Search Results (1,315)

Emotional communication is a multi-modal phenomenon involving posture, gestures, facial expressions, and the human voice. Affective states systematically modulate the acoustic signals produced during speech production through the laryngeal muscles via the central nervous system, transforming the acoustic signal into a means of affective transmission. Additionally, a substantial body of research in sonobiology has shown that audible acoustic waves (AAW) can affect cellular dynamics. This pilot study explores whether the physical–acoustic changes induced by gratitude states in human speech could influence cell proliferation and Ki67 expression in non-auditory cells (661W cell line). We conduct a series of assays, including affective electroencephalogram (EEG) measurements, an affective text quantification algorithm, and a passive vibro-acoustic treatment (PVT), to control the CO₂ incubator environment acoustically, and a proliferation assay with immunolabeling to quantify cell dynamics. Although a larger sample size is needed, the hypothesis that emotions can act as biophysical agents remains a plausible possibility, and feasible physical and biological pathways are discussed. In summary, studying the impact of gratitude AAW on cell biology represents an unexplored research area with the potential to enhance our understanding of the interaction between human cognition and biology through physics principles. Full article

Estimating human joint angles is a crucial task in motion analysis, gesture recognition, and motion intention prediction. This paper presents a novel model-based approach for generating reliable and accurate human joint angle estimation using a dual-branch network. The proposed network leverages combined features derived from encoded sEMG signals and RGB-D image data. To ensure the accuracy and reliability of the estimation algorithm, the proposed network employs a convolutional autoencoder to generate a high-level compression of sEMG features aimed at motion prediction. Considering the variability in the distribution of sEMG signals, the proposed network introduces a vision-based joint regression network to maintain the stability of combined features. Taking into account latency, occlusion, and shading issues with vision data acquisition, the feature fusion network utilizes high-frequency sEMG features as weights for specific features extracted from image data. The proposed method achieves effective human body joint angle estimation for motion analysis and motion intention prediction by mitigating the effects of non-stationary sEMG signals. Full article

This paper introduces a novel capacitive sensor array designed for tactile perception applications. Utilizing an all-in-one inkjet deposition printing process, the sensor array exhibited exceptional flexibility and accuracy. With a resolution of up to 32.7 dpi, the sensor array was capable of capturing the fine details of touch inputs, making it suitable for applications requiring high spatial resolution. The design incorporates two multiplexers to achieve a scanning rate of 100 Hz, ensuring the rapid and responsive data acquisition that is essential for real-time feedback in interactive applications, such as gesture recognition and haptic interfaces. To evaluate the performance of the capacitive sensor array, an experiment that involved handwritten number recognition was conducted. The results demonstrated that the sensor accurately captured fingertip inputs with a high precision. When combined with an Auxiliary Classifier Generative Adversarial Network (ACGAN) algorithm, the sensor system achieved a recognition accuracy of 98% for various handwritten numbers from “0” to “9”. These results show the potential of the capacitive sensor array for advanced human–computer interaction applications. Full article

Current deep learning models for time series often face challenges with generalizability in scenarios characterized by limited samples or inadequately labeled data. By tapping into the robust generative capabilities of diffusion models, which have shown success in computer vision and natural language processing, we see potential for improving the adaptability of deep learning models. However, the specific application of diffusion models in generating samples for time series classification tasks remains underexplored. To bridge this gap, we introduce the MDGPS model, which incorporates multi-task diffusion learning and gradient-free patch search (MDGPS). Our methodology aims to bolster the generalizability of time series classification models confronted with restricted labeled samples. The multi-task diffusion learning module integrates frequency-domain classification with random masked patches diffusion learning, leveraging frequency-domain feature representations and patch observation distributions to improve the discriminative properties of generated samples. Furthermore, a gradient-free patch search module, utilizing the particle swarm optimization algorithm, refines time series for specific samples through a pre-trained multi-task diffusion model. This process aims to reduce classification errors caused by random patch masking. The experimental results on four time series datasets show that the proposed MDGPS model consistently surpasses other methods, achieving the highest classification accuracy and F1-score across all datasets: 95.81%, 87.64%, 82.31%, and 100% in accuracy; and 95.21%, 82.32%, 78.57%, and 100% in F1-Score for Epilepsy, FD-B, Gesture, and EMG, respectively. In addition, evaluations in a reinforcement learning scenario confirm MDGPS’s superior performance. Ablation and visualization experiments further validate the effectiveness of its individual components. Full article

