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There are significant challenges in attracting young people's attention to the protection and inheritance of intangible cultural heritage (ICH). Even Sichuan opera, a highly interactive and entertaining local cultural representative, struggles with inheritance issues. To solve this problem, this paper utilizes Augmented Reality (AR) technology to achieve a virtual try-on of Sichuan opera costumes and interactive face transformation for Sichuan opera. By virtual modeling, skeleton binding, weight setting of Sichuan opera dan-role costumes, and rendering mapping using body tracking and ray tracing as well as cloth simulation techniques, the AR try - on of Sichuan opera costumes is realized. Additionally, the representative facial masks of Sichuan opera's face-changing and role types are digitally reconstructed in 3D, and AR interactive face transformation is accomplished through visual programming. Experimental results demonstrate that in the AR environment, real-time matching and stable interaction among human body movements, costumes, and facial masks are achieved. By conducting AR experiments and surveys among the youth group, it is found that compared to traditional methods, AR technology is more effective in capturing the attention and recognition of the younger generation. This is beneficial for the inheritance and development of intangible cultural heritage within this demographic. [ABSTRACT FROM AUTHOR]

Gait analysis offers vital insights into human movement, aiding in the diagnosis, treatment, and rehabilitation of various conditions. Analyzing gait in corridors, rather than in lab, provides unique advantages for a more comprehensive understanding of human locomotion. However, limited dedicated technologies constrain gait data analysis in this context. In this study, a markerless gait analysis system using an Azure Kinect sensor mounted on a mobile robot is proposed and validated as a potential solution for gait analysis in corridors. Ten healthy participants (4 males and 6 females) underwent two tests. The first test (5 trials per participant) took place in the laboratory. Here, Azure Kinect performance was validated against a Vicon system, assessing eight gait signals and 22 gait parameters. The second test (2 trials per participant) was performed in the corridors over a 32‐m walking distance to compare this gait pattern with the one developed within the laboratory. The intrasession Intraclass Correlation Coefficient (ICC) reliability for in‐lab experiments was assessed by calculating the ICC between gait cycles captured in each session per participant. Notably, knee flexion/extension (ICC‐0.95), hip flexion/extension (ICC‐0.96), pelvis rotation (ICC‐0.88), and interankle distance (ICC‐0.98) demonstrated excellent reliability with high confidence. Similarly, hip adduction/abduction showed good reliability (ICC‐0.79), while trunk rotation exhibited moderate reliability (ICC‐0.72). In contrast, both trunk tilt (ICC‐0.24) and pelvis tilt (ICC‐0.41) consistently displayed lower reliability. This was observed for both the Vicon and the Azure systems, highlighting the intricate nature of capturing precise data for these specific signals in both systems. Validity outcomes indicated comparable error rates to literature standards (12.68° $12.68^\circ $ knee flexion/extension, 5.54° $5.54^\circ $ hip flexion/extension, and 3.45° $3.45^\circ $ hip adduction/abduction), with 11 parameters having no significant differences from Vicon. Comparison of in‐lab and in‐corridor experiments show that individuals exhibit significantly longer stride time (1.10 s vs. 1.05 s), lower pelvis tilt (6.83° $6.83^\circ $ vs. 9.39° $9.39^\circ $), and lower minimum pelvis rotation (−5.82° $-5.82^\circ $ vs. −14.61° $-14.61^\circ $) when walking in the laboratory. This study demonstrates promising outcomes in outdoor gait analysis with a robot‐mounted camera, revealing significant distinctions from controlled laboratory evaluations [ABSTRACT FROM AUTHOR]

The recent surge in biosignal-based control signifies a profound paradigm shift in biomedical engineering. This innovative approach has injected new life into control theory, ushering in advancements in human-body interaction and control. Surface Electromyography (sEMG) emerges as a pivotal biosignal, attracting considerable attention for its wide-ranging applications across medicine, science, and engineering, particularly in the domain of functional rehabilitation. This study delves into the use of sEMG signals for controlling a robotic arm, with the overarching aim of improving the quality of life for people with disabilities in Vietnam. Raw sEMG signals are acquired via appropriate sensors and subjected to a robust processing methodology involving analog-to-digital conversion, band-pass and low-pass filtering, and envelope detection. To demonstrate the efficacy of the processed sEMG signals, this study introduces a robotic arm model capable of mimicking intricate human finger movements. Employing a fuzzy logic control strategy, the robotic arm demonstrates successful operation in experimental trials, characterized by swift response times, thereby positioning it as a valuable assistive device for people with disabilities. This investigation not only validates the feasibility of sEMG-based control for robotic arms, but also underscores its potential to significantly improve the lives of individuals with disabilities, a demographic that represents a substantial portion (approximately 8%) of the Vietnamese population. [ABSTRACT FROM AUTHOR]

Human detection and classification via Wi-Fi transmission have received a lot of attention in recent years as crucial facilitators in security and humancomputer interaction (HCI). The passive Wi-Fi radar (PWR) system used by previous researchers applied cross-ambiguity function (CAF) and CLEAN algorithms to process the detected signals. This paper explores the feasibility and viability of a PWR system in detecting and classifying human movements without utilizing CAF and CLEAN algorithms. The movements are performed by four participants but with comparable body sizes and heights. Three daily human movements are investigated namely walking, bending, and sitting, with each participant performing each movement 24 times, providing a total of 96 samples per activity. The system is evaluated based on the consistency of the signal pattern in a frequency domain and the percentage accuracy is assessed using an artificial neural network (ANN) classifier and trained using a leave-one-out cross-validation (LOOCV) method. The frequency domain results reveal that the signals are consistent, with no noticeable variations or changes in the voltage intensity or shape of the main lobe. The classification of the movements shows that the classifier has an overall accuracy of 97.6%. [ABSTRACT FROM AUTHOR]

