Motion Signals Research Articles

Wood derived conductive aerogel with ultrahigh specific surface area and exceptional mechanical flexibility for pressure sensing

Conductive nanocellulose aerogels possess durability and sensitivity by using as piezoresistive sensors. Those aerogels are mostly prepared via a “bottom-up” strategy by using nanocellulose or dissolved and regenerated cellulose as raw materials. However, there is a pressing need to further enhance their sustainability and economic viability. To alleviate these issues, cellulose aerogel can alternatively be made via a “top-down” technique that entails the direct removal of contaminants from naturally porous biomass. Herein, conductive aerogels with exceptional mechanical flexibility and ultrahigh specific surface area were created. Delignified wood was partially dissolved and regenerated to created nanocellulose fibrils with spider web structure inside the pores of wood. After modification with silane coupling agent and Carbon Nanotubes (CNTs), flexible conductive wood aerogel (C-WA) were obtained. The specific surface area of C-WA was as high as 333.82 m2/g, which was 45 times of that of wood. C-WA could maintain its good resilience even after 1000 cycles, showing high compression resilience and excellent fatigue resistance (stress retention rate was 87.22 %, height retention rate was 99.5 %). The utilization of C-WA as a pressure sensor demonstrated remarkable attributes, including ultra-fast response and stability within the designated range. Furthermore, it exhibited exceptional sensing performance even after enduring 2000 stable cycles. The system is capable of real-time, accurate, and efficient detection of human motion signals, showcasing immense potential for future applications in wearable electronic products.

High-strength self-healing multi-functional hydrogels with worm-like surface through hydrothermal-freeze-thaw method

Soft self-healing materials are promising candidates for flexible electronic devices due to their exceptional compatibility, extensibility, and self-restorability. Generally, these materials suffer from low tensile strength and susceptibility to fracture because of the restricted microstructure design. Herein, we propose a hydrothermal-freeze-thaw method to construct high-strength self-healing hydrogels with even interconnected networks and distinctive wrinkled surfaces. The integration of the wrinkled outer surface with the three-dimensional internal network confers the self-healing hydrogel with enhanced mechanical strength. This hydrogel achieves a tensile strength of 223 kPa, a breaking elongation of 442%, an adhesion strength of 57.6 kPa, and an adhesion energy of 237.2 J m-2. Meanwhile, the hydrogel demonstrates impressive self-repair capability (repair efficiency: 93%). Moreover, the density functional theory (DFT) calculations are used to substantiate the stable existence of hydrogen bonding between the PPPBG hydrogel and water molecules which ensures the durability of the PPPBG hydrogel for long-term application. The measurements demonstrate that this multifunctional hydrogel possesses the requisite sensitivity and durability to serve as a strain sensor, which monitors a spectrum of motion signals including subtle vocalizations, pronounced facial expressions, and limb articulations. This work presents a viable strategy for healthcare monitoring, soft robotics, and interactive electronic skins.

Self-powered highly stretchable ferroelectret nanogenerator towards intelligent sports

Ferroelectret nanogenerators (FENGs), recognized for their porous structures that facilitate charge retention, thereby creating giant electric dipoles and exhibiting remarkable piezoelectric properties, are utilized in the development of various flexible transducers. However, despite their flexibility, most developed ferroelectret nanogenerators lack adequate stretchability and satisfactory transverse piezoelectric properties, significantly inhibiting their widespread deployment in wearable or skin-mounted electronics. Here, we introduce a highly stretchable ferroelectret nanogenerator (HS-FENG) built from laser-induced graphene (LIG), Ecoflex and anhydrous ethanol, demonstrating exceptional flexibility and stretchability, along with longitudinal and transverse piezoelectric effects. The stretchability of HS-FENG can reach a record of 468 %, while the quasi-static piezoelectric coefficients d33 and d31 are approximately 120 pC/N and 70 pC/N, respectively. To our knowledge, this is the first demonstration of the developed FENG with remarkably high stretchability. Furthermore, leveraging the performance of the created HS-FENG, we construct a skin-mounted intelligent kinesiology tape capable of effectively monitoring motion signals from human muscles and joints, thereby offering a deeper understanding of movement for users across different levels of physical activity, from professional athletes to individuals undergoing rehabilitation. The development of intelligent kinesiology tape exemplifies the potential of HS-FENG technology in enhancing professional athletic training and personalized healthcare. It contributes to the advancement of inconspicuous skin-mounted biomechanical feedback systems and human-machine interfaces, marking progress in the field.

