Human Motion Research Articles

Heat and mass transfer analysis on peristaltic transport of PTT fluid with wall properties

The current study is intended to examine the effect of heat and mass transfer on the peristaltic propulsion of the Pan-Thien-Tanner fluid in a cylindrical tube with wall properties. The complexity of the system is reduced by long wavelength and low Reynolds number strategy. The physical behavior of the peristaltic motion of the PTT fluid is explained and elucidated graphically for several values of various parameters connected with this flow problem. It has been revealed that raising the Source/Sink parameter values and the Weissenberg number results in a higher velocity profile. The outcomes embrace the key to understanding the human organs' physiological fluid motion, such as the chime moment in the small intestine and esophagus.

Wood Cell Wall Nanoengineering toward Anisotropic, Strong, and Flexible Cellulosic Hydrogel Sensors.

Achieving highly ionic conductive hydrogels from natural wood remains challenging owing to their insufficient surface area and low number of active sites on the cell wall. This study proposes a viable strategy to design a strong and anisotropic wood-based hydrogel through cell wall nanoengineering. By manipulating the microstructure of the wood cell wall, a flexible cellulosic hydrogel is achieved through Schiff base bonding via the polyacrylamide and cellulose molecular chains. This results in excellent flexibility and mechanical properties of the wood hydrogel with tensile strengths of 22.3 and 6.1 MPa in the longitudinal and transverse directions, respectively. Moreover, confining aqueous salt electrolytes within the porous structure gives anisotropic ionic conductivities (19.5 and 6.02 S/m in the longitudinal and transverse directions, respectively). The wood-based hydrogel sensor has a favorable sensitivity and a stable working performance at a low temperature of -25 °C in monitoring human motions, thereby demonstrating great potential applications in wearable sensor devices.

Sustainable and high performance MXene hydrogel with interlocked structure for machine learning-facilitated human-interactive sensing

Wearable epidermal electronics fabricated from MXene-based conductive hydrogels have attracted considerable interest because of their promising prospects in real-time health monitoring and human–computer interaction sensing. However, the development of a sustainable hydrogel with exceptional mechanical toughness, high conductivity, and self-adhesion is crucial to reliably capture signals through polymer network designs that establish robust interactions between MXene nanosheets and network substrates while minimizing electronic waste generated by discarded sensors. This study presents the fabrication of a sustainable conductive MXene-based dual-network hydrogel through skillful assembly of MXene nanosheets, β-cyclodextrin with azobenzene-modified polyacrylic acid chains, and poly (vinyl alcohol) polymer networks. The hydrogel demonstrates exceptional mechanical properties (strength at breaking ∼ 625 kPa, elongation at break ∼ 630 %) attributed to the presence of numerous hydrogen bonds and host–guest interactions, along with superior electrical conductivity (23.65 S/cm) and self-adhesion. The hydrogel demonstrates exceptional sensitivity across a wide range of strains, enabling the detection of various human movements and achieving successful applications in handwriting recognition and sign language translation. Under acidic condition and UV irradiation, the disassembly of AZO and β-CD as well as the untangling of PVA chains entanglement facilitate the release and recycling of MXene. This study presents a technological platform that holds promise for developing conductive hydrogels with enhanced sensing capabilities, catering to the requirements of stretchable electronic skin and intelligent robotic systems.

A highly stretchable and self-adhesive cellulose complex hydrogels based on PDA@Fe3+ mediated redox reaction for strain sensor

As the application of conductive hydrogels in the field of wearable smart devices is gradually deepening, a variety of hydrogel sensors with high mechanical properties, strong adhesion, fast self-healing, and excellent conductivity are emerging. However, it is still a great challenge to manufacture hydrogel sensors combining multiple properties. Herein, we leveraged the dynamic redox reaction occurring between polydopamine (PDA) and Fe3+ to induce ammonium persulfate (APS) to generate free radicals, thereby initiating the copolymerization of hydroxyethyl methacrylate (HEMA) and acrylic acid (AA) monomers. Then, polypyrrole-encapsulated cellulose nanofibers (PPy@CNF) and carboxymethylcellulose (CMC) were incorporated as conductive reinforced nanofillers and interpenetrating network skeleton. The obtained hydrogel cross-linked through reversible metal-ligand bonds, π-π stacking and abundant hydrogen bonding demonstrated great mechanical properties (strength 240.4 kPa, strain 1175 %) and self-healing ability (88.96 %). Particularly, the gel displayed ultrahigh durability and skin adhesive ability (75 kPa after 10 cycles), surpassing previous skin adhesion hydrogels. Furthermore, through the synergistic conductive effect of PPy@CNF and Fe3+, the prepared hydrogel sensor possessed high sensitivity (GF = 1.89) with a wide sensing range (~1000 %), which could realize the human body's daily motion detection, and had a promising application in flexible wearable electronics.