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The emergence of novel self-powered humidity sensors has attracted considerable attention in the fields of smart electronic devices and personal healthcare. Herein, self-powered humidity sensors have been fabricated using a moisture-driven energy generation (MEG) device based on asymmetric tubular graphitic carbon nitride (g-CN) films prepared on anodized aluminum (AAO) template. At a relative humidity (RH) of 96%, the MEG device can provide an open-circuit voltage of 0.47 V and a short-circuit current of 3.51 µA, with a maximum output power of 0.08 µW. With inherent self-powered ability and humidity response via current variation, an extraordinary response of 1.78 × 106% (41%–96% RH) can be gained from the MEG device. The possible power generation mechanism is that g-CN/AAO heterostructure can form ion gradient and diffusion under the action of moisture to convert chemical potential into electrical potential, evoking a connaturally sensitive response to humidity. Self-powered respiration monitoring device based on the sensor is designed to monitor human movement (sitting, warming up, and running) and sleep status (normal, snoring, and apnea), maintaining excellent stability during cumulative 12-h respiration monitoring. This self-powered humidity sensing technology has promising potential for extensive integration into smart electronic and round-the-clock health monitoring devices. [ABSTRACT FROM AUTHOR]

Biomechanical analysis of human movement plays an essential role in understanding functional changes in people with Amyotrophic Lateral Sclerosis (ALS), providing information on muscle impairment. Studies suggest that surface electromyography (sEMG) may be able to quantify muscle activity, identify levels of fatigue, assess muscle strength, and monitor variation in limb movement. In this article, a systematic review protocol will analyze the psychometric properties of the sEMG regarding the clinical data on the skeletal muscles of people with ALS. This protocol uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological tool. A specific field structure was defined to reach each phase. Nine scientific databases (PubMed, Web of Science, Embase, Elsevier, IEEE, Google Scholar, SciELO, PEDro, LILACS E CENTRAL) were searched. The framework developed will extract data (i.e. study information, sample information, sEMG information, intervention, and outcomes) from the selected studies using a rigorous approach. The data will be described quantitatively using frequency and trend analysis methods, and heterogeneity between the included studies will be assessed using the I2 test. The risk of bias will be summarized using the most recent prediction model risk of bias assessment tool. Be sure to include relevant statistics here, such as sample sizes, response rates, P values or Confidence Intervals. Be specific (by stating the value) rather than general (eg, "there were differences between the groups"). This protocol will map out the construction of a systematic review that will identify and synthesize the advances in movement analysis of people with ALS through sEMG, using data extracted from articles. [ABSTRACT FROM AUTHOR]

Understanding how emerging infectious diseases spread within and between countries is essential to contain future pandemics. Spread to new areas requires connectivity between one or more sources and a suitable local environment, but how these two factors interact at different stages of disease emergence remains largely unknown. Further, no analytical framework exists to examine their roles. Here we develop a dynamic modelling approach for infectious diseases that explicitly models both connectivity via human movement and environmental suitability interactions. We apply it to better understand recently observed (1995-2019) patterns as well as predict past unobserved (1983-2000) and future (2020-2039) spread of dengue in Mexico and Brazil. We find that these models can accurately reconstruct long-term spread pathways, determine historical origins, and identify specific routes of invasion. We find early dengue invasion is more heavily influenced by environmental factors, resulting in patchy non-contiguous spread, while short and long-distance connectivity becomes more important in later stages. Our results have immediate practical applications for forecasting and containing the spread of dengue and emergence of new serotypes. Given current and future trends in human mobility, climate, and zoonotic spillover, understanding the interplay between connectivity and environmental suitability will be increasingly necessary to contain emerging and re-emerging pathogens. Here, using a dynamic modelling approach, the authors find that the spread of dengue through Mexico and Brazil is shaped by specific interactions between human mobility, climate, and the environment. Their models can also be applied to predict future spread in these geographic areas. [ABSTRACT FROM AUTHOR]

The demand for automated systems monitoring and supporting patients in their home-based recovery programs is substantial. While emerging technologies have been proposed as potential solutions to enhance at-home patient care, limited systems are in place due to their challenges in offering real-time monitoring and corrective feedback. Most proposed methodologies provide an overall measure or score for the executed movement. The proposed work involves the adaptation of an existing published dataset to cater to the needs of a system capable of remotely assisting patients, effectively acting as a virtual physical therapist able to provide corrective feedback. A dataset containing a set of three simple exercises for shoulder rehabilitation was processed. Each movement was meticulously annotated for temporal and categorical motion domains to monitor exercise execution in terms of the range of motion completeness and to evaluate compensatory movement patterns. This work carries substantial significance by offering a standardized and easily accessible model for human movement data, thus fostering the advancement of digital assistive systems designed to support home-based rehabilitation programs. [ABSTRACT FROM AUTHOR]

