Human Motion Research Articles

HiSC4D: Human-Centered Interaction and 4D Scene Capture in Large-Scale Space Using Wearable IMUs and LiDAR.

We introduce HiSC4D, a novel Human-centered interaction and 4D Scene Capture method, aimed at accurately and efficiently creating a dynamic digital world, containing large-scale indoor-outdoor scenes, diverse human motions, rich human-human interactions, and human-environment interactions. By utilizing body-mounted IMUs and a head-mounted LiDAR, HiSC4D can capture egocentric human motions in unconstrained space without the need for external devices and pre-built maps. This affords great flexibility and accessibility for human-centered interaction and 4D scene capturing in various environments. Taking into account that IMUs can capture human spatially unrestricted poses but are prone to drifting for long-period using, and while LiDAR is stable for global localization but rough for local positions and orientations, HiSC4D employs a joint optimization method, harmonizing all sensors and utilizing environment cues, yielding promising results for long-term capture in large scenes. To promote research of egocentric human interaction in large scenes and facilitate downstream tasks, we also present a dataset, containing 8 sequences in 4 large scenes (200 to 5,000 [Formula: see text]), providing 36 k frames of accurate 4D human motions with SMPL annotations and dynamic scenes, 31k frames of cropped human point clouds, and scene mesh of the environment. A variety of scenarios, such as the basketball gym and commercial street, alongside challenging human motions, such as daily greeting, one-on-one basketball playing, and tour guiding, demonstrate the effectiveness and the generalization ability of HiSC4D. The dataset and code will be publicly available for research purposes.

Environment-tolerant, inherently conductive and self-adhesive gelatin-based supramolecular eutectogel for flexible sensor

Although hydrogels have attracted increasing attention in the stretchable devices, the low adhesion properties and poor environmental adaptation still seriously restrict their development and application. Herein, we focused on the interaction between polymer networks with disperse media and their resultant influence on gel performance, and constructed self-adhesive and environment-tolerant gelatin/polyacrylamide supramolecular-polymer double-network (Gelatin/PAM SP-DN) eutectogels using multiple supramolecular interactions between natural macromolecule and well-designed deep eutectic solvent (DES). The dual networks of Gelatin/PAM SP-DN eutectogels produced significant supramolecular forces with DES, including hydrogen bonding and electrostatic interaction, contributing to enhance the energy dissipation capacity. Additionally, the Gelatin-PAM SP-DN eutectogels were more prone to generate strong bonding force to various substrates, showcasing both in-situ and ex-situ adhesion performance, and even being used for wet and underwater adhesion. The eutectogels revealed excellent environmental tolerance to maintain excellent mechanical flexibility, conductivity and adhesion at high and low temperatures, ensuring the constructed sensor to sensitively and reliably perceive strain, pressure and human motions over a wide temperature range. Also, the eutectogel demonstrated great potential as a temperature sensor. This work opens up a new horizon in the design of multifunctional and environment-tolerant natural macromolecule-based gel materials for flexible electronics, human-machine interaction and health diagnosis.

Hot spots of resistance: Transit centers as breeding grounds for airborne ARG-carrying bacteriophages

The presence of pathogenic bacteria and antibiotic resistance genes (ARGs) in urban air poses a significant threat to public health. While prevailing research predominantly focuses on the airborne transmission of ARGs by bacteria, the potential influence of other vectors, such as bacteriophages, is often overlooked. This study aims to investigate the characteristics of phages and ARGs in aerosols originating from hospitals, public transit centers, wastewater treatment plants, and landfill sites. The average abundance of ARGs carried by phages in the public transit centers was 8.81 ppm, which was 2 to 3 times higher than that at the other three sites. Additionally, the abundance of ARGs across different risk levels at this site was also significantly higher than at the other three sites. The assembled phage communities bearing ARGs in public transit centers are chiefly governed by homogeneous selection processes, likely influenced by human movement. Furthermore, observations at public transit sites revealed that the average abundance ratio of virulent phages to their hosts was 1.01, and the correlation coefficient between their auxiliary metabolic genes and hosts' metabolic genes was 0.59, which were 20 times and 3 times higher, respectively, than those of temperate phages. This suggests that virulent phages may enhance their survival by altering host metabolism, thereby aiding the dispersion of ARGs and bacterial resistance. These revelations furnish fresh insights into phage-mediated ARG transmission, offering scientific substantiation for strategies aimed at preventing and controlling resistance within aerosols.

