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The main target of this article is to analyze the role of activation energy and thermal radiation effects on the bioconvective peristaltic transport of Sutterby nanofluid in a two-dimensional flexible porous channel with heat and mass transfer. Also, the consequences of Hall current, heat source, and complaint wall properties along with an inclined magnetic field are taken into consideration. The proposed system of governing equations is simplified by using lubrication approximation and solved numerically using MATLAB's bvp5c solver. Further, numerical observations are analyzed to figure out the consequence of different physical parameters on the flow characteristics. According to the observations, it is identified that the Sutterby nanofluid velocity declines with the climb in the damping force parameter, while it enhances with the upsurge in the Darcy number. The Sutterby fluid temperature profile strengthens when the influence of the heat generation and Brinkman number increase, while it depicts the reverse effect with the elevation in the fluid parameter and radiation parameter. The temperature ratio and activation energy parameters were found to have a significant impact on the fluid concentration. The volume of the trapped fluid bolus is an enhancing function of the channel's non-uniformity parameter. Moreover, current work reveals its applicability to recognize the hemodynamic flow analysis and other biofluid movements in the human body and industrial sectors. [ABSTRACT FROM AUTHOR]

Human evolutionary ecology stands to benefit by integrating theory and methods developed in movement ecology, and in turn, to make contributions to the broader field of movement ecology by leveraging our species' distinct attributes. In this paper, we review data and evolutionary models suggesting that major changes in socio-spatial behaviour accompanied the evolution of language. To illustrate and explore these issues, we present a comparison of GPS measures of the socio-spatial behaviour of Hadza hunter–gatherers of northern Tanzania to those of olive baboons (Papio anubis), a comparatively small-brained primate that is also savanna-adapted. While standard spatial metrics show modest differences, measures of spatial diversity, landscape exploration and spatiotemporal displacement between individuals differ markedly. Groups of Hadza foragers rapidly accumulate a vast, diverse knowledge pool about places and things over the horizon, contrasting with the baboon's narrower and more homogeneous pool of ecological information. The larger and more complex socio-spatial world illustrated by the Hadza is one where heightened cognitive abilities for spatial and episodic memory, navigation, perspective taking and communication about things beyond the here and now all have clear value. This article is part of the theme issue 'The spatial–social interface: a theoretical and empirical integration'. [ABSTRACT FROM AUTHOR]

Given the great importance of human‐robot collaboration (HRC), the development of robotic arms tends to prioritize high flexibility, safety as well as low energy consumption. Dielectric elastomer actuators (DEAs), with their high energy density and lightweight characteristics, present a promising alternative for the design of soft robotic arms suitable for HRC setting. Hence, a soft mimic robotic arm powered by dual cone DEA (DCDEA) driver unit is developed that demonstrates high energy density and low energy consumption. The resultant elaborate DCDEA driver unit exhibits a significant bi‐direction actuation stroke with a high load‐to‐weight ratio up to 1,300, ensuring sufficient mechanical power output for powering the robotic arm. The well‐designed efficient transmission mechanism and control system together with DCDEA allow the robotic arm to conduct lifting and twisting action, mimicking the movements of a human arm. During the transportation of a 10 g weight, the robotic arm consumes only ≈35 mJ of energy, while achieving an energy density of 42.1 J kg−1. Encased in soft outer shells, the robotic arm displays full flexibility, high safety, and excellent impact resistance, underscoring its potential as a next‐generation soft robotic arm for HRC setting. [ABSTRACT FROM AUTHOR]

AbstractSport moves people physically, emotionally, socially and movement is central to sport. Yet, engagement with sport in mobilities studies is rare. This paper outlines an agenda for sport mobilities approaches to catalyse greater engagement in the field with the world of sport and to strengthen dialogues with, and mobilities’ position within, interdisciplinary sport studies. We first establish the promise of sport mobilities research before tracing existing engagements, and finally offering a framework for future sport mobilities approaches. [ABSTRACT FROM AUTHOR]

Recognizing that conventional understanding of animal and human locomotion is based on a dated and reductionist machine modeling of organisms, we set out to create a theory of locomotion by reasoning from first principles. We center on the constraints necessitated by 1) the 2nd law of thermodynamics, 2) the theory of evolution, 3) a systems science view of organisms, and 4) the laws of motion, but we also look for compatibility these constraints might find in emerging areas of scientific inquiry (ecological psychology, processual biology, soft matter, biotensegrity), and in the wisdom embedded in various movement traditions and ancient philosophy. Applying and synthesizing these, we propose an updated “bouncing bones” (BB) model for walking and running, which corresponds with maximum efficiency and conservation of energy. [ABSTRACT FROM AUTHOR]

ABSTRACT Estimation of the knee joint strength by maximum voluntary isometric contraction (MVIC) is a common practice to assess strength, coordination, safety to return to work or engage in sports after an injury, and to evaluate the efficacy of treatment modalities and rehabilitation strategies. In this study, we utilize a previously validated coupled finite element‐musculoskeletal model of the lower extremity to explore the sensitivity of output measures (posterior cruciate ligament [PCL]/muscle/contact forces and passive moments) in knee MVIC flexion exercises at seated position. To do so, at three knee flexion angles (KFA), input measures (resistance moment and contribution moments of quadriceps and gastrocnemii) were varied at four levels each using the Taguchi design of experiment. Our findings reveal significant increases in PCL forces with KFA (p < 0.01), net MVIC moment (p < 0.01), and resistance moment of quadriceps (p < 0.01). In contrast, they drop at larger activity in gastrocnemii (p < 0.01). Tibiofemoral (TF) contact forces increase with the net MVIC moment (p < 0.01). The passive knee flexion moment, while highly dependent on the location at which computed, also increases with the net MVIC moment (p < 0.01). Changes in KFA, MVIC moment, and proportions thereof carried by quadriceps and/or gastrocnemii substantially affect biomechanics of the joint. Compared with level walking and stair ascent, slightly larger contact forces/stresses and much greater PCL forces are computed. This study improves our understanding of the knee joint behavior during MVIC in effective evaluation and rehabilitation interventions. Besides, it emphasizes the importance of positioning the joint center in model studies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Beyond qualitative assessment, gait analysis involves the quantitative evaluation of various parameters such as joint kinematics, spatiotemporal metrics, external forces, and muscle activation patterns and forces. Utilizing multibody dynamics-based musculoskeletal (MSK) modeling provides a time and cost-effective non-invasive tool for the prediction of internal joint and muscle forces. Recent advancements in the development of biofidelic MSK models have facilitated their integration into clinical decision-making processes, including quantitative diagnostics, functional assessment of prosthesis and implants, and devising data-driven gait rehabilitation protocols. Through an extensive search and meta-analysis of over 116 studies, this PRISMA-based systematic review provides a comprehensive overview of different existing multibody MSK modeling platforms, including generic templates, methods for personalization to individual subjects, and the solutions used to address statically indeterminate problems. Additionally, it summarizes post-processing techniques and the practical applications of MSK modeling tools. In the field of biomechanics, MSK modeling provides an indispensable tool for simulating and understanding human movement dynamics. However, limitations which remain elusive include the absence of MSK modeling templates based on female anatomy underscores the need for further advancements in this area. [ABSTRACT FROM AUTHOR]