Range-Doppler images represent one of the most informative radar data forms, providing range and frequency information. This study explores the performance of machine learning and deep learning techniques in classifying human activities behind walls using Range-Doppler images. Therefore, we input the HOG features of Range-Doppler images into various machine-learning approaches. Although the HOG feature enhances the performance of machine learning methods, we observe the superior performance of Convolutional Neural Network (CNN) architectures in a more complex scenario in which the number of target activity classes is higher. To obtain sufficient data for the CNN architecture, we combine the Uniform Linear Array (ULA) and stepped-frequency continuous-wave (SFCW) structures, enabling the acquisition of multi-channel data. The experimental results demonstrate both the improvement of the machine learning accuracy from 95.33% to 98.67% through the HOG + Range-Doppler approach and an approximately 6% enhancement in CNN performance achieved through the SFCW-ULA combination. [ABSTRACT FROM AUTHOR]

Flexible sensors have been widely studied for use in motion monitoring, human‒machine interactions (HMIs), personalized medicine, and soft intelligent robots. However, their practical application is limited by their low output performance, narrow measuring range, and unidirectional force detection. Here, to achieve flexibility and high performance simultaneously, we developed a flexible wide-range multidimensional force sensor (FWMFS) similar to bones embedded in muscle structures. The adjustable magnetic field endows the FWMFS with multidimensional perception for detecting forces in different directions. The multilayer stacked coils significantly improved the output from the μV to the mV level while ensuring FWMFS miniaturization. The optimized FWMFS exhibited a high voltage sensitivity of 0.227 mV/N (0.5–8.4 N) and 0.047 mV/N (8.4–60 N) in response to normal forces ranging from 0.5 N to 60 N and could detect lateral forces ranging from 0.2–1.1 N and voltage sensitivities of 1.039 mV/N (0.2–0.5 N) and 0.194 mV/N (0.5–1.1 N). In terms of normal force measurements, the FWMFS can monitor finger pressure and sliding trajectories in response to finger taps, as well as measure plantar pressure for assessing human movement. The plantar pressure signals of five human movements collected by the FWMFS were analyzed using the k-nearest neighbors classification algorithm, which achieved a recognition accuracy of 92%. Additionally, an artificial intelligence biometric authentication system is being developed that classifies and recognizes user passwords. Based on the lateral force measurement ability of the FWMFS, the direction of ball movement can be distinguished, and communication systems such as Morse Code can be expanded. This research has significant potential in intelligent sensing and personalized spatial recognition. [ABSTRACT FROM AUTHOR]

The article "Potsdam data set of eye movement on natural scenes (DAEMONS)" emphasizes the significance of high-quality, openly accessible data sets for studying eye movement behavior. The authors have compiled and released a substantial data set of eye tracking data on 2,400 color photographs of natural scenes. This data set, called DAEMONS, includes annotations and serves as a benchmark for scan path modeling and spatial saliency prediction. The article provides comprehensive details about the stimulus material, image annotations, participants, and experimental setup. The study was conducted ethically, and the authors express gratitude to the individuals and organizations involved in the research. The dataset, along with the stimulus images and eye tracking experiment data, is available in online repositories. [Extracted from the article]

The identification of key points in the human body is vital for sports rehabilitation, medical diagnosis, human–computer interaction, and related fields. Currently, depth cameras provide more precise depth information on these crucial points. However, human motion can lead to variations in the positions of these key points. While the Mediapipe algorithm demonstrates effective anti-shake capabilities for these points, its accuracy can be easily affected by changes in lighting conditions. To address these challenges, this study proposes an illumination-adaptive algorithm for detecting human key points through the fusion of multi-source information. By integrating key point data from the depth camera and Mediapipe, an illumination change model is established to simulate environmental lighting variations. Subsequently, the fitting function of the relationship between lighting conditions and adaptive weights is solved to achieve lighting adaptation for human key point detection. Experimental verification and similarity analysis with benchmark data yielded R2 results of 0.96 and 0.93, and cosine similarity results of 0.92 and 0.90. With a threshold range of 8, the joint accuracy rates for the two rehabilitation actions were found to be 89% and 88%. The experimental results demonstrate the stability of the proposed method in detecting key points in the human body under changing illumination conditions, its anti-shake ability for human movement, and its high detection accuracy. This method shows promise for applications in human–computer interaction, sports rehabilitation, and virtual reality. [ABSTRACT FROM AUTHOR]

Underarm throwing motions are crucial in various sports, including boccia. Unlike healthy players, people with profound weakness, spasticity, athetosis, or deformity in the upper limbs may struggle or find it difficult to control their hands to hold or release a ball using their fingers at the proper timing. To help them, our study aims to understand underarm throwing motions. We start by defining the throwing intention in terms of the launch angle of a ball, which goes hand-in-hand with the timing for releasing the ball. Then, an appropriate part of the body is determined in order to estimate ball-throwing intention based on the swinging motion. Furthermore, the geometric relationship between the movements of the body part and the release angle is investigated by involving multiple subjects. Based on the confirmed correlation, a calibration-and-estimation model that considers individual differences is proposed. The proposed model consists of calibration and estimation modules. To begin, as the calibration module is performed, individual prediction states for each subject are updated online. Then, in the estimation module, the throwing intention is estimated employing the updated prediction. To verify the effectiveness of the model, extensive experiments were conducted with seven subjects. In detail, two evaluation directions were set: (1) how many balls need to be thrown in advance to achieve sufficient accuracy; and (2) whether the model can reach sufficient accuracy despite individual differences. From the evaluation tests, by throwing 20 balls in advance, the model could account for individual differences in the throwing estimation. Consequently, the effectiveness of the model was confirmed when focusing on the movements of the shoulder in the human body during underarm throwing. In the near future, we expect the model to expand the means of supporting disabled people with ball-throwing disabilities. [ABSTRACT FROM AUTHOR]