Genome-Wide Identification of the Remorin Gene Family in Poplar and Their Responses to Abiotic Stresses.

The Remorin (REM) gene family is a plant-specific, oligomeric, filamentous family protein located on the cell membrane, which is important for plant growth and stress responses. In this study, a total of 22 PtREMs were identified in the genome of Populus trichocarpa. Subcellular localization analysis showed that they were predictively distributed in the cell membrane and nucleus. Only five PtREMs members contain both Remorin_C- and Remorin_N-conserved domains, and most of them only contain the Remorin_C domain. A total of 20 gene duplication pairs were found, all of which belonged to fragment duplication. Molecular evolutionary analysis showed the PtREMs have undergone purified selection. Lots of cis-acting elements assigned into categories of plant growth and development, stress response, hormone response and light response were detected in the promoters of PtREMs. PtREMs showed distinct gene expression patterns in response to diverse stress conditions where the mRNA levels of PtREM4.1, PtREM4.2 and PtREM6.11 were induced in most cases. A co-expression network centered by PtREMs was constructed to uncover the possible functions of PtREMs in protein modification, microtube-based movement and hormone signaling. The obtained results shed new light on understanding the roles of PtREMs in coping with environmental stresses in poplar species.

Late feature fusion using neural network with voting classifier for Parkinson’s disease detection

Parkinson’s disease (PD) is classified as a neurological, progressive illness brought on by cell death in the posterior midbrain. Early PD detection will assist doctors in reducing the disease’s consequences. A collection of skilled models that may be applied to regression as well as classification is known as artificial intelligence (AI). PD can be detected using a variety of dataset formats, including text, speech, and picture datasets. For the purpose of classifying Parkinson’s disease, this study suggests merging deep with machine learning recognition approaches. The three primary components of the suggested approach are designed to enhance the accuracy of Parkinson’s disease early diagnosis. These sections cover the topics of categorising, combining, and separating. Convolutional Neural Networks (CNN) as well as attention procedures are used to create feature extractors. The related motion signals are fed to a combination of convolutional neural network and long-short-memory model for feature extraction. Besides, for the classification of patients from non-suffers of Parkinson’s disease, Random Forest, Logistic Regression, Support Vector Machine, Extreme Boot Classifier, and voting classifier were used. Our result shows that for the PD handwriting and related motion datasets, using the proposed CNN with an attention and voting classifier yields 99.95% accuracy, 99.99% precision, 99.98% sensitivity, and 99.95% F1-score. Based on these results, it is warranted to conclude that the proposed methodology of feature extraction from photos of handwriting and relating motor symptoms, fusing of those features, and following it with a voting classifier yields excellent results for PD classification.

An efficient approach for EMG controlled pattern recognition system based on MUAP identification and segregation

An Electromyography (EMG) based pattern recognition system constitutes various steps of signal processing and control engineering from signal acquisition to real-time control. Efficient control of external devices largely depends on the signal processing steps executed before the final output. This work presents a new approach to signal processing using Motor Unit Action Potential (MUAP) based signal decomposition and segmentation. An MUAP is a neurological response during muscle contraction. Due to the higher contact area of surface electrodes, MUAPs from multiple muscles are captured. An MUAP generated from a single muscle usually has identical waveshapes and similar discharging rates and usually lasts for 8–15 ms. These are known as primary MUAPs. The proposed algorithm identifies and uses the primary observed MUAPs for feature extraction and classification. Firstly, noise signals are eliminated by a determined noise margin, which also separates the active muscle movement signals. Next, a novel MUAP identification algorithm is implemented to detect the MUAP trains. Then, identified primary MUAPs are used to make segments with variable widths to extract feature vectors. Based on the correlation score of all the primary MUAPs, the segmentation is performed, which results in segmentation width varying from 110–200 ms. The achieved segmentation width is lesser than the conventional overlapping and non-overlapping methods — the proposed approach results in a 20 to 50% reduction in the segmentation width. Four different classifiers are tested during the machine learning stage to investigate the performance of the proposed approach. The obtained feature sets are then used to train the Linear Discriminant Analysis (LDA), K-Nearest Neighbor (kNN), Decision Tree (DT), and Random Forest (RF) classifiers. The classifiers are tested with precision, recall, F1 score, and accuracy. The kNN and DT classifiers performed better than the LDA and RF classifiers. The maximum precision and recall are 100% while the maximum achieved accuracy is 98.56%. The comparative results show higher accuracy even at lower segmentation widths than the conventional constant window scheme. The kNN and DT classifiers provide a 5% to 15% increment in accuracy compared to the constant window segmentation-based approach.