This data descriptor introduces GaitRec-VR, a 3D gait analysis dataset consisting of 20 healthy participants (9 males, 11 females, age range 21–56) walking at self-selected speeds in a real-world laboratory and the virtual reality (VR) replicas of this laboratory. Utilizing a head-mounted display and a 12-camera motion capture system alongside a synchronized force plate, the dataset encapsulates real and virtual walking experiences. A direct kinematic model and an inverse dynamic approach were employed for kinematics and computation of joint moments respectively, with an average of 23 ± 6 steps for kinematics and five clean force plate strikes per participant for kinetic analysis. GaitRec-VR facilitates a deeper understanding of human movement in virtual environments, particularly focusing on dynamic balance during walking in healthy adults, crucial for effective VR applications in clinical settings. The dataset, available in both.c3d and.csv formats, allows further exploration into VR's impact on gait, bridging the gap between physical and virtual locomotion. [ABSTRACT FROM AUTHOR]

Large cellular phone‐based mobility datasets are an important new data source for research on human movement. We investigate and illustrate bias in representation in a large mobility data set at the census block group, tract, and county levels. We paired American Community Survey (ACS) 2019 data with SafeGraph (SG) cell phone mobility data to elucidate potential bias in SG data by examining ACS estimated population against the number of devices in the SG data, stratifying by key sociodemographic variables such as income, percent Black population, percent of population over 55 years, percent of population 18–65 years, percent of people living in crowded living conditions, and urbanization level. We evaluated whether the bias varied over time by examining a 10‐month period. This bias changes with key demographic characteristics and changes over time. Specifically, we see underrepresentation in areas that have the highest percentage of Black population at all aggregation levels. We also see underrepresentation at all levels in areas with the highest percentage of working age residents as well as areas with the lowest median incomes. Researchers should be cautious when using mobility datasets because of bias differential on key sociodemographic factors and collection time. [ABSTRACT FROM AUTHOR]

The flexible electronic sensor is a critical component of wearable devices, generally requiring high stretchability, excellent transmittance, conductivity, self‐healing capability, and strong adhesion. However, designing ion‐conducting elastomers meeting all these requirements simultaneously remains a challenge. In this study, a novel approach is presented to fabricate highly stretchable, transparent, and self‐healing ion‐conducting elastomers, which are synthesized via photo‐polymerization of two polymerizable deep eutectic solvents (PDESs) monomers, i.e., methacrylic acid (MAA)/choline chloride (ChCl) and itaconic acid (IA)/ChCl. The as‐prepared ion‐conducting elastomers possess outstanding properties, including high transparency, conductivity, and the capability to adhere to various substrates. The elastomers also demonstrate ultra‐stretchability (up to 3900%) owing to a combination of covalent cross‐linking and noncovalent cross‐linking. In addition, the elastomers can recover up to 3250% strain and over 94.5% of their original conductivity after self‐healing at room temperature for 5 min, indicating remarkable mechanical and conductive self‐healing abilities. When utilized as strain sensors to monitor real‐time motion of human fingers, wrist, elbow, and knee joints, the elastomers exhibit stable and strong repetitive electrical signals, demonstrating excellent sensing performance for large‐scale movements of the human body. It is anticipated that these ion‐conducting elastomers will find promising applications in flexible and wearable electronics. [ABSTRACT FROM AUTHOR]

Motor circuits represent the main output of the central nervous system and produce dynamic behaviors ranging from relatively simple rhythmic activities like swimming in fish and breathing in mammals to highly sophisticated dexterous movements in humans. Despite decades of research, the development and function of motor circuits remain poorly understood. Breakthroughs in the field recently provided new tools and tractable model systems that set the stage to discover the molecular mechanisms and circuit logic underlying motor control. Here, we describe recent advances from both vertebrate (mouse, frog) and invertebrate (nematode, fruit fly) systems on cellular and molecular mechanisms that enable motor circuits to develop and function and highlight conserved and divergent mechanisms necessary for motor circuit development. [ABSTRACT FROM AUTHOR]

With the advancement of AI‐generated human motion, it is of increasing importance to think about how we distinguish real human motion from machine‐generated movements. In this study, we recruited professional performers to use the whole body to make a short movement to inform potential observers that they are real humans (instead of machines). Their movements were captured with a motion capture system (Vicon) and later reduced to dynamic point‐like displays (biological motion). They were interviewed after the recording to provide their acting strategies. Naive observers who did not participate in the motion data collection were recruited to watch these videos and judge whether the biological motions looked human‐like or not (YES/NO), as well as to report their judging criteria. The major factors extracted from these reports include kinematics, context, body mechanics, and principles of physical laws. We discuss the impact of these criteria and how they may possibly help improve the future generation of human‐like motions. [ABSTRACT FROM AUTHOR]

Objectives: The antisaccades (AS) task is considered a reliable indicator of inhibitory control of eye movements in humans. Achieving good AS performance requires efficient cognitive processes that are sensitive to changes in brain structure. White matter hyperintensities (WMH) can cause subcortical-cortical dysconnectivity, affecting diverse cognitive domains. Thus, the AS task was investigated in patients with WMH in cerebral small vessel disease (CSVD). Methods: In this retrospective study, 75 participants with WMH, determined by neuroimaging standards for CSVD research, were admitted to the Department of Neurology of Beijing Luhe Hospital, Capital Medical University from January 2021 to December 2022. All subjects underwent the AS task, Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), and 3.0T brain MRI. Additionally, 61 healthy subjects were recruited to characterize WMH profiles. Results: Compared to the control group, patients with WMH had a significantly increased AS error rate (49.81%, p = 0.001) and lower gain (76.00%, p = 0.042). The AS error rate was significantly higher in patients with WMH in the frontal lobe than in those without WMH (p = 0.004). After adjusting for confounders (age), a positive correlation was found between the AS error rate and MoCA scores for patients with WMH (coefficient = 0.262, p = 0.024). Conclusions: Patients with WMH due to CSVD exhibited abnormal AS performances, particularly in the frontal lobe. The eye movement paradigms, the new diagnostic forms in neurology, can be utilized to investigate the distributed cortical and subcortical systems involved in cognitive control processes, offering simple, well-tolerated and highly sensitive advantages over traditional measures. [ABSTRACT FROM AUTHOR]

