Search Results (15)

Background: Humans interacting with virtual objects is becoming common due to the popularity of the devices adopting the mixed reality (MR) techniques. Assessing hand functions using these devices for medical purposes provides alternatives in addition to the traditional hand function assessment techniques. Objectives: The objectives were to compare the movement time (MT) of handing a real and a virtual object between post-surgery wrist fracture patients and healthy adults and to determine the correlation between the MT and commonly adopted hand function indicators. Methods: An experiment was performed. A total of 29 participants, including 17 patients and 12 healthy adults, joined. All the participants moved a real or a virtual tube from an origin to a destination. A set of MR device was adopted to generate the virtual object. The MTs were analyzed to compare differences between the patients and the healthy adults. Regression models were developed to predict the MT under experimental conditions. Results: The MT of the surgical hand was significantly longer than that of the nonsurgical hand of the patients and was significantly longer than that of the left hand of the healthy adults. The MT was negatively correlated with the commonly adopted hand function indicators, including grip strength, range of motion, hand dexterity score, and Modified Mayo Wrist Score. Conclusions: The anticipation that the MT of interacting with virtual objects for patients may reveal hand function characteristics for post-surgery patients was supported. The regression models developed could reveal the progression of hand function recovery for these patients. Having patients interact with virtual objects could be a supplemental approach in assessing their hand functions. Full article

In recent years, the realization of a society in which humans and robots coexist has become highly anticipated. As a result, robots are expected to exhibit versatility regardless of their operating environments, along with high responsiveness, to ensure safety and enable dynamic task execution. To meet these demands, we design a comprehensive system composed of two primary components: high-speed skeleton tracking and path planning. For tracking, we implement a high-speed skeleton tracking method that combines deep learning-based detection with optical flow-based motion extraction. In addition, we introduce a dynamic search area adjustment technique that focuses on the target joint to extract the desired motion more accurately. For path planning, we propose a high-speed, geometry-informed potential field model that addresses four key challenges: (P1) avoiding local minima, (P2) suppressing oscillations, (P3) ensuring adaptability to dynamic environments, and (P4) handling obstacles with arbitrary 3D shapes. We validated the effectiveness of our high-frequency feedback control and the proposed system through a series of simulations and real-world collision-free path planning experiments. Our high-speed skeleton tracking operates at 250 Hz, which is eight times faster than conventional deep learning-based methods, and our path planning method runs at over 10,000 Hz. The proposed system offers both versatility across different working environments and low latencies. Therefore, we hope that it will contribute to a foundational motion generation framework for human–robot collaboration (HRC), applicable to a wide range of downstream tasks while ensuring safety in dynamic environments. Full article

Human skin, the body’s largest organ, plays a crucial role in perceiving mechanical stimulation and facilitating interaction with the external environment. Leveraging the unique attributes of human skin, electronic skin technology aimed at replicating and surpassing the capabilities of natural skin holds significant promise across various domains, including medical care, motion tracking, and intelligent robotics. In recent research, triboelectric nanogenerators have emerged as a compelling solution for addressing the energy challenge in electronic skins. Triboelectric nanogenerators harness the combination of the triboelectric effect and electrostatic induction to efficiently convert mechanical energy into electrical power, serving as self-powered sensors for electronic skins, which possess the advantages of self-powered operation, cost-effectiveness, and compatibility with a wide range of materials. This review provides an introduction to the working principles and the four operational modes of triboelectric nanogenerators, highlighting the functional features of electronic skins, such as stretchability, self-healing, and degradability. The primary focus is on the current applications of self-powered electronic skins based on triboelectric nanogenerators in medical care, motion tracking, and machine tactile recognition. This review concludes by discussing the anticipated challenges in the future development of self-powered electronic skins based on triboelectric nanogenerators. This review holds practical significance for advancing the practical use of self-powered electronic skins based on triboelectric nanogenerators and offers valuable guidance for individuals interested in pursuing scientific and healthy endeavors. Full article

