Search

Abstract Background In the practical application of sarcopenia screening, there is a need for faster, time-saving, and community-friendly detection methods. The primary purpose of this study was to perform sarcopenia screening in community-dwelling older adults and investigate whether surface electromyogram (sEMG) from hand grip could potentially be used to detect sarcopenia using machine learning (ML) methods with reasonable features extracted from sEMG signals. The secondary aim was to provide the interpretability of the obtained ML models using a novel feature importance estimation method. Methods A total of 158 community-dwelling older residents (≥ 60 years old) were recruited. After screening through the diagnostic criteria of the Asian Working Group for Sarcopenia in 2019 (AWGS 2019) and data quality check, participants were assigned to the healthy group (n = 45) and the sarcopenic group (n = 48). sEMG signals from six forearm muscles were recorded during the hand grip task at 20% maximal voluntary contraction (MVC) and 50% MVC. After filtering recorded signals, nine representative features were extracted, including six time-domain features plus three time-frequency domain features. Then, a voting classifier ensembled by a support vector machine (SVM), a random forest (RF), and a gradient boosting machine (GBM) was implemented to classify healthy versus sarcopenic participants. Finally, the SHapley Additive exPlanations (SHAP) method was utilized to investigate feature importance during classification. Results Seven out of the nine features exhibited statistically significant differences between healthy and sarcopenic participants in both 20% and 50% MVC tests. Using these features, the voting classifier achieved 80% sensitivity and 73% accuracy through a five-fold cross-validation. Such performance was better than each of the SVM, RF, and GBM models alone. Lastly, SHAP results revealed that the wavelength (WL) and the kurtosis of continuous wavelet transform coefficients (CWT_kurtosis) had the highest feature impact scores. Conclusion This study proposed a method for community-based sarcopenia screening using sEMG signals of forearm muscles. Using a voting classifier with nine representative features, the accuracy exceeds 70% and the sensitivity exceeds 75%, indicating moderate classification performance. Interpretable results obtained from the SHAP model suggest that motor unit (MU) activation mode may be a key factor affecting sarcopenia.

Abstract Flexible loudspeakers that can be easily distributed in the surrounding environment are essential for creating immersive experiences in human-machine interactions, as these devices can transmit acoustic information conveniently. In this paper, we present a flexible electret loudspeaker that offers numerous benefits, such as eco-friendly, easy fabrication, flexible customization, strong durability, and excellent outputs. The output sound pressure level (SPL) and frequency response characteristic are optimized according to the simulation and experiment results. At a distance of 50 meters, a large-size loudspeaker (50 × 40 cm2) can produce an average SPL of 60 dB (normal SPL range of human voices is between 40 to 70 dB). The frequency response of our loudspeaker is high and relatively consistent up to 15 kHz, which covers the normal frequency range of human voices (

Abstract The recent development of wearable devices is revolutionizing the way of human–machine interaction (HMI). Nowadays, an interactive interface that carries more embedded information is desired to fulfill the increasing demand in era of Internet of Things. However, present approach normally relies on sensor arrays for memory expansion, which inevitably brings the concern of wiring complexity, signal differentiation, power consumption, and miniaturization. Herein, a one‐channel based self‐powered HMI interface, which uses the eigenfrequency of magnetized micropillar (MMP) as identification mechanism, is reported. When manually vibrated, the inherent recovery of the MMP causes a damped oscillation that generates current signals because of Faraday's Law of induction. The time‐to‐frequency conversion explores the MMP‐related eigenfrequency, which provides a specific solution to allocate diverse commands in an interference‐free behavior even with one electric channel. A cylindrical cantilever model is built to regulate the MMP eigenfrequencies via precisely designing the dimensional parameters and material properties. It is shown that using one device and two electrodes, high‐capacity HMI interface can be realized when the magnetic micropillars (MMPs) with different eigenfrequencies have been integrated. This study provides the reference value to design the future HMI system especially for situations that require a more intuitive and intelligent communication experience with high‐memory demand.