Copyright of Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Musculoskeletal models and computational simulations are increasingly employed in clinical and research settings, as they provide insights into human biomechanics by estimating quantities that cannot be easily measured in vivo (e.g., joint contact forces). However, their clinical application remains limited by the lack of standardized protocols for developing personalized models, which in turn heavily rely on the modeler's expertise and require task-specific validation. While motor tasks like walking and cycling have been widely studied, simulating a maximal knee extensor dynamometry test remains unexplored, despite its relevance in rehabilitation. This study aims to fill this gap by investigating the minimum amount of experimental data required to accurately reproduce a maximal voluntary contraction test in silico. For nine healthy young females, four different subject-specific musculoskeletal models with increasing levels of personalization were developed by incorporating muscle volume data from medical images and electromyographic signal envelopes to adjust, respectively, muscle maximal isometric force and tetanic activation limits. At each step of personalization, simulation outcomes were compared to experimental data. Our findings suggest that to reproduce in silico accurately the isometric dynamometry test requires information from both medical imaging and electromyography, even when dealing with healthy subjects. [ABSTRACT FROM AUTHOR]

Conductive hydrogel strain sensors have attracted great attention in various fields. However, most conductive hydrogels are rigid due to the polymerization of conductive polymers, which not only affects wearer comfort but also causes environmental concerns due to their non-biodegradable nature. To address these limitations, researchers have begun incorporating natural polymer compounds into hydrogels, including starch-based hydrogels. However, starch-based hydrogels hinder their applications due to their brittle fracture, poor freezing resistance, and insufficient electrical conductivity. Herein, a multi-functional, environmentally friendly, degradable starch-based conductive hydrogel was developed using a binary system of water and ethylene glycol (EG) as the solvents, starch and polyvinyl alcohol (PVA) as the skeletons, calcium chloride (CaCl2) for conductivity, and gelatin and cellulose nanofibers to synergistically modify the physical cross-linked network. The hydrogel exhibited exceptional properties such as excellent stretchability (478.1%), high tensile strength (2.1 MPa), good toughness (3.7 MJ/m3), and good conductivity (0.22 S/m), as well as excellent anti-freezing and recyclability. Leveraging these properties, a wearable strain and temperature sensor with high sensitivity (GF = 0.74) and cycle stability over a wide strain range was developed, enabling convenient monitoring of human movement and body temperature physiological signals. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present study aimed to develop wearable, electrically conductive hygienic electrodes and their use for Transcutaneous Electrical Nerve Stimulation (TENS) application. The development of electrodes was done in two stages. Firstly, the dispersion of conductive activated carbon particles was done into flexible elastomer followed by its silver electroplating. The Orthogonal Array Testing (OATS) technique was used to optimize the silver-plating recipe. The Fourier transform infrared spectroscopy (FTIR) analysis confirmed the impact of activated carbon by increase in intensity of stretching bands. To improve electrode properties in response to various human body movements, resistivity changes due to stretching and repeated extension were tested on conductive elastomers. Increasing the extension degree allowed a very minute change in electrical resistivity. Therefore, the resistivity can be considered almost constant within the 0-60 % stretch range. While, a notable increase in electrical resistivity was observed after 70 % of stretch. However, the resistivity of elastomers remained stable even after repetitive extension (for over 100 cycles). Conversely, no significant change in resistivity was observed over time when subjected to a constant current. Furthermore, to minimize the effect of skin electrode resistivity during electrotherapy, the range of different pressure was applied over the surface of electrodes. Minimum value of resistivity about 1 Ω.mm was obtained at 6 N/cm2 of applied pressure. Additionally, hygienic properties, such as antiviral, antibacterial and antifungal were examined using different pathogens to assess the impact of the deposited silver particles. In the end, the durability of electrodes against washing and rubbing was confirmed. The potential applications of prepared electrodes are in the field of electrostimulation and electrotherapy etc. [ABSTRACT FROM AUTHOR]

Flexible sensors based on laser‐induced graphene (LIG) are widely used in wearable personal devices, with the morphology and lattice arrangement of LIG the key factors affecting their performance in various applications. In this study, femtosecond‐laser‐induced MXene‐composited graphene (LIMG) is used to improve the electrical conductivity of graphene by incorporating MXene, a 2D material with a high concentration of free electrons, into the LIG structure. By combining pump‐probe detection, laser‐induced breakdown spectroscopy (LIBS), and density functional theory (DFT) calculations, the morphogenesis and lattice structuring principles of LIMG is explored, with the results indicating that MXene materials are successfully embedded in the graphene lattice, altering both their morphology and electrical properties. The structural sparsity and electrical conductivity of LIMG composites (up to 3187 S m−1) are significantly enhanced compared to those of LIG. Based on these findings, LIMG has been used in wearable electronics. LIMG electrodes are used to detect uric acid, with a minimum detection limit of 2.48 µM. Additionally, LIMG‐based pressure and bending sensors have been successfully used to monitor human limb movement and pulse. The direct in situ femtosecond laser patterning synthesis of LIMG has significant implications for developing flexible wearable electronic sensors. [ABSTRACT FROM AUTHOR]