The intention-to-treat (ITT) analysis of the Applying Wolbachia to Eliminate Dengue (AWED) trial estimated a protective efficacy of 77.1% for participants resident in areas randomised to receive releases of wMel-infected Aedes aegypti mosquitoes, an emerging dengue preventive intervention. The limiting assumptions of ITT analyses in cluster randomised trials and the mobility of mosquitoes and humans across cluster boundaries indicate the primary analysis is likely to underestimate the full public health benefit. Using spatiotemporally-resolved data on the distribution of Wolbachia mosquitoes and on the mobility of AWED participants (n = 6306), we perform complier-restricted and per-protocol re-examinations of the efficacy of the Wolbachia intervention. Increased intervention efficacy was estimated in all analyses by the refined exposure measures. The complier-restricted analysis returned an estimated efficacy of 80.7% (95% CI 65.9, 89.0) and the per-protocol analysis estimated 82.7% (71.7, 88.4) efficacy when comparing participants with an estimated wMel exposure of ≥ 80% compared to those with <20%. These reanalyses demonstrate how human and mosquito movement can lead to underestimation of intervention effects in trials of vector interventions and indicate that the protective efficacy of Wolbachia is even higher than reported in the primary trial results. [ABSTRACT FROM AUTHOR]

Multifunctional wearable electronic sensors exhibit significant potential for applications in health management, motion tracking, intelligent healthcare, etc. In this study, we developed a novel assembly method for a polymeric silver nanowire (Ag NW)/transition metal carbide/nitride (MXene) @Loofah device using a facile solution dip-coating technique. During the pretreatment phase, the loofah was conditioned with polydiallyldimethylammonium chloride (PDAC), promoting the self-assembly of MXene layers and bolstering device stability. Then, the Ag NWs/MXene@Loofah was packaged with polyurethane to form a piezoresistive pressure sensor, which demonstrated superior pressure-sensing capabilities and was adept at registering movements of human joints and even subtle pulses. The design strategy presents a novel and rational approach to developing efficient pressure sensors. [ABSTRACT FROM AUTHOR]

MXenes, as emerging 2D sensing materials for next-generation electronics, have attracted tremendous attention owing to their extraordinary electrical conductivity, mechanical strength, and flexibility. However, challenges remain due to the weak stability in the oxygen environment and nonnegligible aggregation of layered MXenes, which severely affect the durability and sensing performances of the corresponding MXene-based pressure sensors, respectively. Here, in this work, we propose an easy-to-fabricate self-assembly strategy to prepare multilayered MXene composite films, where the first layer MXene is hydrogen-bond self-assembled on the electrospun thermoplastic urethane (TPU) fibers surface and the anti-oxidized functionalized-MXene (f-MXene) is subsequently adhered on the MXene layer by spontaneous electrostatic attraction. Remarkably, the f-MXene surface is functionalized with silanization reagents to form a hydrophobic protective layer, thus preventing the oxidation of the MXene-based pressure sensor during service. Simultaneously, the electrostatic self-assembled MXene and f-MXene successfully avoid the invalid stacking of MXene, leading to an improved pressure sensitivity. Moreover, the adopted electrospinning method can facilitate cyclic self-assembly and the formation of a hierarchical micro-nano porous structure of the multilayered f-MXene/MXene/TPU (M-fM2T) composite. The gradient pores can generate changes in the conductive pathways within a wide loading range, broadening the pressure detection range of the as-proposed multilayered f-MXene/MXene/TPU piezoresistive sensor (M-fM2TPS). Experimentally, these novel features endow our M-fM2TPS with an outstanding maximum sensitivity of 40.31 kPa−1 and an extensive sensing range of up to 120 kPa. Additionally, our M-fM2TPS exhibits excellent anti-oxidized properties for environmental stability and mechanical reliability for long-term use, which shows only ~0.8% fractional resistance changes after being placed in a natural environment for over 30 days and provides a reproducible loading–unloading pressure measurement for more than 1000 cycles. As a proof of concept, the M-fM2TPS is deployed to monitor human movements and radial artery pulse. Our anti-oxidized self-assembly strategy of multilayered MXene is expected to guide the future investigation of MXene-based advanced sensors with commercial values. [ABSTRACT FROM AUTHOR]

Functional Movement Screening (FMS) is a test used to evaluate fundamental movement patterns in the human body and identify functional limitations. However, the challenge of carrying out an automated assessment of FMS is that complex human movements are difficult to model accurately and efficiently. To address this challenge, this paper proposes an automatic evaluation method for FMS based on a multi-scale lightweight 3D convolution encoder–decoder (ML3D-ED) architecture. This method adopts a self-built multi-scale lightweight 3D convolution architecture to extract features from videos. The extracted features are then processed using an encoder–decoder architecture and probabilistic integration technique to effectively predict the final score distribution. This architecture, compared with the traditional Two-Stream Inflated 3D ConvNet (I3D) network, offers a better performance and accuracy in capturing advanced human movement features in temporal and spatial dimensions. Specifically, the ML3D-ED backbone network reduces the number of parameters by 59.5% and the computational cost by 77.7% when compared to I3D. Experiments have shown that ML3D-ED achieves an accuracy of 93.33% on public datasets, demonstrating an improvement of approximately 9% over the best existing method. This outcome demonstrates the effectiveness of and advancements made by the ML3D-ED architecture and probabilistic integration technique in extracting advanced human movement features and evaluating functional movements. [ABSTRACT FROM AUTHOR]