MXene-based self-adhesive, ultrasensitive, highly tough flexible hydrogel pressure sensors for motion monitoring and robotic tactile sensing

With the rapid development of artificial intelligence and human–computer interaction fields, wearable electronic devices represented by flexible pressure sensors have a wider range of application scenarios. In this paper, we used a composite hydrogel made by one-pot polymerization using a combination of acrylamide (AM), sodium carboxymethyl cellulose (CMC), dopamine (DA), and MXene (Ti3C2Tx). The composite hydrogel has the advantages of self-adhesion, high sensitivity, large detection range (0–500 kPa), low detection limit, high toughness (compression > 90 %), stability (more than 6700 s) and other advantages of hydrogel flexible pressure strain sensor. Sodium carboxymethyl cellulose (CMC), as a multifunctional thickener and gelling agent, significantly improves rheological properties and viscosity in hydrogels, forms crosslinked structures to enhance mechanical properties and provides reliable toughness for pressure sensors. Dopamine (DA), which is rich in catechol groups, can adhere to the material surface through oxidative polymerization to enhance bond strength. Hydrogel pressure sensors for human motion detection can accurately track joint movement, respond sensitively to changes in knocks and touches, and display specific characters such as Morse code. The technology extends to robotic systems, where pressure-activated electrical signals are used to control robot movements. Therefore, it has a very promising application in the field of human–computer interaction, human-body movement signal detection, etc. PAM/CMC/DA/MXene hydrogel pressure sensors have excellent mechanical, electrical, and adhesion properties, which provide a more reliable solution for the fields of flexible wearable e-skin, human–computer interaction, and tactile sensing.

High‐Performance Multipedal Shape Strain Sensors for Human Motion and Electrophysiological Signal Monitoring

AbstractStrain sensors from conducting polymer hydrogel have been widely employed in various wearable devices, electronic skins, and biomedical applications. These sensors provide outstanding flexibility and high sensitivity by integrating conducting polymer with hydrogels, making them particularly suitable for monitoring human motion and physiological signals like heart rate or muscle activity. Despite their extensive application potential, conducting polymer hydrogel face several technical challenges in practical use, including poor mechanical properties, lack of long‐term stability, and difficulty in customizable design. This work introduces a method for fabricating a multipedal strain sensor using poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS)/polyvinyl alcohol (PVA) dimethyl sulfoxide (DMSO)hydrogels through screen printing and demonstrates its application in human motion monitoring. The multipedal strain sensor demonstrates a low Young's modulus (200 kPa), high stretchability (400%), and excellent mechanical cyclic stability (3000 cycles). Furthermore, this strain sensor is further applied to detect human movements such as chewing, smiling, fist clenching, arm bending, and carotid pulse monitoring. Comparative analysis between the multipedal‐designed sensor and the non‐designed sensor highlights the enhanced sensing capabilities of the multipedal sensor. The design of this multipedal sensor holds the potential to broaden the design concepts for strain sensors and offers new insights for wearable devices and electronic skins.

AI-enhanced backpack with double frequency-up conversion vibration energy converter for motion recognition and extended battery life