High-strength polyvinyl alcohol/gelatin/LiCl dual-network conductive hydrogel for multifunctional sensors and supercapacitors

The synthesis of conductive hydrogels with high mechanical strength, toughness, optimal fracture growth rate and the capability to detect diverse human body movements poses a significant challenge in the realm of flexible electronics. In this study, a one-pot technique utilized effectively to fabricate conductive materials by doping LiCl into a mixture of polyvinyl alcohol (PVA) and gelatin. The PVA/gelatin/LiCl0.3(PGL) conductive hydrogel demonstrates exceptional robustness, flexibility, and resistance to deformation, enabling the monitoring of various physiological signals such as temperature and humidity. Additionally, the PGL demonstrates exceptional elongation properties (up to 1111.32 %), high lifting capacity (up to 25 kg), resistance to deformation, and sustained stability of peak signals even after 300 cycles at 50 % strain. The hydrogel electrolyte exhibits a conductivity of 2.114 S/m at 25 °C and a specific capacitance of up to 48.75 F/g, along with favorable mechanical and electrochemical characteristics. These findings suggest that the PVA/gelatin/LiCl0.3 hydrogel supercapacitor (PGLSC) conductive hydrogel shows significant potential for integration into flexible electronics and wearable technology devices.

Customized flexible iontronic pressure sensors: Multilevel microstructures by 3D-Printing for enhanced sensitivity and broad pressure range

Flexible capacitive pressure sensors stand out for their energy efficiency, rapid responsiveness, and exceptional durability, with their performance hinging on the intricate design of microstructures and the strategic selection of dielectric materials. However, creating cost-effective and practical fabrication methods for these components continues to pose a significant challenge. This study unveils an innovative iontronic flexible pressure sensor that integrates an ionic layer with sophisticated multilayer microstructures, setting new benchmarks in pressure sensing technology. The sensor operates within an expansive pressure range, achieving sensitivities as high as 92.14 kPa−1 at pressures from 0 to 50 kPa, and 22.96 kPa−1 at pressures ranging from 50 to 800 kPa, demonstrating extraordinary sensitivity. It also achieves an exceptionally low detection limit of 6.67 Pa and features swift response times of 56 ms to activate and 18 ms to deactivate. Enhanced by the addition of CuSO4, the sensor not only withstands freezing conditions but also significantly boosts sensitivity. Moreover, this sensor is capable of detecting human joint movements and subtle pressure fluctuations under high-pressure conditions, suggesting a breakthrough in the field. This research presents a simple, reliable, and cost-efficient method for developing flexible iontronic pressure sensors, poised to revolutionize applications in wide-ranging pressures and adverse temperature environments.

Fishing net-inspired PVA-chitosan-CNT hydrogels with high stretchability, sensitivity, and environmentally stability for textile strain sensors

Soft electronic products are being extensively investigated in diverse applications including sensors and devices, due to their superior softness, responsiveness, and biocompatibility. One-dimensional (1-D) fiber electronic devices are recognized for their lightweight, wearable, and stretchable qualities, thus emerging as critical constituents for seamless integration with the human body and attire, exhibiting great potential in wearable applications. However, wearable conductive hydrogel fibers usually face challenges in combining stretchability and excellent stability, notably in high-temperature environment. Herein, a novel stretchable conductive hydrogel fiber, namely PVA-CS-CNT (Polyvinyl Alcohol-Chitosan-Carbon Nanotube) hydrogel fiber, was successfully prepared through a straightforward low-temperature process. This hydrogel fiber not only maintains stable signal transmission at high temperatures but also exhibits significant mechanical and sensing capabilities, ensuring signal stability during repetitive cyclic stretching. Inspired by fishing net, textile sensors were fabricated by weaving PVA-CS-CNT hydrogel fibers, which offered breathability, high stability (withstanding over 500 stretch cycles), high sensitivity (detecting strains as low as 1 %), and exceptional mechanical strength (exceeding 17 MPa). The wearable sensor could not only accurately monitor human movements like stretching and bending, but also adeptly captured delicate signals such as pulses and sounds. These characteristics demonstrated the potential applications of the hydrogel fibers encompassing human motion tracking, intelligent textiles, and soft robotics.