Although word predictability is commonly considered an important factor in reading, sophisticated accounts of predictability in theories of reading are lacking. Computational models of reading traditionally use cloze norming as a proxy of word predictability, but what cloze norms precisely capture remains unclear. This study investigates whether large language models (LLMs) can fill this gap. Contextual predictions are implemented via a novel parallel-graded mechanism, where all predicted words at a given position are pre-activated as a function of contextual certainty, which varies dynamically as text processing unfolds. Through reading simulations with OB1-reader, a cognitive model of word recognition and eye-movement control in reading, we compare the model's fit to eye-movement data when using predictability values derived from a cloze task against those derived from LLMs (GPT-2 and LLaMA). Root Mean Square Error between simulated and human eye movements indicates that LLM predictability provides a better fit than cloze. This is the first study to use LLMs to augment a cognitive model of reading with higher-order language processing while proposing a mechanism on the interplay between word predictability and eye movements. Author summary: Reading comprehension is a crucial skill that is highly predictive of later success in education. One aspect of efficient reading is our ability to predict what is coming next in the text based on the current context. Although we know predictions take place during reading, the mechanism through which contextual facilitation affects oculomotor behaviour in reading is not yet well-understood. Here, we model this mechanism and test different measures of predictability (computational vs. empirical) by simulating eye movements with a cognitive model of reading. Our results suggest that, when implemented with our novel mechanism, a computational measure of predictability provides better fits to eye movements in reading than a traditional empirical measure. With this model, we scrutinize how predictions about upcoming input affects eye movements in reading, and how computational approaches to measuring predictability may support theory testing. Modelling aspects of reading comprehension and testing them against human behaviour contributes to the effort of advancing theory building in reading research. In the longer term, more understanding of reading comprehension may help improve reading pedagogies, diagnoses and treatments. [ABSTRACT FROM AUTHOR]

In this study, a flexible sensor is successfully fabricated using self‐healing nanocomposite hydrogels for monitoring human movement. The eco‐friendly cellulose nanocrystals (CNCs) are used as nano‐reinforcing materials, and the mechanical properties and self‐healing efficiency of the materials are improved. The self‐healing efficiency of hydrogels are realized by introducing a variety of reversible non‐covalent interactions such as hydrogen bonding, borax chelation, and metal coordination. Notably, the mechanical strength and self‐healing efficiency of these nanocomposite hydrogels can reach 2.8 MPa and 89.9%, respectively. Importantly, these self‐healing nanocomposite hydrogels have been widely used in wearable flexible sensors to achieve high sensitivity to large‐scale human movement. It is of great significance to design functional materials with good biocompatibility, sensitivity, and mechanical strength for wearable sensors. [ABSTRACT FROM AUTHOR]

Introduction: Slip-related falls are a significant concern, particularly for vulnerable populations such as the elderly and individuals with gait disorders, necessitating effective preventive measures. This manuscript presents a biomechanical study of how the lower limbs react to perturbations that can trigger a slip-like fall, with the ultimate goal of identifying target specifications for developing a wearable robotic system for slip-like fall prevention. Methods: Our analysis provides a comprehensive understanding of the natural human biomechanical response to slip perturbations in both slipping and trailing legs, by innovatively collecting parameters from both the sagittal and frontal plane since both play pivotal roles in maintaining stability and preventing falls and thus provide new insights to fall prevention. We investigated various external factors, including gait speed, surface inclination, slipping foot, and perturbation intensity, while collecting diverse data sets encompassing kinematic, spatiotemporal parameters, electromyographic data, as well as torque, range of motion, rotations per minute, detection, and actuation times. Results: The biomechanical response to slip-like perturbations by the hips, knees, and ankles of the slipping leg was characterized by extension, flexion, and plantarflexion moments, respectively. In the trailing leg, responses included hip flexion, knee extension, and ankle plantarflexion. Additionally, these responses were influenced by gait speed, surface inclination, and perturbation intensity. Our study identified target range of motion parameters of 85.19°, 106.34°, and 95.23° for the hips, knees, and ankles, respectively. Furthermore, rotations per minute values ranged from 17.85 to 51.10 for the hip, 21.73 to 63.80 for the knee, and 17.52 to 57.14 for the ankle joints. Finally, flexion/extension torque values were estimated as -3.05 to 3.22 Nm/kg for the hip, -1.70 to 2.34 Nm/kg for the knee, and -2.21 to 0.90 Nm/kg for the ankle joints. Discussion: This study contributes valuable insights into the biomechanical aspects of slip-like fall prevention and informs the development of wearable robotic systems to enhance safety in vulnerable populations. [ABSTRACT FROM AUTHOR]

Dressing is a critical component of Activity of Daily Living (ADLs). The development of assistive technology for dressing tasks is urgently needed from the viewpoint of privacy, for example, to prevent the wearer from being seen in the nude. Despite their importance, these technologies are underutilized in garment donning compared to other ADLs. The reasons for the lack of utilization of assistive technology are that the task is complex, as it involves handling individuals with large individual differences in height and symptoms and requires manipulation of flexible clothing. Addressing these variances presents a formidable academic challenge. Our previous study identified periodic patterns in human movements during robot-assisted dressing and noted individual variability in these patterns. Capitalizing on this finding, we propose a novel control strategy for a robot that adapts to individual needs during dressing, employing the ‘Assist-As-Needed’ (AAN) principle from physical therapy. The proposed control method uses Central Pattern Generators (CPG) that can be synchronously controlled in response to external forces, enabling model-free control without human modeling. We utilize the Rowat-Selverston CPG model, which is recognized for its adaptive response to human motions in human-robot interactions such as with handshaking robots. A simulator of the Rowat-Selverston CPG was created. The output of the CPG was confirmed by inputting the data set obtained in previous studies, and the parameters were adjusted. The CPG output was then applied to the recorded target joint trajectory for the dressing assistance. We prepared a replay of the default trajectory, a trajectory with simple harmonic motion applied, and a trajectory with CPG output applied, and confirmed whether individual adaptation according to the AAN was possible through subject experiments. The experimental results showed that the Rowat-Selverston CPG control method enables individual adaptive dressing assistance according to the AAN principle. This study summarizes these methods and results and contributes to the realization of an individually adaptive system that follows the AAN principle, especially in developing assistive technology. [ABSTRACT FROM AUTHOR]

Flexible pressure sensors have attracted much attention because of their application prospects in wearable devices, electronic skin, and health monitoring. However, it is still difficult to obtain pressure sensors with excellent performance simply and efficiently. Indium tin oxide (ITO) is a widely used transparent conductive material, and polyvinyl alcohol (PVA) is an elastic insulating material with the advantages of easy processing and stable mechanical properties. ITO nano‐crystalline particles are dispersed into PVA polymers to form an ITO nanocrystalline‐PVA composite film with good electrical conductivity as a piezorestoresistive material, fabricating a pressure sensor with excellent performance. The pressure sensor possesses superior sensitivity (641.15 kPa−1), wide detection range (0–80 kPa), fast response time (10.93 ms), and recovery time (8.05 ms), and excellent stability (more than 1500 cycles). The pressure sensor shows excellent performance in a variety of applications, such as pulse testing, speech recognition, and detection of various joint movements of the human body (such as knees, fingers, elbows, etc.). The sensor has application prospects in health monitoring and motion perception. [ABSTRACT FROM AUTHOR]

Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as "flickering" in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy. [ABSTRACT FROM AUTHOR]

In the evolving realm of ergonomics, there is a growing demand for enhanced comfortability, visibility, and accessibility in the operation of engineering machinery. This study introduces an innovative approach to assess the ergonomics of a driller's cabin by utilizing a digital human. Through the utilization of inertial motion capture sensors, the method enables the operation of a virtual driller animated by real human movements, thereby producing more precise and realistic human–machine interaction data. Additionally, this study develops a simplified model for the human upper limbs, facilitating the calculation of joint forces and torques. An ergonomic analysis platform, encompassing a virtual driller's cabin and a digital human model, is constructed using Unity 3D. This platform enables the quantitative evaluation of comfortability, visibility, and accessibility. Its versatility extends beyond the current scope, offering substantial support for product development and enhancement. [ABSTRACT FROM AUTHOR]

With the aim of producing a photomechanical material for incorporation in soft microrobots, a one‐dimensional diene coordination polymer (CP) [Cd(F‐bpeb)(3‐CBA)2]n (CP1, F‐bpeb=4,4′‐((1E,1′E)‐(2,5‐difluoro‐1,4‐phenylene)bis(ethene‐2,1‐diyl))dipyridine, 3‐HCBA=3‐chlorobenzoic acid) was synthesized and characterized. Irradiation of CP1 with ultraviolet (UV) or visible light causes [2+2] photocycloaddition reactions resulting in the introduction of crystal strain which triggers various types of crystal movements. Composite films of CP1‐PVA (SC) fabricated by dispersing CP1 crystals into polyvinyl alcohol (PVA) solution allow amplification of the crystal movement so that the film strips exhibit fast and flexible curling upon photoirradiation. The composite films may be cut into long rectangular strips and folded to simulate soft microrobots which exhibit a variety of fast, flexible and continuous photomechanical movements resembling a human performing various gymnastic exercises. [ABSTRACT FROM AUTHOR]

Human movement augmentation is a rising field of research. A promising control strategy for augmented effectors involves utilizing electroencephalography through motor imagery (MI) functions. However, performing MI of a supernumerary effector is challenging, to which MI training is one potential solution. In this study, we investigate the validity of a virtual reality (VR) environment as a medium for eliciting MI neural activations for a supernumerary thumb. Specifically, we assess whether it is possible to induce a distinct neural signature for MI of a supernumerary thumb in VR. Twenty participants underwent a two-fold experiment in which they observed movements of natural and supernumerary thumbs, then engaged in MI of the observed movements. Spectral power and event related desynchronization (ERD) analyses at the group level showed that the MI signature associated with the supernumerary thumb was indeed distinct, significantly different from both the baseline and the MI signature associated with the natural thumb, while single-trial classification showed that it is distinguishable with a 78% and 69% classification accuracy, respectively. Furthermore, spectral power and ERD analyses at the group level showed that the MI signatures associated with directional movement of the supernumerary thumb, flexion and extension, were also significantly different, and single-trial classification demonstrated that these movements could be distinguished with 60% accuracy. Fine-tuning the models further increased the respective classification accuracies, indicating the potential presence of personalized features across subjects. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue on recent advances in the biomechanics of human movement and its clinical applications. It discusses the challenges of linking research projects to clinical practice and highlights the gaps between potential research activities and clinical applications. The special issue includes 10 articles on various aspects of the research field, providing insights into topics such as postural control adaptations in Parkinson's disease, gait analysis, and the relationship between humans and robots. These articles reflect current progress in the research field and offer valuable information for clinical applications. The document also emphasizes the importance of considering electromyographs (EMGs) and electroencephalograms (EEGs) in clinical practice, as well as the potential applications of artificial intelligence (AI) in biomechanics. It encourages further exploration of new research directions in human movement and its clinical applications. [Extracted from the article]

This paper explores the application of computer vision and mathematical modeling to analyze the intricate movements involved in weaving a traditional farming tool, the winnowing basket. By utilizing OpenPose algorithms, the study simplifies and visualizes the craftsmen's motions, particularly focusing on wrist movements. Video data of craftsmen in Chiba, Japan, creating Kizumi (place name) winnowing baskets is used as the basis for analysis. The extracted information is used to generate 2D motion trajectories of the wrist, allowing a comparison between beginners who watched parsed videos and those who watched the original videos in terms of skill acquisition and learning time. By visualizing human body behavior and combining statistical results, this study demonstrates the potential of artificial intelligence techniques such as computer vision for observing repetitive human movement and inheriting traditional skills. [ABSTRACT FROM AUTHOR]

The present study covers the fabrication of flexible piezoelectric nanogenerators and their application towards sustainable power generation. The rod-like structure of erbium-doped ZnO (EZ) nanoparticles prepared by the hydrothermal synthesis route was successfully incorporated inside the polyvinylidene fluoride (PVDF) matrix using the solution casting method. Solution casting is an easy and cost-effective method for fabricating laminated, thin, flexible and lightweight EZ-PVDF nanocomposite films. The formation of the desired crystallographic phase of EZ-PVDF nanocomposite films and the presence of rod-like EZ nanoparticles inside the PVDF matrix were confirmed using X-ray diffraction and FESEM. The enhancement of the β-phase fraction of the EZ-PVDF nanocomposite films as compared to bare PVDF was estimated using FTIR spectroscopy. The presence of a ferroelectric phase in the EZ-PVDF nanocomposite films was found due to the formation of a large area of interfaces between the EZ nanoparticles and the PVDF matrix. The maximum polarizations of 0.00696 μC cm-2 and 0.00683 μC cm-2 for two samples (EZP1 and EZP2, respectively) were observed at an electric field of 1.25 kV cm-1. The piezoelectric voltages were observed at relatively low frequencies for both nanocomposite films. The maximum piezoelectric voltages of 18.9 V and 15.5 V were observed at a 1 Hz frequency for EZP1 and EZP2, respectively. The output piezoelectric current of 16.88 mA and the maximum power density of 7773.68 W m-3 for EZP1 ensure its potential as an efficient piezoelectric nanogenerator with greater efficiency than those reported previously in published articles. The change in the piezoelectric voltage response of the nanocomposite films as a function of mechanical movement of human external body parts renders them the most suitable candidate for human--machine interfacing (HMI) applications, such as bending sensors and human motion sensors. [ABSTRACT FROM AUTHOR]