Abstract Objectives Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents. Lung metastasis (LM) occurs in more than half of patients at different stages of the disease course, which is one of the important factors affecting the long-term survival of OS. To develop and validate machine learning radiomics model based on radiographic and clinical features that could predict LM in OS within 3 years. Methods 486 patients (LM = 200, non-LM = 286) with histologically proven OS were retrospectively analyzed and divided into a training set (n = 389) and a validation set (n = 97). Radiographic features and risk factors (sex, age, tumor location, etc.) associated with LM of patients were evaluated. We built eight clinical-radiomics models (k-nearest neighbor [KNN], logistic regression [LR], support vector machine [SVM], random forest [RF], Decision Tree [DT], Gradient Boosting Decision Tree [GBDT], AdaBoost, and extreme gradient boosting [XGBoost]) and compared their performance. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. Results The radscore, ALP, and tumor size had significant differences between the LM and non-LM groups (t radscore = -5.829, χ 2 ALP = 97.137, t size = -3.437, P

Intelligent robot is the ultimate goal in the robotics field. Existing works leverage learning-based or optimization-based methods to accomplish human-defined tasks. However, the challenge of enabling robots to explore various environments autonomously remains unresolved. In this work, we propose a framework named GExp, which endows robots with the capability of exploring and learning autonomously without human intervention. To achieve this goal, we devise modules including self-exploration, knowledge-base-building, and close-loop feedback based on foundation models. Inspired by the way that infants interact with the world, GExp encourages robots to understand and explore the environment with a series of self-generated tasks. During the process of exploration, the robot will acquire skills from experiences that are useful in the future. GExp provides robots with the ability to solve complex tasks through self-exploration. GExp work is independent of prior interactive knowledge and human intervention, allowing it to adapt directly to different scenarios, unlike previous studies that provided in-context examples as few-shot learning. In addition, we propose a workflow of deploying the real-world robot system with self-learned skills as an embodied assistant. Project website: GExp.com.

Due to the unique electromechanical energy conversion capability of ferroelectric materials, they have been at the forefront of materials science for a variety of applications such as sensors, actuators and energy harvesting. Researchers have focused on exploring approaches to achieve improved ferroelectric performance, and to ensure that the available material systems are more environmentally friendly. This comprehensive review summarizes recent research progress on porous ceramics and highlights the variety of factors that are often ignored, namely the influence of porosity on the Curie temperature, and applications of porous ferroelectric materials with adjustable Curie temperature. Finally, the development trends and challenges of porous ferroelectric materials are discussed, aiming to provide new insights for the design and construction of ferroelectric materials.

Abstract An electret-based mechanical antenna (EBMA), which can transmit extremely low frequency (ELF) electromagnetic signals, has the advantages of miniaturization and high transmitting efficiency, with great potential applications in air, underwater, and underground communications. To improve the charge density of the electret, which is a key factor in determining the radiation performance of an EBMA, this work proposes a fluorinated ethylene propylene/terpolymer of tetrafluoroethylene, hexafluoropropylene and vinylidene fluoride (FEP/THV) unipolar electret exhibiting negative polarity, reaching a total charge density up to −0.46 mC/m2 for each layer of electret. Long transmission distances can be achieved in sea water, soil, and air using a 3-layer-FEP/THV-based EBMA with a compact volume of 5 × 10−4 m3. As an application demonstration, binary ASCII-coded ELF information of “BUAA” is successfully transmitted with a power consumption

Moisture-induced energy generation is a potential green energy power source. Here, the authors report a moisture-induced autonomous surface potential oscillation phenomenon and apply it to the demonstration of energy harvesters with long persistence time and good energy density

Different coating technological requirements of automobile electro-coating control system have different desired trajectories. For a dual parallel robot used for automobile electro-coating conveying (DPRAEC), an intelligent tracking smooth sliding mode synchronization control method (ITSSMSC) is proposed to achieve better tracking control performance with high synchronization and flexibility for the system under different desired trajectories. According to the dual parallel and symmetrical structure of the robot, the sliding surface is designed based on a composite error. A deep neural network with multi-hidden layer is trained by deep learning algorithms. Then, any given trajectories caused by different coating technological requirements could be tracked without tuning additional parameters and the synchronous performance could be guaranteed. The convergence of the network training process and the stability of the ITSSMSC are proved theoretically. Compared with the synchronization proportion-derivative control, the sliding mode control based on constant velocity reaching law without synchronization control and the smooth sliding mode synchronization control without training, simulation and experimental results demonstrate the effectiveness of the proposed approach. Compared with synchronization proportion-derivative control, the maximum tracking errors of constant velocity reaching law without synchronization control among each active joint are, respectively, reduced 92.2%, 94.7%, 98.8%. The maximum tracking errors of smooth sliding mode synchronization control and ITSSMSC in z direction are, respectively, 9.06×10 −5 m and 1.01×10 −9 m. The maximum tracking errors of smooth sliding mode synchronization control and ITSSMSC in β angle are, respectively, 4.50×10 −3 rad and 1.45×10 −4 rad. Therefore, the proposed control method could improve the tracking control performance and synchronization performance simultaneously under different desired trajectories.