Magnetron sputtering preparation of flexible ZnO/AlN thin-films sensors with hybrid piezoelectric effect for broad-range human motions detection

Wearable sensors with excellent comfort, flexibility and fast response are widely applied in human-machine interactions, artificial skin, motion detection and healthcare monitoring. However, preparation processes of the most of current piezoelectric sensors are relatively expensive and complicate, limiting their mass productions in the applications. This work demonstrates the superior piezoelectric performance and stability of ZnO/AlN flexible thin films prepared by magnetron sputtering for broad-range human motions detection, and first-principles calculation are applied to reveal the complicate piezoelectric effect of the hybrid system. The ZnO/AlN hybrid thin films sensor exhibits superior piezoelectric performance and excellent reliability, with presenting high outputs voltage (3.63 V), high degree of response speed (42.33 ms), water stability and robust performance upon 10000 cycles. First-principles calculations reveal that formation of the ZnO/AlN interface facilitates charge transport and improves efficiency of carrier transport, and results in reduced in band gap and enhanced electrical conductivity. By applying the sensors in human health monitoring, the underlying behaviors can be determined by output voltage of the sensor in real-time. This work offers a sensitive, simple structured and robust solution for human motion detection.

Construction of high strength PVA/cellulose conductive hydrogels based on sodium citrate/aluminium chloride dual ions regulation

Hydrogels used for flexible sensing usually require a good balance between mechanical strength and conductivity. In this study, polyvinyl alcohol/carboxymethyl cellulose/cellulose nanofibers (PVA/CMC/CNF) hydrogels with multi-hierarchical structures were firstly prepared by adjusting the CNF content. Then, PVA/CMC/CNF-xM with excellent mechanical properties and conductivity were prepared by cyclic freezing-thawing and sodium citrate/aluminium chloride (Na3Cit/AlCl3) dual ions salt equilibrium methods. Results showed that at an ion concentration of 3 mol/L, PVA/CMC/CNF-3 M hydrogel exhibited a tensile strength, elongation at break and conductivity of 3.41 MPa, 1271 % and 0.35 S/m, respectively. The structural evolution of PVA/CMC/CNF-xM conductive hydrogels were studied, and the results indicated that Cit3− formed numerous intermolecular hydrogen bonds, while Al3+ could strongly coordinate with carboxyl and hydroxyl groups between the polysaccharide chains. Meanwhile, PVA/CMC/CNF-3 M possessed a low strain detection limit of 1 %, making it not only can be used for human motion monitoring, but also information encoding and transmission. This work may provide a facile approach for preparing high-strength and conductive hydrogels, which can be applied in flexible wearable electronic devices and information transmission.

GUESS: GradUally Enriching SyntheSis for Text-Driven Human Motion Generation.

In this article, we propose a novel cascaded diffusion-based generative framework for text-driven human motion synthesis, which exploits a strategy named GradUally Enriching SyntheSis (GUESS as its abbreviation). The strategy sets up generation objectives by grouping body joints of detailed skeletons in close semantic proximity together and then replacing each of such joint group with a single body-part node. Such an operation recursively abstracts a human pose to coarser and coarser skeletons at multiple granularity levels. With gradually increasing the abstraction level, human motion becomes more and more concise and stable, significantly benefiting the cross-modal motion synthesis task. The whole text-driven human motion synthesis problem is then divided into multiple abstraction levels and solved with a multi-stage generation framework with a cascaded latent diffusion model: an initial generator first generates the coarsest human motion guess from a given text description; then, a series of successive generators gradually enrich the motion details based on the textual description and the previous synthesized results. Notably, we further integrate GUESS with the proposed dynamic multi-condition fusion mechanism to dynamically balance the cooperative effects of the given textual condition and synthesized coarse motion prompt in different generation stages. Extensive experiments on large-scale datasets verify that GUESS outperforms existing state-of-the-art methods by large margins in terms of accuracy, realisticness, and diversity.