Introduction: Trunk and shoulder strength are consistently shown to be involved in performance limitations, as well as contributing to stability, power output, and reducing the risk of injury. Although their biomechanical interaction is a critical aspect for athletes, there is limited research on the relationship between trunk and shoulder strength in sports where upper body mechanics are critical for optimal performance. Purpose: This study examined the differences and relationships between trunk rotational strength and shoulder rotational strength among athletes participating in mixed martial arts (MMA), tennis, swimming, and baseball. Methods: Maximal voluntary contraction tests were performed to evaluate strength of 39 professional adult male athletes from disciplines of MMA (n = 6), tennis (n = 11), swimming (n = 11) and baseball (n = 11). Peak force data were used in sports comparison and relationship analysis between trunk and shoulder rotation strength parameters. Results: The findings revealed a complex and significant relationship between trunk and shoulder strength, with unique patterns for each athletic discipline. Tennis players exhibited a strong correlation between trunk bilateral differences and internal shoulder rotation, while other disciplines demonstrated a more balanced use of trunk asymmetry. Swimmers displayed the best interactions between trunk and shoulder overall, emphasizing the aquatic environment's biomechanical demands. In MMA, the strongest correlation was between shoulder internal and external rotation with the trunk, mainly due to the number of defensive movements in addition to offensive ones. Baseball pitchers showed a significant correlation between internal/external shoulder rotation strength ratio and trunk asymmetry. Conclusion: While no differences in peak force variables were found, unique relationships between trunk and shoulder rotational performance were discovered. The results suggest a long-term sport-specific adaptation of the trunk-shoulder interaction in sports that require upper limb power movements. It seems, that the relationship between the various parameters of trunk and shoulder was influenced by the movement stereotype of each sport. Therefore, recognition of sport-specific interactions is critical to the development of effective training programs that enhance performance and potentially reduce injury risk in different sports. Researchers and practitioners should focus on longitudinally monitoring fluctuations in TRS and SRS relationships throughout each sport season and examining potential associations with injury incidence. [ABSTRACT FROM AUTHOR]

An electronic tongue (E-tongue) comprises a series of sensors that simulate human perception of taste and embedded artificial intelligence (AI) for data analysis and recognition. Traditional E-tongues based on electrochemical methods suffer from a bulky size and require larger sample volumes and extra power sources, limiting their applications in in vivo medical diagnosis and analytical chemistry. Inspired by the mechanics of the human tongue, triboelectric components have been incorporated into E-tongue platforms to overcome these limitations. In this study, an integrated multichannel triboelectric bioinspired E-tongue (TBIET) device was developed on a single glass slide chip to improve the device's taste classification accuracy by utilizing numerous sensory signals. The detection capability of the TBIET was further validated using various test samples, including representative human body, environmental, and beverage samples. The TBIET achieved a remarkably high classification accuracy. For instance, chemical solutions showed 100% identification accuracy, environmental samples reached 98.3% accuracy, and four typical teas demonstrated 97.0% accuracy. Additionally, the classification accuracy of NaCl solutions with five different concentrations reached 96.9%. The innovative TBIET exhibits a remarkable capacity to detect and analyze droplets with ultrahigh sensitivity to their electrical properties. Moreover, it offers a high degree of reliability in accurately detecting and analyzing various liquid samples within a short timeframe. The development of a self-powered portable triboelectric E-tongue prototype is a notable advancement in the field and is one that can greatly enhance the feasibility of rapid on-site detection of liquid samples in various settings. [ABSTRACT FROM AUTHOR]

Acquiring intentions before the start of lower limb movement imagery is the key issue in providing precise control strategies for the lower limb neurorehabilitation system. To this end, the feasibility of using electroencephalogram (EEG) signals prior to movement imagery to fully predict the movement imagery start‐stop intention and intention types of lower limb ambulation is investigated. The EEG signal is pre‑processed and the movement‑related cortical potential (MRCP) is extracted. 15 channels with significant discriminability are selected based on MRCP. The temporal convolutional network models are used to decode the movement imagery intention and intention types of lower limb ambulation from the selected MRCP channel features. The results show that the EEG channel signals selected by MRCP morphology are with significant separable differences in both start‐stop intention and intention types, verifying that using only pre‑movement imagery EEG signals is capable of fully prediction the movement imagery start‐stop intention and intention types of lower limb ambulation movement in humans. [ABSTRACT FROM AUTHOR]