This paper proposes a data-driven human-like driver model (HDM) based on the analysis and understanding of human drivers’ behavior in path-tracking tasks. The proposed model contains a visual perception module and a decision-making module. The visual perception module was established to extract the visual inputs, including road information and vehicle motion states, which can be perceived by human drivers. The extracted inputs utilized for lateral steering decisions can reflect specific driving skills exhibited by human drivers like compensation control, preview behavior, and anticipation ability. On this basis, an adaptive neuro-fuzzy inference system (ANFIS) was adopted to design the decision-making module. The inputs of the ANFIS include the vehicle speed, lateral deviation in the near zone, and heading angle error in the far zone. The output is the steering wheel angle. ANFIS can mimic the fuzzy reasoning characteristics of human driving behavior. Next, a large amount of human driving data was collected through driving simulator experiments. Based on the data, the HDM was established. Finally, the results of the joint simulation under PreScan/MATLAB verified the superior performances of the proposed HDM. Full article

In recent times, there has been a notable global phenomenon characterized by a double predicament arising from the concomitant rise in worldwide life expectancy and a significant decrease in birth rates. The emergence of this phenomenon has posed a significant challenge for governments worldwide. It not only poses a threat to the continued viability of state-funded welfare programs, such as social security, but also indicates a potential decline in the future workforce and tax revenue, including contributions to social benefits. Given the anticipated escalation of these issues in the forthcoming decades, it is crucial to comprehensively examine the extension of the human lifespan to evaluate the magnitude of this matter. Recent research has focused on utilizing stochastic differential equations as a helpful means of describing the dynamic nature of mortality rates, in order to tackle this intricate issue. The usage of these models proves to be superior to deterministic ones due to their capacity to incorporate stochastic variations within the environment. This enables individuals to gain a more comprehensive understanding of the inherent uncertainty associated with future forecasts. The most important aims of this study are to fit and compare stochastic differential equation models for mortality (the geometric Brownian motion and the stochastic Gompertz model), conducting separate analyses for each age group and sex, in order to generate forecasts of the central mortality rates in France up until the year 2030. Additionally, this study aims to compare the outcomes obtained from fitting these models to the central mortality rates in Portugal. The results obtained from this work are quite promising since both stochastic differential equation models manage to replicate the decreasing central mortality rate phenomenon and provide plausible forecasts for future time and for both populations. Moreover, we also deduce that the performances of the models differ when analyzing both populations under study due to the significant contrast between the mortality dynamics of the countries under study, a consequence of both external factors (such as the effect of historical events on Portuguese and French mortality) and internal factors (behavioral effect). Full article

In the fourth industrial revolution, the scale of execution for interactive applications increased substantially. These interactive and animated applications are human-centric, and the representation of human motion is unavoidable, making the representation of human motions ubiquitous. Animators strive to computationally process human motion in a way that the motions appear realistic in animated applications. Motion style transfer is an attractive technique that is widely used to create realistic motions in near real-time. motion style transfer approach employs existing captured motion data to generate realistic samples automatically and updates the motion data accordingly. This approach eliminates the need for handcrafted motions from scratch for every frame. The popularity of deep learning (DL) algorithms reshapes motion style transfer approaches, as such algorithms can predict subsequent motion styles. The majority of motion style transfer approaches use different variants of deep neural networks (DNNs) to accomplish motion style transfer approaches. This paper provides a comprehensive comparative analysis of existing state-of-the-art DL-based motion style transfer approaches. The enabling technologies that facilitate motion style transfer approaches are briefly presented in this paper. When employing DL-based methods for motion style transfer, the selection of the training dataset plays a key role in the performance. By anticipating this vital aspect, this paper provides a detailed summary of existing well-known motion datasets. As an outcome of the extensive overview of the domain, this paper highlights the contemporary challenges faced by motion style transfer approaches. Full article

Over the years, several bare metal and crack-based strain sensors have been proposed for various fields of science and technology. However, due to their low gauge factor, metal-based strain sensors have limited practical applications. The crack-based strain sensor, on the other hand, demonstrated excellent sensitivity and a high gauge factor. However, the crack-based strain sensor exhibited non-linear behavior at low strains, severely limiting its real-time applications. Generally, the crack-based strain sensors are fabricated by generating cracks by bending a polymer film on which a metal layer has been deposited with a constant curvature. However, the random formation of cracks produces nonlinear behavior in the crack sensors. To overcome the limitations of the current state of the art, we propose a V-groove-based metal strain sensor for human motion monitoring and Morse code generation. The V-groove crack-based strain sensor is fabricated on polyurethane acrylate (PUA) using the modified photolithography technique. During the procedure, a V-groove pattern formed on the surface of the sensor, and a uniform crack formed over the entire surface by concentrating stress along the groove. To improve the sensitivity and selectivity of the sensor, we generated the cracks in a controlled direction. The proposed strain sensor exhibited high sensitivity and excellent fidelity compared to the other reported metal strain sensors. The gauge factor of the proposed V-groove-induced crack sensor is 10-fold higher than the gauge factor of the reported metal strain sensors. In addition, the fabricated V-groove-based strain sensor exhibited rapid response and recovery times. The practical feasibility of the proposed V-groove-induced crack-based strain sensor is demonstrated through human motion monitoring and the generation of Morse code. The proposed V-groove crack sensor can detect multiple motions in a variety of human activities and is anticipated to be utilized in several applications due to its high durability and reproducibility. Full article