Neural Control for Human-Robot Interaction with Human Motion Intention Estimation

Neural Control for Human-Robot Interaction with Human Motion Intention Estimation

Vision-based approach for human motion detection and smart appliance control

This study focuses on the use of computer vision technology and motion detection sensors to create an intelligent system that recognizes human presence in monitored spaces. The system uses a relay module for automation and control of household appliances while sensing motion detection, operated by an ESP32 microcontroller. This innovative solution addresses two major issues in home automation: reliable human presence recognition and seamless appliance control. The research merges a camera-based vision system with motion sensors, comparing motion and vision-based identification. The ESP32 microcontroller improves motion detection precision and context awareness by integrating motion sensors and computer vision technologies. The integration of a camera module allows real-time analysis and recognition of human presence, reducing false alarms. The relay module also enables automated control of home appliances, synchronizing and feedbacking operations with sensed human presence. The dynamic adaptation of the system improves user convenience and energy efficiency.

Vat Photopolymerization 3D Printing of High-Performance Ionic Organogel for Multifunctional Sensors

Ionic gels present a promising solution to meet the growing demand for stretchable and conductive materials in wearable and flexible devices. Compared to ionic hydrogels, which exhibit poor thermal and electrical stability, ionic organogels demonstrate significant potential due to their superior performance characteristics. However, the fabrication of ionic organogels that exhibit high mechanical properties, transparency, and stability remains a significant challenge. In this paper, we report the fabrication of an ionic organogel using vat photopolymerization 3D printing with Projection Micro-Stereolithography technology under mild conditions. The 3D-printed ionic organogel exhibits outstanding transparency (93.81% in the range of 400-800nm), high stretchability (elongation at break of 2782%), tunable mechanical properties, excellent ionic conductivity, electrical stability with a high decomposition voltage of 4V, and thermal stability up to 300°C. The strain sensor based on the ionic organogel is capable of monitoring human movement, including both large-scale movements and subtle signals, with high sensitivity and extreme-temperature tolerance. In addition, the ionic organogel-based pressure and temperature sensor with dual functionality demonstrates excellent temperature and pressure sensing capabilities, even at extreme temperatures up to 100°C (boiling point of water). Thus, the as-fabricated ionic organogel presents an excellent choice of material for wearable and flexible devices, particularly for applications operating in harsh and extreme-temperature environments.

A Review of 3D Human Pose Estimation

Abstract. Visual perception and human body recognition are fundamental capabilities required for effective and safe interactions between artificial intelligence (AI), computer vision, and humans in real-world scenarios. Recent groundbreaking developments in AI and computer vision have resulted in major advancements in human body recognition technology. However, research in human body recognition is still in the early stages of the product lifecycle. Identifying the three-dimensional locations of the joints in the human body from pictures or videos is known as 3D posture estimation. Although it is widely used in areas like human motion analysis and robotics, it continues to be a difficult task due to challenges such as depth ambiguity and the scarcity of robust datasets. Over the past decade, numerous methods have been developed, many of which are based on deep learning, significantly improving the performance of existing benchmarks. A comprehensive literature review of this field is crucial for future development. However, in nowadays,more and more such research has mainly concentrated on traditional techniques, requirement for a comprehensive examination of tools based on deep learning. This paper delivers a thorough overview of current deep learning-based 3D pose estimation algorithms, outlining their advantages and limitations while providing a detailed understanding of the field. It also explores commonly used benchmark datasets and methods for analyzing human poses in unlabeled field images, providing a thorough comparative analysis. Finally, insights are provided to aid in the design of future models and algorithms.