With growing interest in physical activities, wearable device sales have been increasing each year due to their help in tracking movement. However, keeping these devices running for a long time is still a key challenge. This paper presents an AI-enhanced backpack equipped with a hybrid vibration energy converter that integrates piezoelectric (PE) and electromagnetic (EM) technologies, featuring a double frequency-up conversion (FUC) mechanism. The PE unit enables efficient motion recognition, while the EM unit extends battery life via kinetic energy harvesting. The double FUC mechanism boosts human motion signal strength and resolution by converting sub-Hertz frequencies to hundreds of Hertz. This makes the device particularly effective in environments with performance constraints and interference. By adopting deep learning techniques, the system maintains a high motion recognition accuracy of 97.33 % even under low data sampling rates and significant noise interference. The EM unit effectively harvests kinetic energy, generating an average output power of 4.5 mW during activities such as running at 4.5 Hz, despite of the device’s compact size. Additionally, a power management circuit with human motion-stimulated switch optimizes battery life by managing the system’s sleep and active states. This innovative energy management approach ensures that during human activity, the system conserves energy by remaining in sleep state for at least 30 % of the time, significantly extending battery life and enhancing the backpack’s suitability for outdoor applications.

Human in the collaborative loop: a strategy for integrating human activity recognition and non-invasive brain-machine interfaces to control collaborative robots.

Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method's potential benefits and implications for HRC.

Multi-step prediction of ship heave motion using transformer-enhanced multi-scale CNN

To achieve precise multi-step heave motion prediction for active compensation control in marine equipment, an innovative approach that integrates an attention-fused multi-scale CNN with a Transformer encoder is introduced in this study. The dual-path CNN and point-wise convolution-enhanced Transformer encoder are designed to capture local features across various scales and the global characteristics of heave motion signals, respectively. Furthermore, a novel optimization objective which combines a slope factor-optimized Huber loss function with a maximum square distance loss function is proposed to better approximate the original signals at extreme points. The proposed model is trained and tested on simulated data under multiple sea conditions. In typical active compensation scenarios (Hs=3.5m), the proposed model demonstrates superior performance compared to baseline methods, achieving an RMSE of 0.0083 m, an MAE of 0.0066 m, and the upper whisker of the box plot for the prediction error is less than 0.02 m. These outcomes effectively satisfy the accuracy requirements for active compensation systems. The generalization test on real-ship collected data demonstrates excellent performance after fine-tuning. Additionally, the model is considered suitable for deployment in real-world applications due to its memory efficiency and fast inference speed.

Characteristics of motion signal profiles of tonic-clonic, tonic, hyperkinetic, and motor seizures extracted from nocturnal video recordings.

In this study, characteristics of signal profiles formed by motion, oscillation, and sound signals were analyzed to evaluate generalizability and variability in a single patient setting (intra-patient variability) and between patients (inter-patient variability). As a secondary objective, the effect of brivaracetam intervention on signal profiles was explored. Patient data included 13 hyperkinetic seizures, 65 tonic seizures, 13 tonic-clonic seizures, and 138 motor seizures from 11 patients. All patients underwent an 8-week monitoring, and after a 3-week baseline, brivaracetam was initiated. Motion, oscillation, and sound features extracted from the video were used to form signal profiles. Variance of signals was calculated, and combined median and quartile visualizations were used to visualize the results. Similarly, the effect of intervention was visualized. Hyperkinetic motion signals showed a rapid increase in motion and sound signals without oscillations and achieved low intra-patient variance. Tonic component created a recognizable peak in motion signal typical for tonic and tonic-clonic seizures. For tonic seizures, inter-patient variance was low. Motor signal profiles were varying, and they did not form a generalizable signal profile. Visually recognizable changes were observed in the signal profiles of two patients. Video-based motion signal analysis enabled the extraction of motion features characteristic for different motor seizure types which might be useful in further development of this system. Tonic component formed a recognizable seizure signature in the motion signal. Hyperkinetic and motor seizures may have not only significantly different motion signal amplitude but also overlapping signal profile characteristics which might hamper their automatic differentiation. Motion signals might be useful in the assessment of movement intensity changes to evaluate the treatment effect. Further research is needed to test generalizability and to increase reliability of the results.

