which has certain limitations in further revealing the nonlinear coupling between neural signals. Although mutual information (MI) and transfer entropy (TE) based on information theory can capture both linear and nonlinear correlations, the equitability of these algorithms is ignored and the nonlinear components of the correlation cannot be separated. The maximal information coefficient (MIC) is a suitable method to measure the coupling between neurophysiological signals. This study extends the MIC to the time-frequency domain, named time-frequency maximal information coefficient (TFMIC), to explore the FCMC in a specific frequency band. The effectiveness, equitability, and robustness of the algorithm on the simulation data was verified and compared with coherence, TE- and MI- based methods. Simulation results showed that the TFMIC could accurately detect the coupling for different functional relationships at low noise levels. The dorsiflexion experimental results revealed that the beta-band (14-30 Hz) significant coupling was observed at channels Cz, C4, FC4, and FCz. Additionally, the results showed that the coupling was higher in the alpha-band (8-13 Hz) and beta-band (14-30 Hz) than in the gamma-band (31-45 Hz). This might be related to a transition between sensorimotor states. Specifically, the nonlinear component of FCMC was also observed at channels Cz, C4, FC4, and FCz. This study expanded the research on nonlinear coupling components in FCMC.

Human Respiratory Monitoring Based on Schottky Resistance Humidity Sensors.

Two types of Schottky structure sensors (silicon nanowire (SiNW)/ZnO/reduced graphene oxide (rGO) and SiNW/TiO2/rGO) were designed, their humidity resistance characteristics were studied, and the sensors were applied to detect sleep apnea through breath humidity monitoring. The results show that the resistance of the sensors exhibited significant changes with increasing humidity, the response times of the two sensors within the relative humidity range of 23-97% were 49 s and 67 s, and the recovery times were 24 s and 43 s, respectively. Meanwhile, continuous breathing monitoring results indicate that the sensitivity of the sensors remained basically unchanged during 10 min of normal breathing and simulated apnea. The response of the sensor is still good after 30 days of use. We believe that the Schottky structure composite sensor is a very promising technology for human breathing monitoring.

Near-infrared tunable diode laser absorption spectroscopy-based determination of carbon dioxide in human exhaled breath.

A spectroscopic detection system for the accurate monitoring of carbon dioxide (CO2) in exhaled breath was realized by tunable diode laser absorption spectroscopy (TDLAS) in conjunction with a vertical-cavity surface-emitting laser (VCSEL) and a multipass cell with an effective optical path-length of 20 m. The VCSEL diode emitting light with an output power of 0.8 mW, covered the strong absorption line of CO2 at 6330.82 cm-1 by drive-current tuning. The minimum detectable concentration of 0.769‰ for CO2 detection was obtained, and a measurement precision of approximately 100 ppm was achieved with an integration time of 168 s. Real-time online measurements were carried out for the detection of CO2 expirograms from healthy subjects, different concentrations were obtained in dead space and alveolar gas. The exhaled CO2 increased significantly with the increasing physical activity, reaches its maximal value at the beginning of respiratory compensation and then decreased slightly until maximal exercise. The developed measurement system has a great potential to be applied in practice for the detection of pulmonary diseases associated with CO2 retention.

A New Approach to Fall Detection Based on Improved Dual Parallel Channels Convolutional Neural Network.

Falls are the major cause of fatal and non-fatal injury among people aged more than 65 years. Due to the grave consequences of the occurrence of falls, it is necessary to conduct thorough research on falls. This paper presents a method for the study of fall detection using surface electromyography (sEMG) based on an improved dual parallel channels convolutional neural network (IDPC-CNN). The proposed IDPC-CNN model is designed to identify falls from daily activities using the spectral features of sEMG. Firstly, the classification accuracy of time domain features and spectrograms are compared using linear discriminant analysis (LDA), k-nearest neighbor (KNN) and support vector machine (SVM). Results show that spectrograms provide a richer way to extract pattern information and better classification performance. Therefore, the spectrogram features of sEMG are selected as the input of IDPC-CNN to distinguish between daily activities and falls. Finally, The IDPC-CNN is compared with SVM and three different structure CNNs under the same conditions. Experimental results show that the proposed IDPC-CNN achieves 92.55% accuracy, 95.71% sensitivity and 91.7% specificity. Overall, The IDPC-CNN is more effective than the comparison in accuracy, efficiency, training and generalization.

Dynamic Balance Measurement and Quantitative Assessment Using Wearable Plantar-Pressure Insoles in a Pose-Sensed Virtual Environment.