3D printing for capacitive flexible pressure sensors of small pressure high sensitivity with microdome-labyrinth structure

Abstract Capacitive flexible pressure sensors have excellent sensing characteristics such as ductility and high sensitivity, and are capable of accurately detecting small pressures generated by human movement and human-computer interaction. However, small pressure sensors fabricated using most conventional preparation methods (e.g., stencil molding) do not provide high enough resolution for small pressure measurements. Therefore, in this paper, capacitive flexible pressure sensors that can be applied to motion signal detection and motion sensing are prepared using digital light processing (DLP) printing technology. Elastomers with microdome and labyrinth composite structures are prepared using polyurethane acrylate (PUA) materials. At the same time, the sensor is capable of detecting minute pressures as low as 1pa and has a wide detection range with a sensitivity S of 0.037kPa-1 in the 0-20kPa stress range and 0.007kPa-1 in the 300kPa-400kPa stress range.

Dancing robots: aesthetic engagement is shaped by stimulus and knowledge cues to human animacy

IntroductionArtificial intelligence (AI) and robots are increasingly shaping the aesthetic preferences of art consumers, influencing how they perceive and engage with artistic works. This development raises various questions: do cues to the humanness of the origin of an artwork or artist influence our aesthetic preferences?.MethodsAcross two experiments, we investigated how the perception and appreciation of dance is influenced by cues to human animacy. We manipulated Agent Form (human-like or robot-like dancer), Belief about Movement Source (human motion capture or computer animation), Source of Choreography (human- or computer-generated), and Belief about Choreography Source (believed to be human- or computer-generated).ResultsResults pointed toward agent congruence: In Experiment 1, robot agents were preferred when the movement source was believed to be computer animation. In Experiment 2, robot agents were preferred when the choreography was believed to be computer-generated, while choreographies believed to be human-generated were generally preferred. Participants could not accurately identify the actual source of choreography. These results persisted beyond the effects of age, dance expertise, technological expertise, attitudes toward AI, and perceived familiarity, complexity, evocativeness, technical competence, or reproducibility of the dance. Dance expertise, technological expertise, and attitudes toward AI independently impacted aesthetic judgments.DiscussionThese findings provide insights into the design of robotic dance, highlighting features of dance choreography and audience characteristics that influence aesthetic engagement. To enhance AI-driven creative productions, shaping perceptions will be crucial for better audience reception and engagement.

Interactive Haptic System with Multimodal Tactile Sensing and Hydraulic Feedback for Realistic Human–Machine Interaction

With the increasing demand for immersive haptic experiences, multimodal tactile sensing and feedback have become crucial for enhancing user interaction. In this work, an interactive haptic system comprising a robotic hand and wearable tactile feedback device is introduced to facilitate tactile information exchange between humans and robots. The robotic hand, equipped with force and temperature sensors, mimics human movements through vision recognition and captures detailed tactile data. The data are then transmitted to the user through a hydraulic feedback device, providing realistic touch sensations. The dual capabilities of the hydraulic mechanism to simultaneously simulate both pressure and temperature sensations simplify the device structure and enhance reliability compared to systems that require multiple tactile actuators. The force feedback system delivers pressures ranging from 0.5 to 10.5 N across five levels while the temperature feedback mechanism provides thermal sensations ranging from 3 to 50 °C across three levels. Extensive experimental validations demonstrate the system's effectiveness in applications such as remote robotic surgery and telepresence, enabling users to perceive real‐time contact pressure and temperature during remote manipulation tasks. The proposed system, capable of delivering multimodal tactile feedback through a single actuator, represents a significant step toward more immersive and realistic haptic experiences, highlighting its broad applicability.