Object detection and action/gesture recognition have become imperative in security and surveillance fields, finding extensive applications in everyday life. Advancement in such technologies will help in furthering cybersecurity and extended reality systems through the accurate identification of users and their interactions, which plays a pivotal role in the security management of an entity and providing an immersive experience. Essentially, it enables the identification of human–object interaction to track actions and behaviors along with user identification. Yet, it is performed by traditional camera-based methods with high difficulties and challenges since occlusion, different camera viewpoints, and background noise lead to significant appearance variation. Deep learning techniques also demand large and labeled datasets and a large amount of computational power. In this paper, a novel approach to the recognition of human–object interactions and the identification of interacting users is proposed, based on three-dimensional hand pose data from an egocentric camera view. A multistage approach that integrates object detection with interaction recognition and user identification using the data from hand joints and vertices is proposed. Our approach uses a statistical attribute-based model for feature extraction and representation. The proposed technique is tested on the HOI4D dataset using the XGBoost classifier, achieving an average F1-score of 81% for human–object interaction and an average F1-score of 80% for user identification, hence proving to be effective. This technique is mostly targeted for extended reality systems, as proper interaction recognition and users identification are the keys to keeping systems secure and personalized. Its relevance extends into cybersecurity, augmented reality, virtual reality, and human–robot interactions, offering a potent solution for security enhancement along with enhancing interactivity in such systems. Full article

Currently, as an increasing number of people have been addicted to using cellular phones, smartphone tenosynovitis has become common from long-term use of fingers for their operations. Hand exercise while playing video games, which is called exergame, can be a good solution to provide enjoyable daily exercise opportunities for its prevention, particularly, for young people. In this paper, we implemented a simple exergame system with a hand gesture recognition program made in Python using the Mediapipe library. We designed three sets of hand gestures to control the key operations to play the games as different exercises useful for tenosynovitis prevention. For evaluations, we prepared five video games running on a web browser and asked 10 students from Okayama and Hiroshima Universities, Japan, to play them and answer 10 questions in the questionnaire. Their playing results and System Usability Scale (SUS) scores confirmed the usability of the proposal, although we improved one gesture set to reduce its complexity. Moreover, by measuring the angles for maximum wrist movements, we found that the wrist flexibility was improved by playing the games, which verifies the effectiveness of the proposal. Full article

(1) Background: The kick of the ball in soccer is considered one of the most important technical gestures in soccer. Despite this, there is little evidence on ball-striking performance factors in base soccer. The main objectives of the present study were to identify the potential factors of long-ball kicking (LBK) performance and to determine the target training cut-off for LBK performance in young soccer players. (2) Methods: A cross-sectional observational study was conducted with 31 soccer players, with ages ranging from 12 to 18 years. Age, anthropometric data, sport experience, range of motion (ROM) and maximal isometric strength (MIS) of the lower limb were noted. Kick-of-the-ball performance was assessed by maximum ball displacement per kick. A k-mean cluster analysis determined two groups according to ball-kicking performance: low group (LPG-LBK) and high group (HPG-LBK). (3) Results: Differences were found between both groups in age, body mass, body mass index, leg length and knee flexion ROM (BF10 ≤ 6.33; δ ≥ 0.86 (moderate or higher)). Among the factors discussed above, age was the strongest predictor of ball-striking performance (odds ratio = 2.867; p = 0.003). The optimal cut-off for age predicting those players most likely to have a higher ball-striking performance was determined to be 13.5 years (p = 0.001; area under the curve = 85.3%). (4) Conclusions: Age over 13.5 increases the chances of a higher optimal ball-striking performance. The flexibility (knee flexion ROM) and strength (knee flexors) must be specifically trained in soccer players beginning at an early age. Full article