Multibody dynamics-based musculoskeletal modeling for gait analysis: a systematic review.

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.

Cation-π Interactions Based Conductive Hydrogels with Slide-Ring Structure Toward Super Long-Time in-air/Underwater Linear Sensing and Communication.

Conductive hydrogels (CHs) are attracted more attention in the flexible wearable sensors field, however, how to stably apply CHs underwater is still a big challenge. In order to achieve the usage of CHs in aquatic environments, the integrated properties such as water retention ability, resistance to swelling, toughness, adhesiveness, linear GF sensing, and long-term usage are necessary to consider, but rarely reported in the previous reports. This paper proposes CHs prepared using cationic and aromatic monomers along with polyrotaxanes-based crosslinkers. Due to the intermolecular cation-π interactions and topological slide-ring-based polyrotaxanes, the CHs exhibit good mechanical performance, adhesive nature, and anti-swelling properties. The presence of slide-ring-based topological architecture effectively mitigates stress concentration. Additionally, the encapsulation of PA allows CHs to maintain functionality even after 240 days of direct placement at room temperature. Notably, the designed CHs exhibit linear sensitivity in detecting land/underwater human motions, and serve as Morse code signal transmitters for information transmission. Thus, the designed CHs may have broad applications in the underwater wearable sensors field.

CP decomposition-based algorithms for completion problem of motion capture data

Motion capture (MoCap) technology is an essential tool for recording and analyzing movements of objects or humans. However, MoCap systems frequently encounter the challenge of missing data, stemming from mismatched markers, occlusion, or equipment limitations. Recovery of these missing data is imperative to maintain the reliability and integrity of MoCap recordings. This paper introduces a novel application of the tensor framework for MoCap data completion. We propose three completion algorithms based on the canonical polyadic (CP) decomposition of tensors. The first algorithm utilizes CP decomposition to capture the low-rank structure of the tensor. However, relying only on low-rank assumptions may be insufficient to deal with complex motion data. Thus, we propose two modified CP decompositions that incorporate additional information, SmoothCP and SparseCP decompositions. SmoothCP integrates piecewise smoothness prior, while SparseCP incorporates sparsity prior, each aiming to improve the accuracy and robustness of MoCap data recovery. To compare and evaluate the merit of the proposed algorithms over other tensor completion methods in terms of several evaluation metrics, we conduct numerical experiments with different MoCap sequences from the CMU motion capture dataset.

UWB distance estimation errors in (non-)line of sight situations within the context of 3D analysis of human movement

Abstract Integrated Ultrawideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensors are becoming popular for indoor localization applications, as a higher accuracy can be achieved than with just MIMU sensors. These integrated sensors could extend stability and accuracy in the field of 3D analysis of human movement (3D AHM) if they can deliver position estimates with an accuracy close to 1 cm. Achieving this high accuracy of 1 cm remains challenging, with most studies reporting position estimation errors around 5 cm, often due to Non-Line of Sight (NLOS) conditions and systematic UWB sensor distance estimation errors. Studying the distance error characteristic of UWB in situations of 3D AHM is essential to deal with these errors. While research on UWB distance errors in Line of Sight (LOS) and NLOS situations exists, few studies focus on the NLOS errors caused by the human body and were not in the relevant scenarios of 3D AHM. Therefore, this article examines UWB sensor performance and distance error characteristics in LOS and NLOS situations typical for the 3D AHM. Both the LOS and NLOS situations were studied in the typical 3D AHM distance range of 0.2 m to 2 m. The NLOS situations were studied first with a human subject as NLOS causing object and then with simulated human body segments (PVC pipes filled with water) of varying diameters. In LOS situations, consistent systematic bias errors were observed, along with incidental errors at specific positions in the room. In NLOS scenarios caused by the human and simulated body segments, a consistent and reproducible overestimation of distances was found. The reproducibility of these errors based on relative node and object positions suggests that systematic mitigation methods could significantly reduce errors, enabling more accurate and reproducible 3D human movement analysis.

Emergence of social phases in human movement

Emergence of social phases in human movement

Identifying Phases in Low-Speed Human Movement

Identifying Phases in Low-Speed Human Movement

Composite dyadic models for spatio-temporal data.