Estimation and Removal of Residual Motion Artifact in Retrospectively Motion-Corrected fMRI Data: A Comparison of Intervolume and Intravolume Motion Using Gold Standard Simulated Motion Data

Residual head motion artifact in motion-corrected resting-state (rs-) functional MRI (fMRI) and fMRI datasets reduces the temporal signal-to-noise ratio and leaves non-neuronal signal components in the data, which can induce false findings in these studies. While various residual motion nuisance regressors have been proposed to regress out residual motion artifact after motion correction, these validations have typically been conducted empirically in in vivo data, since realistic head motion–corrupted MR data are not available. Here, we generated motion-corrupted MR data by altering imaging plane coordinates before each volume and slice acquisition from an ex vivo brain phantom using the simulated prospective acquisition correction (SIMPACE) sequence. Testing SIMPACE motion-corrupted data with various intervolume motion patterns, we first investigated the mechanism of the residual motion signal after motion correction and also proposed a voxel-wise motion nuisance regressor, called the partial volume (PV) regressor. We also modified the slice-oriented motion-correction method (SLOMOCO) pipeline with 6 volume-wise rigid intervolume motion parameters (Vol-mopa), 6 slice-wise rigid intravolume motion parameters (Sli-mopa), and the proposed PV motion nuisance regressor. We then compared the residual signal after application of the modified SLOMOCO (mSLOMOCO) pipeline with two other methods: intervolume motion-correction method (VOLMOCO), and the original SLOMOCO (oSLOMOCO). We found that mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors outperformed both VOLMOCO with 6 Vol-mopa and PV regressors and oSLOMOCO with 14 voxel-wise regressors. In tests of the 10 different motion patterns of SIMPACE datasets with 1× and 2× amplified intravolume motion, mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors pipeline produced the average standard deviation (SD) of the residual time series signals in the gray matter (GM) smaller by 29% (1× amplified intravolume motion) and 45% (2× amplified intravolume motion) than VOLMOCO with 6 Vol-mopa and PV regressors pipeline. Also, mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors pipeline outperformed oSLOMOCO with 14 voxelwise regressors pipeline, generating the average SD in GM smaller by 28% (1× amplified intravolume motion) and 31% (2× amplified intravolume motion) than oSLOMOCO with 14 voxel-wise regressors pipeline. The novel PV regressor also effectively reduced residual motion artifact as a motion nuisance regressor after both VOLMOCO and mSLOMOCO.

Super-strain, high sensitivity, and multi-responsive flexible sensor based on nanocellulose hydrogels

The application of flexible sensors based on conductive hydrogels in human motion monitoring has been widely studied. Here, a facile one-pot method is proposed to prepare nanocellulose/polyacrylamide/sodium alginate hydrogel (CNWs/SA/PAM). The resulting CNWs/SA/PAM hydrogel has exceptional mechanical properties (7850 %, 1.3 MPa) and high sensitivity (14200 %, GF=3.6, 68 ms). The nanocellulose crystals (CNWs) are bent and entangled with the backbone of the polyacrylamide/sodium alginate (PAM/SA) hydrogel network, effectively transferring external mechanical forces to the entire physical and chemical cross-linking domains. This feature considerably improves the tensile strength, strain sensing range, and sensitivity of the hydrogel. The CNWs/SA/PAM hydrogel exhibits a sensitive response to water molecules and temperature-stimulated shape memory behaviour and can act as a programmable deformation actuator. The hydrogel sensor can detect and monitor subtle human motion signals in a timely and accurate manner. It is designed as a single-electrode friction nanogenerator insole (insole-TENG) that can monitor the large motion states of the human body in real time and can be efficiently used for small self-powered electronic devices. This study will shed some light on developing cellulose hydrogels in multifunctional human motion health detection sensors, soft-actuated robots, and self-powered applications for small electronic devices.

High elongation, low hysteresis, fatigue resistant gel electrolyte for supercapacitor and strain sensor

With the arrival of the era of the Internet of Everything and the popularization of flexible wearable devices, energy supply devices serving flexible wearable devices have also become a research hotspot. Hydrogels are gaining ground in the field of flexible energy supply devices, especially supercapacitors, which have attracted wide attention due to the advantages of fast charging/discharging, high power density, and stability. However, flexible supercapacitor inevitably undergoes varying degrees of deformation during use, and their service life decreases dramatically with the prolongation of use. Therefore, the fabrication of a flexible supercapacitor that can be assembled from hydrogel electrolyte and is fatigue-resistant and stable has become a promising research direction. In this study, we constructed a gel electrolyte with high stretchability, low hysteresis, high stability, and fatigue resistance by designing a dual network structure, and assembled it into a supercapacitor. The device has many excellent features, providing a surface capacitance of 100 mF/cm2, a power density of 0.19 mW/cm2, and an energy density of 425 μWh/cm2 at a frequency of 1 mA/cm2. More importantly, it also exhibits superb stability when subjected to varying degrees of bending and compression. In addition, the gel electrolyte can detect small changes of the human body and quickly and sensitively convert motion signals into electrical signals, showing great potential as a flexible wearable device. This study provides an effective strategy for designing gel electrolytes with low hysteresis and high stability for wider application in flexible electronic devices.