The acquisition of deictic verbs is a significant milestone in language development. This complex process requires an understanding of the interplay between the personal pronouns “I/you” and deictic verbs. Although demonstrating the cognitive processes associated with deictic shifting through data is valuable, research issues regarding data accuracy and the spatial arrangement of the self and other remain unresolved. This pilot study aimed to quantitatively measure the body movements of Japanese-speaking children during their utterances of “come/go”. Twelve typically developing children aged 6–7 participated in this study. Multiple scenarios were set up where the researcher presented phrases using “come/go” with deictic gestures, such as moving one’s upper body forward or backward, and the participant replied with “come/go”. When performing a role, the researcher sat face-to-face or side-by-side with the participant, depending on the type of question–response. It is possible that there is a learning process whereby verbal responses using “come/go” align with corresponding body movements in the specific question type. This process is deeply involved in the development of perspective-taking abilities. Future research with relatively large samples and cross-cultural comparisons is warranted to deepen the understanding of this linguistic acquisition process and its implications. Full article

The advancement of deep learning techniques has significantly propelled the development of the continuous sign language recognition (cSLR) task. However, the spatial feature extraction of sign language videos in the RGB space tends to focus on the overall image information while neglecting the perception of traits at different granularities, such as eye gaze and lip shape, which are more detailed, or posture and gestures, which are more macroscopic. Exploring the efficient fusion of visual information of different granularities is crucial for accurate sign language recognition. In addition, applying a vanilla Transformer to sequence modeling in cSLR exhibits weak performance because specific video frames could interfere with the attention mechanism. These limitations constrain the capability to understand potential semantic characteristics. We introduce a feature fusion method for integrating visual features of disparate granularities and refine the metric of attention to enhance the Transformer’s comprehension of video content. Specifically, we extract CNN feature maps with varying receptive fields and employ a self-attention mechanism to fuse feature maps of different granularities, thereby obtaining multi-scale spatial features of the sign language framework. As for video modeling, we first analyze why the vanilla Transformer failed in cSLR and observe that the magnitude of the feature vectors of video frames could interfere with the distribution of attention weights. Therefore, we utilize the Euclidean distance among vectors to measure the attention weights instead of scaled-dot to enhance dynamic temporal modeling capabilities. Finally, we integrate the two components to construct the model MSF-ET (Multi-Scaled feature Fusion–Euclidean Transformer) for cSLR and train the model end-to-end. We perform experiments on large-scale cSLR benchmarks—PHOENIX-2014 and Chinese Sign Language (CSL)—to validate the effectiveness. Full article

Although meditating on death has long been a central philosophical practice and is gaining prominence in modern European public discourse, certain misconceptions still persist. The Austrian filmmaker Michael Haneke does not shy away from confronting real and performed images of death, combining a denouncing cinematic approach with no less polemic aesthetic and ethical theories. Certainly, visually shocking and disturbing films can, in their own way, challenge the boundaries of what is thinkable, at times even touching upon the unthinkable. Images of death and death-related themes are particularly pervasive in Haneke’s films. His films raise significant philosophical and ethical questions about mortality, violence, death, and ageing. This analysis is a tentative attempt to map how Haneke explores representations of death and dying in Benny’s Video (1992) and Funny Games (1997), with particular reference to the rewind gesture depicted in both films. In doing so, it aims to examine the conversation such films prompt between moving images and the audience. Full article

The topic of phonation onset gestures is of great interest to singers, acousticians, and voice physiologists alike. The vocal pedagogue and voice researcher Manuel Garcia, in the mid-19th century, first coined the term “coup de la glotte”. Given that Garcia defined the process as “a precise articulation of the glottis that leads to a precise and clean tone attack”, the term can certainly be linked to the concept of “vocal onset” as we understand it today. However, Garcia did not, by any means, have the technical measures at his disposal to investigate this phenomenon. In order to better understand modern ways of investigating vocal onset—and the limitations that still exist—it seems worthwhile to approach the subject from a historical perspective. High-speed video laryngoscopy (HSV) can be regarded as the gold standard among today’s examination methods. Nonetheless, it still does not allow the three-dimensionality of vocal fold vibrations to be examined as it relates to vocal onset. Clearly, measuring methods in voice physiology have developed fundamentally since Garcia’s time. This offers grounds for hope that the still unanswered questions around the phenomenon of vocal onset will be resolved in the near future. One promising approach could be to develop ultra-fast three-dimensional MRI further. Full article