Mechanistic statistical models are commonly used to study the flow of biological processes. For example, in landscape genetics, the aim is to infer spatial mechanisms that govern gene flow in populations. Existing statistical approaches in landscape genetics do not account for temporal dependence in the data and may be computationally prohibitive. We infer mechanisms with a Bayesian hierarchical dyadic model that scales well with large data sets and that accounts for spatial and temporal dependence. We construct a fully connected network comprising spatio-temporal data for the dyadic model and use normalized composite likelihoods to account for the dependence structure in space and time. We develop a dyadic model to account for physical mechanisms commonly found in physical-statistical models and apply our methods to ancient human DNA data to infer the mechanisms that affected human movement in Bronze Age Europe.

Molecular and Cellular Mechanisms of Motor Circuit Development.

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.

Reversibly Compressible Silanated Cellulose Nanofibril Aerogel for Triboelectric Taekwondo Scoring Sensors.

The integration of bio-based materials into triboelectric nanogenerators (TENGs) for energy harvesting from human body motions has sparked considerable research attention. Here, a silanated cellulose nanofibril (SCNF) aerogel is reported for structurally reliable TENGs and reversely compressible Taekwondo scoring sensors under repeated impacts. The preparation of the aerogel involves silanizing cellulose nanofibers (CNFs) with vinyltrimethoxysilane (VTMS), following by freeze-drying and post-heating treatment. The SCNF aerogel with crosslinked physico-chemical bonding and highly porous network is found to exhibit superior mechanical strength and reversible compressibility as well as enhanced water repellency and electron-donating ability. The TENG having a tribo-positive SCNF layer exhibits exceptional triboelectric performances, generating a voltage of 270V, current of 11µA, and power density of 401.1mWm-2 under an applied force of 8N at a frequency of 5Hz. With its inherent merits in material composition, structural configuration, and device sensitivity, the SCNF TENG demonstrates the capability to seamlessly integrate into a Taekwondo protection gear, serving as an efficient self-powered sensor for monitoring hitting scores. This study highlights the significant potential of a facilely fabricated SCNF aerogel for the development of high-performance, bio-friendly, and cost-effective Bio-TENGs, enabling their application as self-powered wearable devices and sports engineering sensors.

Seeking justice on social media: Funas as a localized form of Latin American youth activism

A funa, a public denouncement aimed at raising moral condemnation of a person accused of perpetrating a crime or injustice, has become a major digital activism instrument in Latin America, particularly in Chile. Originated in the human rights movement in the 1990s, funas re-emerged as a new form of online activism that hybridized with a Latin American and historical form of protest to exert informal justice through social media. Drawing on a face-to-face survey of Chilean youth aged 18–29 years, we examine the factors that explain young people’s attitudes and behaviors regarding funas. We find that funas are gendered, with issues like sexual harassment and misconduct being the primary subject of funas. We also show that women hold more positive views about funas and are more likely to engage in online funas. Finally, our findings indicate that individuals who trust the judicial system are less likely to share funas on social media.

Self-Healable, Low-Hysteresis, and Notch-Insensitive Conductive Hydrogels Enabled by Viscoelastic Nanoparticles for Sensitive Human Motion Sensing

Self-Healable, Low-Hysteresis, and Notch-Insensitive Conductive Hydrogels Enabled by Viscoelastic Nanoparticles for Sensitive Human Motion Sensing

Self-powered human movement measurement with a unidirectional ratchet driven wearable energy harvester

A large amount of energy is generated during human movement which is mostly not effectively utilized. To collect human movement energy and utilize it rationally, a wearable piezoelectric-electromagnetic energy harvester (WPEEH) driven by a unidirectional ratchet is proposed, which adopts the principle of up-conversion to convert mechanical energy into electrical energy. The electromagnetic power generator unit is used to obtain energy to power the sensing circuit, while piezoelectric generator unit can be used for both energy generation and self-powered sensing. A spring push rod and a ratchet structure are used to convert the swinging of the human limb into the unidirectional rotation of the device to enhance its energy harvesting efficiency. The theoretical model of the WPEEH is established, and the experimental test platform is built. The results show that the WPEEH is able to generate a maximum power of 17.2 mW at an oscillation frequency of 1.5 Hz. In the actual wearable test, the proposed energy harvester is able to supply the harvested energy to the low-power electronic devices through capacitors, and it enables to light up 42 LEDs as well as to drive the digital thermo-hygrometer to work continuously and stably. Apart from having excellent properties of a high output power and low drive frequency, the proposed energy harvester also exhibits a high energy density (7.1 × 10–5 W/cm3). What’s more, the speed of human movement can be predicted and calculated for different individuals by testing the results of the swing frequency with a maximum error of 2.71 %. The investigation proves that the proposed WPEEH can measure human movement and has great potential in the field of power supply for low-power electronic devices.