Introduction: Advances in motion capture technology include markerless systems to facilitate valid data collection. Recently, the technological reliability of this technology has been reported for human movement assessments. To further understand sources of potential error, biological reliability must also be determined. The aim of this study was to determine the day-to-day reliability for a three-dimensional markerless motion capture (MMC) system to quantify 4 movement analysis composite scores, and 81 kinematic variables. Methods: Twenty-two healthy men (n = 11; ... + SD; age = 23.0 ± 2.6 years, height = 180.4.8 cm, weight = 80.4 ± 7.3 kg) and women (n = 11; age = 20.8 ± 1.1 years, height = 172.2 ± 7.4 cm, weight = 68.0 ± 7.3 kg) participated in this study. All subjects performed 4 standardized test batteries consisting of 14 different movements on four separate days. A three-dimensional MMC system (DARI Motion, Lenexa, KS) using 8 cameras surrounding the testing area was used to quantify movement characteristics. 1 x 4 RMANOVAs were used to determine significant differences across days for the composite movement analysis scores, and RM-MANOVAs were used to determine test day differences for the kinematic data (p < 0.05). Intraclass correlation coefficients (ICCs) were reported for all variables to determine test reliability. To determine biological variability, mean absolute differences from previously reported technological variability data were subtracted from the total variability data from the present study. Results: No differences were observed for any composite score (i.e., athleticism, explosiveness, quality, readiness; or any of the 81 kinematic variables. Furthermore, 84 of 85 measured variables exhibited good to excellent ICCs (0.61-0.99). When compared to previously reported technological variability data, 62.3% of item variability was due to biological variability, with 66 of 85 variables exhibiting biological variability as the primary source of error (i.e., >50% total variability). Discussion: Combined, these findings effectively add to the body of literature suggesting sufficient reliability for MMC solutions in capturing kinematic features of human movement. [ABSTRACT FROM AUTHOR]

Non-verbal communication, especially eye contact, plays a crucial role in human interaction. Integrating eye contact capabilities into robotic behavior presents challenges that require comprehensive interdisciplinary collaboration from the fields of vision science and robotics. This study introduces an innovative methodology to improve the user's perception of the tabletop social robot Haru's direct gaze by explaining how eye and head movements can be managed through simulating visual perception processing. By considering the mechanisms of eye and head movements during visual perception processing, the robot's eye and head gaze behaviors are intended to replicate human gaze movements with Vestibulo-Ocular Movement (VOM) during face-to-face interaction, ultimately enhancing the user's perception of the robot's direct gaze. [ABSTRACT FROM AUTHOR]

Gelatin hydrogel with excellent biodegradability and biocompatibility is widely used in drug delivery, electronic devices, and flexible sensors. However, it is prone to dehydration, which severely restricts its applications. The incorporation of hydrated ions, for example choline chloride (ChCl), can not only improve its water retention capacity but also keep its biocompatibility. Unfortunately, the improvement in water retention is realized at the expense of mechanical properties because the ChCl can suppress the crystallization of gelatin. Here, ChCl is introduced into gelatin hydrogel using a simple post‐treatment to simultaneously improve its water retention capacity and mechanical properties. As a result, the resultant gelatin gel possesses a maximum fracture strength of 5.4 MPa with a strain of 623% and can maintain its weight for at least 30 days. In addition, the gelatin gel exhibits intriguing properties, such as self‐healing, anti‐freezing, anti‐drying, recycling, conductivity, and moldability, which can be successfully served as wearable sensors to monitor human movements. Further, a sensitive touch sensor based on electrode potential is established for the first time using the obtained gel, which can transmit messages through the touch potential signal. [ABSTRACT FROM AUTHOR]

The enaminone derivatives of sulfonamides (1-11) were obtained in good yield and high purity. Choline esterase (ChE) inhibitory activities of the novel compounds against AChE and BChE were determined by Ellman's method. Ki values of compounds for AChE and BChE enzymes were obtained in the ranges of 14.28-160.17 µM, and 8.30-324.27 µM, respectively. Compound, 9 presented good activity towards AChE and BChE with Ki values of 14.28 µM and 8.30 µM, respectively. Compounds 2 and 10 were found to be the most potent compounds showing cytotoxic effect (IC50 = 71.54 µg/mL and IC50 = 83.59 µg/mL), respectively, on lung cancer cell line (A549) and normal cells (Beas-2B) (IC50 = 164.62 µg/mL and IC50 = 155.64 µg/mL), respectively. The compounds have interacted with various proteins like AChE enzyme protein (PDB ID: 4M0E) and BChE enzyme protein (PDB ID: 5NN0). Finally, ADME/T analysis was performed to predict the movements of molecules in human metabolism. [ABSTRACT FROM AUTHOR]

With their unique electrical, mechanical, and surface properties, gold nanoparticles (AuNPs) open up new possibilities for sensor technology. In particular, conductive thin films constructed from ligand‐stabilized AuNPs are considered an ideal sensing platform due to their high surface area, excellent conductivity, and biocompatibility. However, most methods for making conductive AuNPs thin‐film sensors with excellent sensitivity require expensive equipment. In this work, an innovative resistive strain sensor consisting of AuNPs and poly (allylamine hydrochloride) (PAH) based on the mutual adsorption of positive and negative charges using a low‐cost layer‐by‐layer self‐assembly (LBL‐SA) approach on a flexible polyester substrate is developed. The conductance changes at low temperatures of the AuNPs/PAH agree with the Arrhenius‐type activation of charge transport. Additionally, the maximum gauge factor of the sensor is shown experimentally to be ≈656 when 1% strain is applied to the sensor film. This work demonstrates that the sensor detects body motions, eyeball movements, and facial micro‐expressions. For detecting eyeball movements and facial micro‐expressions, the macro‐recall can reach 91.5% and 98.8%. Simultaneously, the sensor can control the virtual avatar's eye movements and human facial micro‐expressions in VR. Therefore, nanoparticle‐based sensors can be extensively used in future applications related to healthcare and human‐computer interaction. [ABSTRACT FROM AUTHOR]