The center of plantar pressure (COP) reflects the dynamic balance of subjects to a certain extent. In this study, wearable pressure insoles are designed, body pose measure is detected by the Kinect sensor, and a balance evaluation system is formulated. With the designed games for the interactive actions, the Kinect sensor reads the skeletal poses to judge whether the desired action is performed, and the pressure insoles simultaneously collect the plantar pressure data. The COP displacement and its speed are calculated to determine the body sway and the ability of balance control. Significant differences in the dispersion of the COP distribution of the 12 subjects have been obtained, indicating different balancing abilities of the examined subjects. A novel assessment process is also proposed in the paper, in which a correlation analysis is made between the de facto sit-to-stand (STS) test and the proposed method; the Pearson and Spearman correlations are also conducted, which reveal a significant positive correlation. Finally, four undergraduate volunteers with a right leg sports injury participate in the experiments. The experimental results show that the normal side and abnormal side have significantly different characters, suggesting that our method is effective and robust for balance measurements.

A Graphene-Based Flexible Pressure Sensor with Applications to Plantar Pressure Measurement and Gait Analysis.

In the present study, we propose and develop a flexible pressure sensor based on the piezoresistive effect of multilayer graphene films on polyester textile. The pressure response results from the deformation of graphene conductive network structure and the changes in resistance. Here, we show that the graphene pressure sensor can achieve a sensitivity value of 0.012 kPa - 1 , the measurement range can be as high as 800 kPa, and the response time can reach to 50 ms. Subsequently, a stable in-shoe wireless plantar pressure measurement system is developed and dynamic pressure distribution is acquired in real-time. Overall, the graphene textile pressure sensor has the advantage of wide dynamic range, flexibility and comfort, which provides the high possibility for footwear evaluation, clinical gait analysis and pathological foot diagnosis.

Flexible Graphene Electrodes for Prolonged Dynamic ECG Monitoring.

This paper describes the development of a graphene-based dry flexible electrocardiography (ECG) electrode and a portable wireless ECG measurement system. First, graphene films on polyethylene terephthalate (PET) substrates and graphene paper were used to construct the ECG electrode. Then, a graphene textile was synthesized for the fabrication of a wearable ECG monitoring system. The structure and the electrical properties of the graphene electrodes were evaluated using Raman spectroscopy, scanning electron microscopy (SEM), and alternating current impedance spectroscopy. ECG signals were then collected from healthy subjects using the developed graphene electrode and portable measurement system. The results show that the graphene electrode was able to acquire the typical characteristics and features of human ECG signals with a high signal-to-noise (SNR) ratio in different states of motion. A week-long continuous wearability test showed no degradation in the ECG signal quality over time. The graphene-based flexible electrode demonstrates comfortability, good biocompatibility, and high electrophysiological detection sensitivity. The graphene electrode also combines the potential for use in long-term wearable dynamic cardiac activity monitoring systems with convenience and comfort for use in home health care of elderly and high-risk adults.

Targeted Fe-filled carbon nanotube as a multifunctional contrast agent for thermoacoustic and magnetic resonance imaging of tumor in living mice.

Microwave-induced thermoacoustic imaging (TAI) can map the microwave absorption distribution of targets, which depends on the electrical and magnetic properties. Although carbon nanotubes (CNTs) with good electrical properties have been used as TAI contrast agents, the negligible magnetic absorption hinders its application for sensitive detection. In order to exploit CNTs with electrical and magnetic absorption properties as agent of TAI, the ferromagnetic material-filled multi-walled CNTs (MMWCNTs) are investigated. In this study, the folic acid conjugated plain multiwalled CNTs (MWCNTs) and MMWCNTs were injected through the tail-vein of mice separately, and TAI and magnetic resonance imaging (MRI) were performed. The results show the MMWCNTs can clearly image the size and edge of the tumor with the TAI contrast enhancement of 67% and T2 signal intensity decrease of four fifths compared to MWCNTs. This study demonstrated the hybrid particles have the potential to be a high-sensitive contrast agent for accurate tumor detection. From the Clinical Editor: Novel imaging modalities are emerging. Microwave-induced thermoacoustic imaging (TAI) relies on the absorption distribution of microwave of targets. In this article the authors investigate the use of ferromagnetic material-filled multi-walled CNTs as contrast agents for both TAI and MRI in an in-vivo model for tumors. The positive findings would imply that the application of dual-modality probe could provide more accurate imaging for the clinical setting.