Movement Sensing Opportunities for Monitoring Dynamic Cognitive States

In occupational domains such as sports, healthcare, driving, and military, both individuals and small groups are expected to perform challenging tasks under adverse conditions that induce transient cognitive states such as stress, workload, and uncertainty. Wearable and standoff 6DOF sensing technologies are advancing rapidly, including increasingly miniaturized yet robust inertial measurement units (IMUs) and portable marker-less infrared optical motion tracking. These sensing technologies may offer opportunities to track overt physical behavior and classify cognitive states relevant to human performance in diverse human–machine domains. We describe progress in research attempting to distinguish cognitive states by tracking movement behavior in both individuals and small groups, examining potential applications in sports, healthcare, driving, and the military. In the context of military training and operations, there are no generally accepted methods for classifying transient mental states such as uncertainty from movement-related data, despite its importance for shaping decision-making and behavior. To fill this gap, an example data set is presented including optical motion capture of rifle trajectories during a dynamic marksmanship task that elicits variable uncertainty; using machine learning, we demonstrate that features of weapon trajectories capturing the complexity of motion are valuable for classifying low versus high uncertainty states. We argue that leveraging metrics of human movement behavior reveals opportunities to complement relatively costly and less portable neurophysiological sensing technologies and enables domain-specific human–machine interfaces to support a wide range of cognitive functions.

A Bistate Intelligent Eutectogel Material with Water‐Regulated Stiffness for Diverse Designed Functionality

AbstractThe design of stiffness‐changeable synthetic materials that can mimic their biological counterparts in the production of adaptable structures and functions is highly important but remains challenging. Here, the development of a bioinspired intelligent material composed of a deep eutectic solvent (DES) and an ionically crosslinked polymer network that is capable of switching reversibly between hard plastic and soft elastic states in response to water is reported. The eutectogel stiffness can be dramatically switched and flexibly regulated by water, which can activate salt ionization and shield the ionic crosslink. This design principle is extensively applied to multiple polymers, producing a series of bistate materials that exhibit water‐regulated capabilities ranging from self‐healing, shape memory, remolding, antifreezing, anticompressing, and stretchability to conductivity. These bistate materials are used for diverse functions, such as performing mechanical tasks, sensing and monitoring human movements, lighting LEDs, and outputting signals. This work opens an avenue for the development of next‐generation stiffness‐changeable materials with diverse functionalities for various designed applications.

Environmental displacement and political instability: Evidence from Africa

Does environmental displacement provoke political instability? Though migration has long been considered an intermediary in the causal path between environmental change and political upheaval, the relationship remains theoretically underdeveloped and evidence has been limited. This article examines the impact of displacement caused by sudden-onset natural hazards on disruptive antigovernment events including armed conflict, protests and violent riots. It leverages the new Environmental Displacement Dataset (EnDis), an original dataset that identifies quantities of human movement in response to six types of sudden-onset natural hazards in Africa from 1990 to 2017, including floods, storms, wildfires, landslides, earthquakes and volcanic activity. The results of the analyses show that while environmental displacement is not associated with civil war onset or protests, it does increase the incidence of attacks by armed non-state actors and violent riots. Importantly, these destabilizing effects occurr primarily (1) in the context of displacement driven by floods and storms, and (2) when levels of displacement are well above average. Collectively, these findings portend deepening security crises and violent political upheaval as climate change drives more frequent episodes of extreme weather and excessive environmental displacement.

Kajian Fenomena Socio Entrepreneurship terhadap Pemberdayaan Manusia (Studi Kasus pada Pengembangan Kualitas Hidup Penyintas Disabilitas)

Conceptual studies on socio-entrepreneurship have yet to be widely discussed in Indonesia, but the trend of socio-entrepreneurship has been widely present and developed in the community. Social problems in society have given rise to initiatives by several people to carry out a human empowerment movement to improve the quality of life for other humans. This study aims to examine the phenomenon of socio-entrepreneurship towards human empowerment with a case study on the development of the quality of life of survivors of disabilities, especially survivors of Cornelia de Lange Syndrome (CdLS) in Indonesia. This study uses a literature study method related to theories, concepts, findings, previous research results, and case studies. The results of this study indicate that socio-entrepreneurship or social entrepreneurship is an integration related to the creation of socio-economic values ​​based on empathy and caring for each other. The implementation of socio-entrepreneurship in society is based on various internal and external backgrounds. It faces many challenges and opportunities that need to be studied in more detail.