Recently, the dual-permanent-magnet-excited (DPME) machine has attracted growing attention due to its high torque density. Due to the bidirectional field modulated effect (BFME), airgap flux density harmonics (AFDHs) are more complex and abundant than traditional permanent magnet synchronous machines (PMSMs). Moreover, the back-electromotive force (EMF) generated by AFDHs is also complex. Unfortunately, only a few papers qualitatively analyze back-EMF. The qualitative analysis for back-EMF can reveal some important conclusions; for example, only AFDHs meeting specific pole pair numbers (PPNs) can generate back-EMF. However, it may also ignore some details and valuable findings. In this paper, a purely analytical magnetomotive force (MMF) permeance model (PAMPM) for a DPME machine is built to quantitatively analyze the back-EMF. The PAMPM does not require a numerical method, such as conformal transformation. With the PAMPM, AFDHs that contribute to the generation of back-EMF can be recognized and quantified. Interestingly, AFDHs with $m_{2}p_{2}=np_{3}$ cause PM flux-linkage to have a dc bias, and not all AFDHs play a positive role in the generation of back-EMF. The main recognition results are as follows: 1) the S-II and R-II types of AFDHs in a 12/10 DPME machine overall make a negative contribution to the generation of back-EMF; and 2) AFDHs with PPN=22 in the two types mainly cause a negative contribution. To further verify the above results, 2D finite element simulation and experimental tests of a prototype machine are also conducted.

Harvesting energy from human body motions is an appropriate option as an assistant or even subversive way for powering the booming wearable electronics, in which smart textiles are important components. Here, we fabricate a robust power textile with working mechanism of triboelectrification effect and electrostatic induction effect by simply integrating the normal textiles and polydimethylsiloxane (PDMS)/conductive yarns electrodes. Maximum peak loading voltage and current reaching 230 V and 11.6 μA are obtained by rubbing our power textile, and this alternating and irregular electricity can be accumulated in a capacitor or directly light up 20 blue LEDs. The recovered energy generation ability of the power textile after washing successfully demonstrate its robustness, showing the potential application in powering the electronics in smart textiles.

This paper considers the finite-time dynamic tracking control for parallel robots with uncertainties and input saturation via a finite-time nonsingular terminal sliding mode control scheme. A disturbance observer is designed to estimate the lumped disturbance in the dynamic model of the parallel robot, including modeling errors, friction and external disturbance. By introducing the fractional exponential powers into the existing asymptotic convergent auxiliary system, a novel finite-time convergent auxiliary system is constructed to compensate for input saturation. The finite-time nonsingular terminal sliding mode control is proposed based on the disturbance estimation and the state of the novel auxiliary system, so that the convergence performance, control accuracy and robustness are improved. Due to the estimation and compensation for the lumped disturbance, the inherent chattering characteristic of sliding mode control can be alleviated by reducing the control gain. The finite-time stability of the closed-loop system is proved with Lyapunov theory. Finally, simulation and experimental research on the dynamic control of a conveying parallel robot are carried out to verify the effectiveness of the proposed method.

Objective: COVID-19-associated cough is a common acute and persistent symptom after SARS-CoV-2 infection, affecting thousands of patients physically and mentally. Fu’s subcutaneous needling (FSN) has been used for cough clinical, and this study aimed to examine the effect of FSN on COVID-19-associated cough. Methods: Patients with COVID-19-associated cough were enrolled and received FSN treatment once a day for three days. Then, the response rates after the first treatment, defined as the proportion of participants whose cough intensity visual analogue scale (VAS) was reduced by at least 50%, were compared with each assessment time after FSN treatment. Cough evaluation test (CET) score, fatigue scale-14 (FS-14), and adverse events were assessed. Results: One hundred and fifty-four patients completed the study. The rapid response rates increased to 24.02, 66.88, and 85.06% after each FSN treatment respectively, and up to 90.90% till the follow-up. Compared with the post-first FSN treatment, the response rates showed significant differences with each postFSN treatment (P < 0.001). Compared with the baseline, the VAS score, the CET score, and the FS-14 score were all significantly reduced after three times FSN treatments, and those reductions persisted until the follow-up (P < 0.001). A correlation between pre-post intervention change of CET score and FS-14 score was significant (P < 0.001). There were no adverse events happened in the study. Conclusions: FSN rapidly alleviated COVID-19-associated cough intensity, improved the quality of life, and reduced fatigue of patients. Cough relief was the primary benefit of FSN, and a certain correlation was found between cough relief and fatigue alleviation. [ABSTRACT FROM AUTHOR]