Analysis of the Spatial Distribution and Common Mode Error Correlation in a Small-Scale GNSS Network.

When analyzing GPS time series, common mode errors (CME) often obscure the actual crustal movement signals, leading to deviations in the velocity estimates of station coordinates. Therefore, mitigating the impact of CME on station positioning accuracy is crucial to ensuring the precision and reliability of GNSS time series. The current approach to separating CME mainly uses signal filtering methods to decompose the residuals of the observation network into multiple signals, from which the signals corresponding to CME are identified and separated. However, this method overlooks the spatial correlation of the stations. In this paper, we improved the Independent Component Analysis (ICA) method by introducing correlation coefficients as weighting factors, allowing for more accurate emphasis or attenuation of the contributions of the GNSS network's spatial distribution during the ICA process. The results show that the improved Weighted Independent Component Analysis (WICA) method can reduce the root mean square (RMS) of the coordinate time series by an average of 27.96%, 15.23%, and 28.33% in the E, N, and U components, respectively. Compared to the ICA method, considering the spatial distribution correlation of stations, the improved WICA method shows enhancements of 12.53%, 3.70%, and 8.97% in the E, N, and U directions, respectively. This demonstrates the effectiveness of the WICA method in separating CMEs and provides a new algorithmic approach for CME separation methods.

Flexible and wearable multilayer structure fiber sensor for motion and human physiological signal monitoring

AbstractThe development of flexible piezoresistive sensors has increasingly attracted widespread attention due to their various potential applications in wearable devices, human–machine interfaces, and health monitoring systems. Developing high‐performance sensors that can accurately monitor movement and human health monitoring is very necessary. This study uses silver nanoparticles, MXene, and polypyrrole to prepare conductive fabrics based on various fabrics through layer‐by‐layer encapsulation. Finally, conductive fabrics with different layers were encapsulated to prepare a multilayer fiber sensor. Next, the series piezoresistive performance of sensors made of different fabric materials and sensors with different layers were tested. The experimental results show that the sensor prepared has the best performance when the selected fabric material is mulberry silk and the number of layers of the fabric is five. After testing, the sensitivity of the sensor is 4.9029 at low strain and 0.7901 at high strain. Moreover, it exhibits good stability during 1600 compression‐release cycles and temperatures range from −20 to 60°C. The designed sensor performs well in motion and human physiological signal monitoring. This study provides new ideas and references for the design and research of multilayer fiber sensors.

Co-speech head nods are used to enhance prosodic prominence at different levels of narrow focus in French.

Previous research has shown that prosodic structure can regulate the relationship between co-speech gestures and speech itself. Most co-speech studies have focused on manual gestures, but head movements have also been observed to accompany speech events by Munhall, Jones, Callan, Kuratate, and Vatikiotis-Bateson [(2004). Psychol. Sci. 15(2), 133-137], and these co-verbal gestures may be linked to prosodic prominence, as shown by Esteve-Gibert, Borrás-Comes, Asor, Swerts, and Prieto [(2017). J. Acoust. Soc. Am. 141(6), 4727-4739], Hadar, Steiner, Grant, and Rose [(1984). Hum. Mov. Sci. 3, 237-245], and House, Beskow, and Granström [(2001). Lang. Speech 26(2), 117-129]. This study examines how the timing and magnitude of head nods may be related to degrees of prosodic prominence connected to different focus conditions. Using electromagnetic articulometry, a time-varying signal of vertical head movement for 12 native French speakers was generated to examine the relationship between head nod gestures and F0 peaks. The results suggest that speakers use two different alignment strategies, which integrate both temporal and magnitudinal aspects of the gesture. Some evidence of inter-speaker preferences in the use of the two strategies was observed, although the inter-speaker variability is not categorical. Importantly, prosodic prominence itself is not the cause of the difference between the two strategies, but instead magnifies their inherent differences. In this way, the use of co-speech head nod gestures under French focus conditions can be considered as a method of prosodic enhancement.