The use of functional near-infrared spectroscopy (fNIRS) for brain imaging during human movement continues to increase. This technology measures brain activity non-invasively using near-infrared light, is highly portable, and robust to motion artifact. However, the spatial resolution of fNIRS is lower than that of other imaging modalities. It is unclear whether fNIRS has sufficient spatial resolution to differentiate nearby areas of the cortex, such as the leg areas of the motor cortex. Therefore, the purpose of this study was to determine fNIRS' ability to discern laterality of lower body contractions. Activity in the primary motor cortex was recorded in forty participants (mean = 23.4 years, SD = 4.5, female = 23, male = 17) while performing unilateral lower body contractions. Contractions were performed at 30% of maximal force against a handheld dynamometer. These contractions included knee extension, knee flexion, dorsiflexion, and plantar flexion of the left and right legs. fNIRS signals were recorded and stored for offline processing and analysis. Channels of fNIRS data were grouped into regions of interest, with five tolerance conditions ranging from strict to lenient. Four of five tolerance conditions resulted in significant differences in cortical activation between hemispheres. During right leg contractions, the left hemisphere was more active than the right hemisphere. Similarly, during left leg contractions, the right hemisphere was more active than the left hemisphere. These results suggest that fNIRS has sufficient spatial resolution to distinguish laterality of lower body contractions. This makes fNIRS an attractive technology in research and clinical applications in which laterality of brain activity is required during lower body activity. [ABSTRACT FROM AUTHOR]

Qiaomai is one of the eight extraordinary meridians, and its theory has long been marginalized in academic research on meridians. So its academic and practical value has not received sufficient attention. This article is based on the theory in Huangdi Neijing, combined with unearthed literature from the Pre-Qin period and the fusion of various medical schools reflected in the meridian theory system, to explore qiaomai's name and essence. Our team believes that the formation of qiaomai is the result of the long-term exploration and application of somatic twisting daoyin in Central Plains medicine. The formation of qiaomai coincides with the practical process and therapeutic thinking of somatic twisting daoyin, which has gone through a transformation from directly treating sick tendons to treating sick qi by adjusting tendons. The name of qiaomai may originate from the meanings of various movements such as limb touching, stretching, and swinging in the somatic twisting technique. The primitive physiological function of qiaomai is to dominate human movement, and its physiological level should be located in the tendons of the human body. Qiaomai constantly intersected and was confused with the bladder meridian of foot-taiyang and the kidney meridian of foot-shaoyin after the integration of meridian theory and visceral medicine during the period of Huangdi Neijing, and the positioning and function of qiaomai gradually became blurred. This cognitive confusion has become the fundamental reason why the academic exploration and clinical application of qiaomai has been hindered for a long time. This also suggests that when studying various Pre-Qin medical theoretical systems in Huangdi Neijing, sufficient screening, analysis, and restoration should be carried out to ensure that theories from different sources are authentic. [ABSTRACT FROM AUTHOR]

This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Toward that goal, we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in the proximity of garden plants, namely salad, basil, and tomatoes. To measure plant perception, we used the plant SpikerBox, which is a device that measures changes in the voltage differentials of plants between roots and leaves. Using machine learning, we found that the voltage differentials over time of the plant predict if (a) eurythmy has been performed, and (b) which kind of eurythmy gestures has been performed. We also find that the signals are different based on the species of the plant. In other words, the perception of a salad, tomato, or basil might differ just as perception of different species of animals differ. This opens new ways of studying plant ecosystems while also paving the way to use plants as biosensors for analyzing human movement. [ABSTRACT FROM AUTHOR]

Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as "sensors", which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key locations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur. [ABSTRACT FROM AUTHOR]

Introduction: This study introduces a new method to create virtual reality (VR) environments for studying synchrony in human body movements and their prosocial effects. Previous studies have shown the positive effects of synchrony, but more controlled and ecologically valid paradigms are needed to explore these effects deeper and translate them to the therapeutic domain. Methods: A total of 82 healthy subjects participated in this study. They performed simple periodic hand movements in a virtual environment with a virtual character (VC) mimicking them. We used inverse kinematics (IKs) to create character movements. The VCs mimic the participants after a short delay in the synchronous group and after a great delay in the nonsynchronous group. The subjective feeling of synchrony and social closeness was measured using a set of rating questionnaires. Results: The participants in the synchronous group reported more synchrony than the nonsynchronous group. The degree of social closeness between the two groups was not significantly different; however, there was a significant positive correlation between the reported degree of synchrony and social closeness within each group. Conclusion: Using a simple VR environment in which body movements are simulated by IKs can engender the feeling of synchrony and exert its prosocial effects. [ABSTRACT FROM AUTHOR]

Interesting sequential patterns in human movement trajectories can provide valuable knowledge for urban management, planning, and location-based business. Existing methods for mining such patterns, however, tend not to consider the reduced likeliness of trips with increasing travel cost. Consequently, it is difficult to differentiate the patterns emerging from people's specific travel interests from those simply due to travel convenience. To solve this problem, this article presents Geo-SigSPM for mining geographically interesting and statistically significant sequential patterns from trajectories. Here, 'geographically interesting' patterns are those more frequent than their expected frequencies which consider both the travel cost and non-redundancy of any place in the patterns. To achieve this, Geo-SigSPM formulates the expected frequencies of the patterns based on doubly-constrained human mobility models and the frequencies of their subsequences. A set of statistical tests is also developed to evaluate the identified interesting patterns. Experiments with synthetic and Foursquare check-in datasets demonstrate the efficacy of Geo- SigSPM in discovering geographically interesting patterns, controlling the spurious pattern rate, and discovering patterns that better reflect people's specific travel interests than the conventional frequency-based pattern mining approach. Geo-SigSPM is a promising solution to improving relevant decision-making when people's travel preference beyond travel cost is concerned. [ABSTRACT FROM AUTHOR]