Leadership and the ethics of hope: Václav Havel and the Charter 77 human rights movement in Czechoslovakia

Hope as a feature in both authentic and spiritual leadership theory has become a diminished moral concept, departing from its philosophical and theological roots, and distorted by excessive positivity, success, instrumentalization, cultural bias, and ideology. Following Ciulla’s (2014) assertion that biography can do much to inform leadership theory, this paper contributes a richer understanding of hope through the though case of Václav Havel and the Charter 77 human rights movement in Czechoslovakia using French existentialist Gabriel Marcel’s ethics of hope. Five elements of Marcel’s philosophy demonstrate hope’s transforming vulnerability amidst technological control, relationship with despair, active presence, creative fidelity, and transcending power in love. Examining Czechoslovakia’s human rights movement through the lens of Marcellian ethics provides insight into the role of absurdity and truth-telling as a precursor to hope, taking responsibility for failure, overcoming fear through the “solidarity of the shaken,” creative fidelity through living in truth and using humor, and love manifesting as sacrifice and openness to seeing one’s contribution to the moral contamination of society under totalitarianism.

Functional Data Analysis: An Introduction and Recent Developments.

Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available through a code and data supplement and on GitHub.

Hot topic: Avian influenza subtype H5N1 in US dairy—A preliminary dairy foods perspective

In February and March of 2024, an unusual illness began affecting dairy herds primarily in Texas and neighboring states. The causative agent of this illness was ultimately confirmed in late March 2024 to be a strain of highly pathogenic avian influenza H5N1 belonging to clade 2.3.4.4b. In the months following the emergence of this viral disease in cattle, infections have spread to at least 191 herds in 13 states at the time of this writing in August 2024, primarily through cattle and human movement. Surprisingly, early examination of raw milk samples from clinically affected animals indicated that the virus had an affinity for the mammary tissue, and viral shedding into raw milk occurred at high levels, exceeding 108 log10 50% tissue culture infectious dose (TCID50) in some cases. These high viral loads coupled with evidence that farm cats who consumed raw milk from clinically ill animals were infected and exhibited high mortality rates, raised concerns about the safety of the US milk supply for human consumption. To date, 4 cow-associated human infections have been reported, all from farm employees with direct contact with infected animals. Several parameters ultimately affect the theoretical public health risk from consumption of dairy products manufactured from a milk supply containing H5N1, namely (1) initial viral load, (2) persistence of H5N1 in raw milk, (3) viral inactivation through processing practices including pasteurization, and (4) human susceptibility and infectious dose. In the short period since the emergence of this disease in dairy cattle in the United States, research has begun to answer these critical questions, although our knowledge is still quite limited at this time. Here we review the literature available from the current H5N1 outbreak in US dairy cattle, as well as selected relevant literature from previous research in other animal agriculture sectors, that affect our current understanding of the parameters associated with the food safety risk of this disease in the US dairy supply chain.

Multifunctional organohydrogels enabling sensitive strain sensing and self-powered triboelectricity

Hydrogels have broad application prospects in portable strain sensors and triboelectric nanogenerators (TENG), and have attracted great attention in the field of wearable electronic devices and energy transfer devices. However, the development of multifunctional gels that can meet a variety of application scenarios is still a challenging problem. In this study, a multifunctional poly(acrylamide-co-2-acrylamide-2-methylpropanesulfonic acid)/lignosulfonate/Zr4+ organohydrogel noted as “P(AM-co-AMPS)/LS/Zr4+” was fabricated based on dynamic metal coordination bonds and hydrogen bonds, which could be used as flexible strain sensor and TENG electrode material. The organohydrogel based strain sensor could monitor both large and tiny human movements due to the high sensitivity (GF = 4.37, up to 200 %) and circulation stability. The assembled organohydrogel-based triboelectric nanogenerator (O-TENG) could generate an open-circuit voltage of 70.47 V and a short-circuit current of 25.21 μA. It is worth mentioning that the O-TENG could light up 44 LED bulbs by tapping with the palm at −20 °C, and the electronic output performance of the O-TENG after self-healing was unaffected. Additionally, the O-TENG exhibited enormous potentials in the biomechanical energy harvesting and self-powered motion sensors in the areas of human movements detection, human–computer interaction, electronic skin and self-powered devices.