In 1969, American artist Robert Mallary (1917–1997) coined the term ‘transductive art’ to describe an approach to art based on the notion of receiving energy from one system and retransmitting it, often in a different form, to another. Long before the realm of techno-art became a recognizable construct, Mallary was interested in a system of relationships, seeking in his words, ‘an element of perfection’ in combinations of materials and technologies to make ‘a beautiful whole’. From his experiments with the Mexican Muralists to assemblage and Neo-Dada sculpture, and finally, his synergistic relationship with the computer, Mallary’s work addressed the place of the human in a technological world. He instigated one of the first American computer art curriculums within a fine art department, developing early examples of software created by artists for use by artists. His espousal of the digital to become a ‘Supermedium’, led him to conceptualise a ‘spatial-synesthetic art’, a multi-media immersive environment combining three-dimensional projected visual elements, motion, and sound. Although unrealised, this system anticipated future VR/virtual reality developments such as the ‘Cave Automatic Virtual Environment’ (CAVE™) system developed at the University of Illinois, Chicago, in 1992. The current review will therefore argue, by example, that Mallary deserves a prominent position in the history of techno-art, and by virtue of both the several emerging influences he had the insight to recognise and bring together and his numerous subsequent contributions as simultaneously an artist, a theorist, and an educator. Full article

Catching flying objects is a challenging task in human–robot interaction. Traditional techniques predict the intersection position and time using the information obtained during the free-flying ball motion. A common pain point in these systems is the short ball flight time and uncertainties in the ball’s trajectory estimation. In this paper, we present the Robot Anticipation Learning System (RALS) that accounts for the information obtained from observation of the thrower’s hand motion before the ball is released. RALS takes extra time for the robot to start moving in the direction of the target before the opponent finishes throwing. To the best of our knowledge, this is the first robot control system for ball-catching with anticipation skills. Our results show that the information fused from both throwing and flying motions improves the ball-catching rate by up to 20% compared to the baseline approach, with the predictions relying only on the information acquired during the flight phase. Full article

Motion sickness (MS) is a syndrome associated with symptoms like nausea, dizziness, and other forms of physical discomfort. Automated vehicles (AVs) are potent at inducing MS because users are not adapted to this novel form of transportation, are provided with less information about the own vehicle’s trajectory, and are likely to engage in non-driving related tasks. Because individuals with an especially high MS susceptibility could be limited in their use of AVs, the demand for MS mitigation strategies is high. Passenger anticipation has been shown to have a modulating effect on symptoms, thus mitigating MS. To find an effective mitigation strategy, the prototype of a human–machine interface (HMI) that presents anticipatory ambient light cues for the AV’s next turn to the passenger was evaluated. In a realistic driving study with participants (N = 16) in an AV on a test track, an MS mitigation effect was evaluated based on the MS increase during the trial. An MS mitigation effect was found within a highly susceptible subsample through the presentation of anticipatory ambient light cues. The HMI prototype was proven to be effective regarding highly susceptible users. Future iterations could alleviate MS in field settings and improve the acceptance of AVs. Full article

Aging is a progressive functional decline in organs and tissues over time and typically represents the accumulation of psychological and social changes in a human being. Diverse diseases, such as cardiovascular, musculoskeletal, and neurodegenerative disorders, are now understood to be caused by aging. While biological assessment of aging mainly focuses on the gradual changes that occur either on the molecular scale, for example, alteration of gene expression and epigenetic modification, or on larger scales, for example, changes in muscle strength and cardiac function, the mechanics that regulates the behavior of individual cells and interactions between the internal elements of cells, are largely missing. In this study, we show that the dynamic features of migrating cells across different human ages could help to establish the underlying mechanism of biological age-dependent cellular functional decline. To determine the relationship between cellular dynamics and human age, we identify the characteristic relationship between cell migration and nuclear motion which is tightly regulated by nucleus-bound cytoskeletal organization. This analysis demonstrates that actomyosin contractility-dependent nuclear motion plays a key role in cell migration. We anticipate this study to provide noble biophysical insights on biological aging in order to precisely diagnose age-related chronic diseases. Full article