Assistive robotic platforms have recently gained popularity in various healthcare applications, and their use has expanded to social settings such as education, tourism, and manufacturing. These social robots, often in the form of bio-inspired humanoid systems, provide significant psychological and physiological benefits through one-on-one interactions. To optimize the interaction between social robotic platforms and humans, it is crucial for these robots to identify and mimic human motions in real time. This research presents a motion prediction model developed using convolutional neural networks (CNNs) to efficiently determine the type of motions at the initial state. Once identified, the corresponding reactions of the robots are executed by moving their joints along specific trajectories derived through temporal alignment and stored in a pre-selected motion library. In this study, we developed a multi-axial robotic arm integrated with a motion identification model to interact with humans by emulating their movements. The robotic arm follows pre-selected trajectories for corresponding interactions, which are generated based on identified human motions. To address the nonlinearities and cross-coupled dynamics of the robotic system, we applied a control strategy for precise motion tracking. This integrated system ensures that the robotic arm can achieve adequate controlled outcomes, thus validating the feasibility of such an interactive robotic system in providing effective bio-inspired motion emulation. [ABSTRACT FROM AUTHOR]

In today's society, robots are increasingly being developed and playing an important role in many fields of industry. Combined with advances in artificial intelligence, sensors, and design principles, these robots are becoming smarter, more flexible, and especially capable of interacting more naturally with humans. In that context, a comprehensive humanoid robot with human-like actions and emotions has been designed to move flexibly like a human, performing movements to simulate the movements of the human neck and head so that the robot can interact with the surrounding environment. The mechanical design of the emotional humanoid robot head focuses on the natural and flexible movement of human electric motors, including flexible suitable connections, precise motors, and feedback signals. The feedback control parts, such as the neck, eyes, eyebrows, and mouth, are especially combined with artificial skin to create a human-like appearance. This study aims to contribute to the field of biomimetic humanoid robotics by developing a comprehensive design for a humanoid robot head with human-like actions and emotions, as well as evaluating the effectiveness of the motor and feedback control system in simulating human behavior and emotional expression, thereby enhancing natural interaction between robots and humans. Experimental results from the survey showed that the behavioral simulation rate reached 94.72%, and the emotional expression rate was 91.50%. [ABSTRACT FROM AUTHOR]

The humanoid torso is crucial for the overall performance of a humanoid robot. Developing an effective humanoid spine is essential for enhancing this mechanism. This paper introduces a one-vertebral-disc unit inspired by human spine anatomy. A prototype was created using 3D-printed parts and commercially available components. Two general human-like motions are achieved using two servo motors and two pulleys, reducing the number of servo motors needed. The results indicate that a one-vertebral-disc unit can bend up to 15 degrees. The proposed mechanism functions effectively and successfully mimics human movements. It holds potential for integration into humanoid torsos, enabling efficient performance in human-like tasks in the future. [ABSTRACT FROM AUTHOR]

In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture "come" alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety. [ABSTRACT FROM AUTHOR]

BACKGROUND: Human gait pattern recognition and compliance control are key technologies for achieving high coordination and assistance between exoskeleton robots and human movements. OBJECTIVE: In order to improve the adaptability of exoskeleton robots to the human body, this paper proposes an exoskeleton compliance control strategy based on dual interaction torque phase separation control method. METHODS: A support phase swing phase split control strategy based on dual interaction torque is proposed. Utilize the interaction force of human joints and adopt a model-based method to control the support phase. By utilizing the interaction force of exoskeleton joints and using a torque closed-loop method to control the swing phase, a multi-state control method of motion is achieved. RESULTS: A lower limb exoskeleton knee joint testing platform is built to verify the proposed human gait recognition The effectiveness of human-machine interaction force identification and human-machine coupling system compliance control technology. CONCLUSION: The proposed control method can effectively adjust joint torque, enabling the exoskeleton robot to maintain balance and stability during the entire walking phase. [ABSTRACT FROM AUTHOR]

A cleanroom free optimized fabrication of a low-cost facile tungsten diselenide (WSe2) combined with chitosan-based hydrogel device is reported for multifunctional applications including tactile sensing, pulse rate monitoring, respiratory rate monitoring, human body movements detection, and human electrophysiological signal detection. Chitosan being a natural biodegradable, non-toxic compound serves as a substrate to the semiconducting WSe2 electrode which is synthesized using a single step hydrothermal technique. Elaborate characterization studies are performed to confirm the morphological, structural, and electrical properties of the fabricated chitosan/WSe2 device. Chitosan/WSe2 sensor with copper contacts on each side is put directly on skin to capture human body motions. The resistivity of the sample was calculated as 26 kΩ m−1. The device behaves as an ultrasensitive pressure sensor for tactile and arterial pulse sensing with response time of 0.9 s and sensitivity of around 0.02 kPa−1. It is also capable for strain sensing with a gauge factor of 54 which is significantly higher than similar other reported electrodes. The human body movements sensing can be attributed to the piezoresistive character of WSe2 that originates from its non-centrosymmetric structure. Further, the sensor is employed for monitoring respiratory rate which measures to 13 counts/min for healthy individual and electrophysiological signals like ECG and EOG which can be used later for detecting numerous pathological conditions in humans. Electrophysiological signal sensing is carried out using a bio-signal amplifier (Bio-Amp EXG Pill) connected to Arduino. The skin-friendly, low toxic WSe2/chitosan dry electrodes pave the way for replacing wet electrodes and find numerous applications in personalized healthcare. [ABSTRACT FROM AUTHOR]