3D printed multi-coupled bioinspired skin-electronic interfaces with enhanced adhesion for monitoring and treatment

Skin-electronic interfaces have broad applications in fields such as diagnostics, therapy, health monitoring, and smart wearables. However, they face various challenges in practical use. For instance, in wet environments, the cohesion of the material may be compromised, and under dynamic conditions, maintaining conformal adhesion becomes difficult, leading to reduced sensitivity and fidelity of electrical signal transmission. The key scientific issue lies in forming a stable and tight mechanical-electronic coupling at the tissue-electronic interface. Here, inspired by octopus sucker structures and snail mucus, we propose a strategy for hydrogel skin-electronic interfaces based on multi-coupled bioinspired adhesion and introduce an ultrasound (US)-mediated interfacial toughness enhancement mechanism. Ultimately, using digital light processing micro-nano additive manufacturing technology (DLP 3D), we have developed a multifunctional, diagnostic-therapeutic integrated patch (PAMS). This patch exhibits moderate water swelling properties, a maximum deformation of up to 460%, high sensitivity (GF = 4.73), and tough and controllable bioadhesion (shear strength increased by 109.29%). Apart from outstanding mechanical and electronic properties, the patch also demonstrates good biocompatibility, anti-bacterial properties, photothermal properties, and resistance to freezing at −20 °C. Experimental results show that this skin-electronic interface can sensitively monitor temperature, motion, and electrocardiogram signals. Utilizing a rat frostbite model, we have demonstrated that this skin-electronic interface can effectively accelerate the wound healing process as a wound patch. This research offers a promising strategy for improving the performance of bioelectronic devices, sensor-based educational reforms and personalized diagnostics and therapeutics in the future. Statement of significanceEstablishing stable and tight mechanical-electronic coupling at the tissue-electronic interface is essential for the diverse applications of bioelectronic devices. This study aims to develop a multifunctional, diagnostic-therapeutic integrated hydrogel skin-electronic interface patch with enhanced interfacial toughness. The patch is based on a multi-coupled bioinspired adhesive-enhanced mechanism, allowing for personalized 3D printing customization. It can be used as a high-performance diagnostic-therapeutic sensor and effectively promote frostbite wound healing. We anticipate that this research will provide new insights for constructing the next generation of multifunctional integrated high-performance bioelectronic interfaces.

Novel C. elegans models of Lewy body disease reveal pathological protein interactions and widespread miRNA dysregulation

Lewy body diseases (LBD) comprise a group of complex neurodegenerative conditions originating from accumulation of misfolded alpha-synuclein (α-syn) in the form of Lewy bodies. LBD pathologies are characterized by α-syn deposition in association with other proteins such as Amyloid β (Aβ), Tau, and TAR-DNA-binding protein. To investigate the complex interactions of these proteins, we constructed 2 novel transgenic overexpressing (OE) C. elegans strains (α-synA53T;Taupro-agg (OE) and α-synA53T;Aβ1-42;Taupro-agg (OE)) and compared them with previously established Parkinson’s, Alzheimer’s, and Lewy Body Dementia disease models. The LBD models presented here demonstrate impairments including uncoordinated movement, egg-laying deficits, altered serotonergic and cholinergic signaling, memory and posture deficits, as well as dopaminergic neuron damage and loss. Expression levels of total and prone to aggregation α-syn protein were increased in α-synA53T;Aβ1-42 but decreased in α-synA53T;Taupro-agg animals when compared to α-synA53T animals suggesting protein interactions. These alterations were also observed at the mRNA level suggesting a pre-transcriptional mechanism. miRNA-seq revealed that cel-miR-1018 was upregulated in LBD models α-synA53T, α-synA53T;Aβ1-42, and α-synA53T;Taupro-agg compared with WT. cel-miR-58c was upregulated in α-synA53T;Taupro-agg but downregulated in α-synA53T and α-synA53T;Aβ1-42 compared with WT. cel-miR-41-3p and cel-miR-355-5p were significantly downregulated in 3 LBD models. Our results obtained in a model organism provide evidence of interactions between different pathological proteins and alterations in specific miRNAs that may further exacerbate or ameliorate LBD pathology.