Hand gesture recognition plays a significant role in human-to-human and human-to-machine interactions. Currently, most hand gesture detection methods rely on fixed hand gesture recognition. However, with the diversity and variability of hand gestures in daily life, this paper proposes a registerable hand gesture recognition approach based on Triple Loss. By learning the differences between different hand gestures, it can cluster them and identify newly added gestures. This paper constructs a registerable gesture dataset (RGDS) for training registerable hand gesture recognition models. Additionally, it proposes a normalization method for transforming hand gesture data and a FingerComb block for combining and extracting hand gesture data to enhance features and accelerate model convergence. It also improves ResNet and introduces FingerNet for registerable single-hand gesture recognition. The proposed model performs well on the RGDS dataset. The system is registerable, allowing users to flexibly register their own hand gestures for personalized gesture recognition. Full article

Studies about the effects of SARS-CoV-2 on pregnant women and children born to positive women are controversial with regard to possible inner ear-related damage but most of them do not detect the involvement of this virus in auditory function. However, only a few studies on long-term effects on language development are currently available because of the recent onset of the pandemic. The aim of this study was to investigate the impact of SARS-CoV-2 infection on perceptual and expressive abilities and the emerging development of communication in young children. To this purpose, the MacArthur–Bates Communicative Development Inventory—Words and Gestures form (CDI-WG), was administered to parents. In total, 115 children whose mother was infected by SARS-CoV-2 during pregnancy were enrolled in the study and evaluated at the Audiology Service of the Modena University Hospital. All children underwent Otoacoustic Emissions (OAE) at birth: 114/115 had a “pass” result bilaterally, while 1 case had a unilateral “refer” result. Overall, 110/115 newborns (95.65%) underwent audiological evaluation between 10–18 months of age. In 5/110 patients (3.6%), the Pure Tone Average (PTA) result was equal to 35 dB; one case had a hearing threshold of around 50 dB due to a bilateral effusive otitis media. A notable finding was the percentage of children with tubal dysfunction in both evaluations, within 2 months of age and around 12 months of age. Most children revealed normal hearing. The CDI-WG was completed by 56/115 families. The rate of children below the fifth percentile was 8.9% for sentences understood, 12.5% for words understood, and 5.4% for words produced. Concerning CDI-Gestures, only 2 children (3.6%) were below the fifth percentile. A structured audiological follow-up in association with the evaluation of communication skills of children appears fundamental, particularly in the years of maximum neuroplasticity. Long-term studies are still necessary to evaluate the possible consequences of the pandemic. Full article

Pantomimes are human actions that simulate ideas, objects, and events, commonly used in conversation, performance art, and gesture-based interfaces for computing and controlling robots. Yet, their underlying neurocognitive mechanisms are not well understood. In this review, we examine pantomimes through two parallel lines of research: (1) the two visual systems (TVS) framework for visually guided action, and (2) the neuropsychological literature on limb apraxia. Historically, the TVS framework has considered pantomime actions as expressions of conscious perceptual processing in the ventral stream, but an emerging view is that they are jointly influenced by ventral and dorsal stream processing. Within the apraxia literature, pantomimes were historically viewed as learned motor schemas, but there is growing recognition that they include creative and improvised actions. Both literatures now recognize that pantomimes are often created spontaneously, sometimes drawing on memory and always requiring online cognitive control. By highlighting this convergence of ideas, we aim to encourage greater collaboration across these two research areas, in an effort to better understand these uniquely human behaviors. Full article

Researchers have attempted to control robotic hands and prostheses through biosignals but could not match the human hand. Surface electromyography records electrical muscle activity using non-invasive electrodes and has been the primary method in most studies. While surface electromyography-based hand motion decoding shows promise, it has not yet met the requirements for reliable use. Combining different sensing modalities has been shown to improve hand gesture classification accuracy. This work introduces a multimodal bracelet that integrates a 24-channel force myography system with six commercial surface electromyography sensors, each containing a six-axis inertial measurement unit. The device’s functionality was tested by acquiring muscular activity with the proposed device from five participants performing five different gestures in a random order. A random forest model was then used to classify the performed gestures from the acquired signal. The results confirmed the device’s functionality, making it suitable to study sensor fusion for intent detection in future studies. The results showed that combining all modalities yielded the highest classification accuracies across all participants, reaching $92.3\pm 2.6\%$ on average, effectively reducing misclassifications by 37% and 22% compared to using surface electromyography and force myography individually as input signals, respectively. This demonstrates the potential benefits of sensor fusion for more robust and accurate hand gesture classification and paves the way for advanced control of robotic and prosthetic hands. Full article