The closing of national borders was one of the most far-reaching policy measures adopted to limit the spread of the virus during the coronavirus disease 2019 pandemic. Despite its unprecedented nature and far-reaching impact on individual lives, blocking almost all human movements not only into but also out of states, popular support for this measure was surprisingly high. How can this support be explained? Using an original 4-wave panel dataset across 11 countries together with cross-national policy data, we explore individual and contextual drivers of border closure support throughout 2020. We find that higher support can partly be explained by political attitudes related to cross-border mobility, such as distrust in foreigners or right-wing ideology. Primarily, however, support for closing borders is shaped by respondents' evaluation of governments' handling of the pandemic, as well as the intensity and timing of the constraining measures. We also find that support wanes over time, which may indicate a policy fatigue effect. Interestingly, health concerns (exposure to the virus) have almost no influence on support. Even in this exceptional situation, and when confronted with a new policy issue, citizens' preferences are thus primarily a politically driven response to government measures. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) offers a novel method in human–machine interactions (HMIs) since it is a distinct physiological electrical signal that conceals human movement intention and muscle information. Unfortunately, the nonlinear and non-smooth features of sEMG signals often make joint angle estimation difficult. This paper proposes a joint angle prediction model for the continuous estimation of wrist motion angle changes based on sEMG signals. The proposed model combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network, where the TCN can sense local information and mine the deeper information of the sEMG signals, while LSTM, with its excellent temporal memory capability, can make up for the lack of the ability of the TCN to capture the long-term dependence of the sEMG signals, resulting in a better prediction. We validated the proposed method in the publicly available Ninapro DB1 dataset by selecting the first eight subjects and picking three types of wrist-dependent movements: wrist flexion (WF), wrist ulnar deviation (WUD), and wrist extension and closed hand (WECH). Finally, the proposed TCN-LSTM model was compared with the TCN and LSTM models. The proposed TCN-LSTM outperformed the TCN and LSTM models in terms of the root mean square error (RMSE) and average coefficient of determination (R2). The TCN-LSTM model achieved an average RMSE of 0.064, representing a 41% reduction compared to the TCN model and a 52% reduction compared to the LSTM model. The TCN-LSTM also achieved an average R2 of 0.93, indicating an 11% improvement over the TCN model and an 18% improvement over the LSTM model. [ABSTRACT FROM AUTHOR]

In the area of flexible electronics, pressure sensors are a widely utilized variety of flexible electronics that are both indispensable and prevalent. The importance of pressure sensors in various fields is currently increasing, leading to the exploration of materials with unique structural and piezoelectric properties. Perovskite-based materials are ideal for use as flexible pressure sensors (FPSs) due to their flexibility, chemical composition, strain tolerance, high piezoelectric and piezoresistive properties, and potential integration with other technologies. This article presents a comprehensive study of perovskite-based materials used in FPSs and discusses their components, performance, and applications in detecting human movement, electronic skin, and wireless monitoring. This work also discusses challenges like material instability, durability, and toxicity, the limited widespread application due to environmental factors and toxicity concerns, and complex fabrication and future directions for perovskite-based FPSs, providing valuable insights for researchers in structural health monitoring, physical health monitoring, and industrial applications. [ABSTRACT FROM AUTHOR]

The actuation of traditional anthropomorphic hands is relatively complex, and there is little research on humanoid skin. In view of the above problems, this study proposed an anthropomorphic hand with biological and kinematic characteristics of the human hand, including structural skeletal parts and humanoid skin. Firstly, the overall structure and control system of the anthropomorphic hand were designed. Then, the humanoid skin was fabricated and its physical and mechanical properties were tested. Based on the overall structure of the rope-driven anthropomorphic hand, its motion characteristics were simulated and analyzed using multi-body dynamics software ADAMS/Cable. Finally, control experiment verifies the performance of the anthropomorphic hand described in this paper. The results are that the proposed anthropomorphic hand can reproduce the movement characteristics of the human hand well, and can grip objects of different shapes, different sizes and different weights stably, with a maximum grip force of 11.91 N measured. [ABSTRACT FROM AUTHOR]

Noise is a ubiquitous component of motor systems that leads to behavioral variability of all types of movements. Nonetheless, systems-based models investigating human movements are generally deterministic and explain only the central tendencies like mean trajectories. In this paper, a novel approach to modeling kinematic variability of movements is presented and tested on the oculomotor system. This approach reconciles the two prominent philosophies of saccade control: displacement-based control versus velocity-based control. This was achieved by quantifying the variability in saccadic eye movements and developing a stochastic model of its control. The proposed stochastic dual model generated significantly better fits of inter-trial variances of the saccade trajectories compared to existing models. These results suggest that the saccadic system can flexibly use the information of both desired displacement and velocity for its control. This study presents a potential framework for investigating computational principles of motor control in the presence of noise utilizing stochastic modeling of kinematic variability. [ABSTRACT FROM AUTHOR]

[Display omitted] Advanced numerical simulations of the mechanical behavior of human skin require thorough calibration of the material's constitutive models based on experimental ex vivo mechanical tests along with images of tissue microstructure for a variety of biomedical applications. In this work, a total of 14 human healthy skin samples and 4 additional scarred skin samples were experimentally analyzed to gain deep insights into the biomechanics of human skin. In particular, second harmonic generation (SHG) microscopy was used to extract detailed images of the distribution of collagen fibers, which were subsequently processed using a three-dimensional Fourier transform-based method recently proposed by the authors to quantify the distribution of fiber orientations. Mechanical tests under both biaxial and uniaxial loading were performed to calibrate the relevant mechanical parameters of two widely used constitutive models of soft fiber-reinforced biological tissues that account for non-symmetrical fiber dispersion. The calibration of the models allowed us to identify correlations between the mechanical parameters of the constitutive models considered. Constitutive models for soft collagenous tissues can accurately reproduce the complex nonlinear and anisotropic mechanical behavior of skin. However, a comprehensive analysis of both microstructural and mechanical parameters is still missing for human skin. In this study, these parameters are determined by combining biaxial mechanical tests and SHG stacks of collagen fibers on ex vivo healthy human skin samples. The constitutive parameters are provided for two widely used hyperelastic models and enable accurate characterization of skin mechanical behavior for advanced numerical simulations. [ABSTRACT FROM AUTHOR]

Drones have found extensive utility in both public and personal places. Consequently, the accurate detection and tracking of drones have emerged as pivotal endeavors in terms of ensuring their optimal performance. This research paper introduces a novel application for discerning the movements of humans and drones from cloud points through the utilization of frequency-modulated continuous wave radar. The dynamic density-based spatial clustering of applications with noise (Dynamic-DBSCAN) algorithm was employed to classify cloud points into separate groups corresponding to the number of objects within the tracking area. Compared to the original DBSCAN algorithm, this method increased accuracy by about 16.8%, achieving an accuracy of up to 93.99%. Subsequently, a trio of deep learning algorithms--long short-term memory, deep neural network, and residual network (ResNet)--were harnessed to ascertain the categorization of each group as either human or drone. According to the results, ResNet achieved the best accuracy rate of 97.72%. Overall, this study underscores the efficacy of the proposed method in accurately and efficiently distinguishing between human and drone entities for effective monitoring and management. [ABSTRACT FROM AUTHOR]