Heatwaves and air pollution are serious environmental problems that adversely affect human health. While related studies have typically employed ground-level data, the long-term and episodic characteristics of meteorology and air quality at higher altitudes have yet to be fully understood. This study developed a 3-Dimensional Real-timE Atmospheric Monitoring System (3DREAMS) to measure and analyze the vertical profiles of horizontal wind speed and direction, vertical wind velocity as well as aerosol backscatter. The system was applied to Hong Kong, a highly dense city with complex topography, during each season and including hot-and-polluted episodes (HPEs) in 2019. The results reveal that the high spatial wind variability and wind characteristics in the lower atmosphere in Hong Kong can extend upwards by up to 0.66 km, thus highlighting the importance of mountains for the wind environment in the city. Both upslope and downslope winds were observed at one site, whereas downward air motions predominated at another site. The high temperature and high concentration of fine particulate matter during HPEs were caused by a significant reduction in both horizontal and vertical wind speeds that established conditions favorable for heat and air pollutant accumulation, and by the prevailing westerly wind promoting transboundary air pollution. The findings of this study are anticipated to provide valuable insight for weather forecasting and air quality studies. The 3DREAMS will be further developed to monitor upper atmosphere wind and air quality over the Greater Bay Area of China. Full article

Various neural network based methods are capable of anticipating human body motions from data for a short period of time. What these methods lack are the interpretability and explainability of the network and its results. We propose to use Dynamic Mode Decomposition with delays to represent and anticipate human body motions. Exploring the influence of the number of delays on the reconstruction and prediction of various motion classes, we show that the anticipation errors in our results are comparable to or even better for very short anticipation times (<0.4 s) than a recurrent neural network based method. We perceive our method as a first step towards the interpretability of the results by representing human body motions as linear combinations of previous states and delays. In addition, compared to the neural network based methods large training times are not needed. Actually, our methods do not even regress to any other motions than the one to be anticipated and hence it is of a generic nature. Full article

The anticipated next stage of human organization is often described by futurists as a global technological singularity. This next stage of complex organization is hypothesized to be actualized by scientific-technic knowledge networks. However, the general consequences of this process for the meaning of human existence are unknown. Here, it is argued that cosmic evolutionary philosophy is a useful worldview for grounding an understanding of the potential nature of this futures event. In the cosmic evolutionary philosophy, reality is conceptualized locally as a universal dynamic of emergent evolving relations. This universal dynamic is structured by a singular astrophysical origin and an organizational progress from sub-atomic particles to global civilization mediated by qualitative phase transitions. From this theoretical ground, we attempt to understand the next stage of universal dynamics in terms of the motion of general ideation attempting to actualize higher unity. In this way, we approach technological singularity dialectically as an event caused by ideational transformations and mediated by an emergent intersubjective objectivity. From these speculations, a historically-engaged perspective on the nature of human consciousness is articulated where the truth of reality as an emergent unity depends on the collective action of a multiplicity of human observers. Full article

The excellent compliance and large range of motion of soft actuators controlled by fluid pressure has lead to strong interest in applying devices of this type for biomimetic and human-robot interaction applications. However, in contrast to soft actuators fabricated from stretchable silicone materials, conventional technologies for position sensing are typically rigid or bulky and are not ideal for integration into soft robotic devices. Therefore, in order to facilitate the use of soft pneumatic actuators in applications where position sensing or closed loop control is required, a soft pneumatic bending actuator with an integrated carbon nanotube position sensor has been developed. The integrated carbon nanotube position sensor presented in this work is flexible and well suited to measuring the large displacements frequently encountered in soft robotics. The sensor is produced by a simple soft lithography process during the fabrication of the soft pneumatic actuator, with a greater than 30% resistance change between the relaxed state and the maximum displacement position. It is anticipated that integrated resistive position sensors using a similar design will be useful in a wide range of soft robotic systems. Full article