Electronic skins (E-skins) can detect human health and movement, and have potential in the fields of human–machine interactions and artificial intelligence. However, traditional hydrogel-based E-skins suffer from poor mechanical strength, low conductivity, and instability due to water evaporation. Herein, a semi-interpenetrating network developed by polysaccharide biomass konjac glucomannan (KGM) was introduced into a covalent-crosslinked network polyacrylamide-co- polyacrylic acid (PAM-co-PAA) to advance the above dissatisfaction of E-skins. This synthesized a transparent, tough, non-volatile, and highly stretchable ionogel with an ionic liquid named 1-ethyl-3-methylimidazolium dicyamide (EMIM:DCA) as conductive media. This ionogel exhibited extraordinary mechanical strength (tensile strength of 2.77 MPa), outstanding mechanical durability (100 stretching cycles of 250%), and elongation (elongation at break of 997%). More importantly, the ionogel demonstrated remarkable anti-freezing performance (high flexibility at -20℃) and high conductivity (3.94 mS/cm) in the absence of water. Besides, after assembling KGM-enhanced ionogel, the sensor exhibited comprehensive strain sensing performance, which could effectively and accurately monitor human motion via Bluetooth transmission. This strategy paves the way for a viable new generation of multifunctional biomimetic super-sensitive sensors, which are promising for applications such as intelligent devices, health detection, and biomedical monitoring in harsh conditions. [ABSTRACT FROM AUTHOR]

Traditional robotic arms rely on complex programming and predefined trajectories to operate, which limits their applicability. To improve the flexibility and adaptability of the robot arm, the research focuses on improving the grasping performance of the robot arm based on vision technology. Kinect technology is used to capture human arm movements, and Kalman filter is introduced to smooth image data, so as to optimize the motion recognition process. In this study, the residual network model is further improved, and ELU activation function and pre-activation mechanism are introduced to enhance the classification accuracy of gesture images. The results showed that the improved ResNet50 model achieves 95% recognition accuracy after 25 iterations of training, while the original model is 80%. The application of Kalman filter makes the motion tracking curve smoother and shows the correction effect of this method. In simulation tests, the robotic arm is able to identify different elbow bending angles with 90-96 percent accuracy, while mimicking five specific hand gestures with 96-98 percent accuracy. These data support the practicability and effectiveness of the application of vision capture technology and deep learning model in the field of intelligent control of robotic arms. [ABSTRACT FROM AUTHOR]

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The detection of anomalous behavior in video surveillance is a focus of present research which has huge value and extensive application probabilities. Due to the difficulty of human movement and the feasibility of environments, anomalous behavior detection has some challenges. This paper proposed a deep learning-based approach for detecting the weapon and anomalous behavior. The Residual Attention-based Long-Short Term Memory (LSTM) with Self Gated Rectified Linear Unit (SGReLU) is proposed to enhance the detection accuracy. The median filter is used for preprocessing which removes noise from the UCF-Crime dataset and feeds into Histogram of Oriented Gradients (HOG) for feature extraction. Then, the Reverse Learning Chimp Optimization Algorithm (RL-COA) is utilized for hyperparameter optimization which attains the individual's reverse solution and then preserves the individual with high fitness values. At last, the Residual Attention-based LSTM with SGReLU is utilized for the classification process. This model overcomes neuron dead issues by allowing negative values for some neurons and minimizing the probability of inactive neurons. The proposed model attained better results on UCF-Crime dataset through the metrics like accuracy, precision, recall, f1-score and AUC values of about 98.84%, 98.62%, 98.47%, 98.35% and 98.21% correspondingly that ensures accurate detection when compared to existing techniques such as ResNet18 with Simple Recurrent Unit (SRU), Residual attention-based LSTM and Convolutional LSTM. [ABSTRACT FROM AUTHOR]

Supernumerary robotic limbs (SRLs) are a novel type of wearable robot used as the third limb to assist the wearer to perform operations that are difficult or impossible for human hands. Although SRLs can compensate for and enhance human physiological abilities, the unpredictable disturbances caused by human movements significantly affect the coordinating control between the robot and wearer. In this study, a general modeling of supernumerary robotic arms (SRAs) on an omnidirectional floating base is presented. Using position and orientation feedback from sensors at the base and tip, three control methods based on different sensor feedback are proposed to improve tracking accuracy. Experiments on point and trajectory tracking are conducted on the SRAs. In the results (point and circular trajectory‐tracking errors with the manipulator as floating base: 1.18 ± 0.56 mm [mean ± standard deviation(SD) error] and 1.42 ± 0.43 mm, point trajectory‐tracking errors with the human shoulder as floating base: 1.37 ± 0.58 mm, and the performance in perforation positioning operation experiment), it is demonstrated that the proposed controllers enable the SRAs to achieve high‐precision tracking and good adaptability to different user movements and frequencies. Also, in the results, future studies on dynamic high‐precision manipulation of SRLs are motivated. [ABSTRACT FROM AUTHOR]

Portable power is an effective solution to realize self-powered sensors for wearable devices, promoting future sustainable development. Membrane-based triboelectric nanogenerators (M-TENGs) have emerged as a promising technology for harvesting biomechanical energy from human motion owing to their advantages, such as simple structure, lightweight design, and efficient energy conversion. However, the poor durability, low adaptability, and un-washability of two-dimensional membrane materials have largely hindered their application in wearable electronics. In this study, we propose a sheath–core polyvinylidene fluoride (PVDF)/graphene (G)-carbon fiber (CF) yarn fabricated via conjugate electrospinning, comprising a commercial CF core and an electrospun graphene-doped PVDF sheath, which improves the fatigue resistance of electrospun nanofiber films under prolonged friction and keeps a high degree of freedom. The resulting electronic textile, woven with the large-scale electrospun PVDF/G-CF yarn, demonstrates a remarkable power density of 25.5 mW·m−2. The tight distribution of PVDF/G nanofibers on the textile surface ensures excellent softness, washability, and durability. Furthermore, the electrospun PVDF/G-CF textile exhibits significant potential in pressure sensing, self-powered operation, and motion detection, making it highly suitable for wearable electronics applications. [ABSTRACT FROM AUTHOR]

This article discusses the impact of climate change on human heat stress and the limitations of using the wet-bulb temperature as an index of heat stress. The wet-bulb temperature is often used in various indices to predict human heat stress, but it underestimates the impact of climate change on human thermoregulation, especially at lower humidity. The article highlights the importance of considering factors such as air movement and boundary layers in accurately quantifying heat exchange. The authors suggest using more complex models that take into account multiple variables to better predict human responses to heat stress. [Extracted from the article]

This review of movement stability, optimality, and agility is based on the theory of motor control with changes in spatial referent coordinates for the effectors, the principle of abundance, and the uncontrolled manifold hypothesis. A new optimality principle is suggested based on the concept of optimal sharing corresponding to a vector in the space of elemental variables locally orthogonal to the uncontrolled manifold. Motion along this direction is as.sociated with minimal components along the relatively unstable directions within the uncontrolled manifold leading to a minimal motor equivalent motion. For well-practiced actions, this task-specific criterion is followed in spaces of referent coordinates. Consequences of the suggested framework include trade-offs among stability, optimality, and agility, unintentional changes in performance, hand dominance, finger specialization. individual traits in performance, and movement disorders in neurological patients. [ABSTRACT FROM AUTHOR]

In this study, artificial neural networks (NN) are applied to the design of a transtibial prosthesis to adapt triplanar and bidirectional movements of human locomotion for rehabilitation. NN-based control is used because the prosthesis system is highly nonlinear and has variables with too many uncertainties caused by variations in ankle movements, weight damping, dorsiflexion, and flexion in the amputation area due to biological stimuli. To identify and detect these movements in the transtibial prosthesis, myoelectric signals are used that determine its position and adapt its trajectory through linear and rotary actuators. The input and desired parameters for the NN controller and the backpropagation algorithm are obtained based on the movements of the human ankle and foot based on their trajectory. The prototype is manufactured from different types of plastic using a 3D grapher, which can perform the main stages of human locomotion due to the learning carried out by the NN, reducing the risk of falls, and having a more comfortable and natural gait cycle in the rehabilitation of people. From the output response obtained from the NN controller, only a time delay is obtained without overshoot terms, and the trajectory tracking is adjusted. Simulation and experimental results show that the proposed NN-based control system can ensure the stability of the system and maintain good tracking of human locomotion. [ABSTRACT FROM AUTHOR]

Optical Motion Capture Systems (OMCSs) are considered the gold standard for kinematic measurement of human movements. However, in situations such as measuring wrist kinematics during a hairdressing activity, markers can be obscured, resulting in a loss of data. Other measurement methods based on non-optical data can be considered, such as magneto-inertial measurement units (MIMUs). Their accuracy is generally lower than that of an OMCS. In this context, it may be worth considering a hybrid system [MIMU + OMCS] to take advantage of OMCS accuracy while limiting occultation problems. The aim of this work was (1) to propose a methodology for coupling a low-cost MIMU (BNO055) to an OMCS in order to evaluate wrist kinematics, and then (2) to evaluate the accuracy of this hybrid system [MIMU + OMCS] during a simple hairdressing gesture. During hair cutting gestures, the root mean square error compared with the OMCS was 4.53° (1.45°) for flexion/extension, 5.07° (1.30°) for adduction/abduction, and 3.65° (1.19°) for pronation/supination. During combing gestures, they were significantly higher, but remained below 10°. In conclusion, this system allows for maintaining wrist kinematics in case of the loss of hand markers while preserving an acceptable level of precision (<10°) for ergonomic measurement or entertainment purposes. [ABSTRACT FROM AUTHOR]

[Display omitted] Recently, wearable electronic products and gadgets have developed quickly with the aim of catching up to or perhaps surpassing the ability of human skin to perceive information from the external world, such as pressure and strain. In this study, by first treating the cellulosic fiber (modal textile) substrate with (3-aminopropyl) triethoxysilane (APTES) and then covering it with conductive nanocomposites, a bionic corpuscle layer is produced. The sandwich structure of tactile corpuscle-inspired bionic (TCB) piezoresistive sensors created with the layer-by-layer (LBL) technology consists of a pressure-sensitive module (a bionic corpuscle), interdigital electrodes (a bionic sensory nerve), and a PU membrane (a bionic epidermis). The synergistic mechanism of hydrogen bond and coupling agent helps to improve the adhesive properties of conductive materials, and thus improve the pressure sensitive properties. The TCB sensor possesses favorable sensitivity (1.0005 kPa−1), a wide linear sensing range (1700 kPa), and a rapid response time (40 ms). The sensor is expected to be applied in a wide range of possible applications including human movement tracking, wearable detection system, and textile electronics. [ABSTRACT FROM AUTHOR]