Search Results (155)

The COVID-19 pandemic has exposed and intensified structural tensions surrounding work−life balance, precarity, and gender inequalities in academia. This paper examines the spatial, temporal, and emotional disruptions experienced by early-career and precarious researchers in Italy during the first national lockdown (March–April 2020) and their engagement in remote academic work. Adopting an exploratory and qualitative approach, the study draws on ten narrative video interviews and thirty participant-generated images to investigate how structural dimensions—such as gender, class, caregiving responsibilities, and the organizational culture of the neoliberal university—shaped these lived experiences. The findings highlight the implosion of boundaries between paid work, care, family life, and personal space and how this disarticulation exacerbated existing inequalities, particularly for women and caregivers. By interpreting both visual and narrative data through a sociological lens on gender, work, and organizations, the paper contributes to current debates on the transformation of academic labor and the reshaping of temporal work regimes through the everyday use of digital technologies in contemporary neoliberal capitalism. It challenges the individualization of discourses on productivity and flexibility and calls for gender-sensitive, structurally informed policies that support equitable and sustainable transitions in work and family life, in line with European policy frameworks. Full article

Recognizing hand actions and poses from first-person RGB videos is crucial for applications like human–computer interaction. However, the recognition accuracy is often affected by factors such as occlusion and blurring. In this study, we propose a unified framework for action recognition and hand pose estimation in first-person RGB videos. The framework consists of two main modules: the Hand Pose Estimation Module and the Action Recognition Module. In the Hand Pose Estimation Module, each video frame is fed into a multi-layer transformer encoder after passing through a feature extractor. The hand pose results and object categories for each frame are obtained through multi-layer perceptron prediction using a dual residual network structure. The above prediction results are concatenated with the feature information corresponding to each frame for subsequent action recognition tasks. In the Action Recognition Module, the feature vectors from each frame are aggregated by a multi-layer transformer encoder to capture the temporal information of the hand between video frames and obtain the motion trajectory. The final output is the category of hand movements in consecutive video frames. We conducted experiments on two publicly available datasets, FPHA and H2O, and the results show that our method achieves significant improvements on both datasets, with action recognition accuracies of 94.82% and 87.92%, respectively. Full article

The full-body illusion (FBI) is a phenomenon where individuals experience body perception not in their physical body but in an external virtual body. Previous studies have shown that the relationship between the self and the virtual body influences the occurrence and intensity of the FBI. However, the influence of interpersonal factors on the FBI has not been explored. This study investigated the effect of interpersonal synchrony on body perception through an evaluation experiment involving the FBI. Specifically, the participant and an experimenter clapped together while their movements were recorded by a video camera placed behind the participant and displayed to them via a head-mounted display (HMD). This setup presented synchronous visuotactile stimuli, aligning the visual feedback with the tactile sensations in the participant’s hands, to induce the FBI. The experimenter’s clapping rhythm was manipulated to either be synchronous or asynchronous with the participant’s rhythm, thus controlling the state of movement synchronization between the participant and the experimenter. The impact on the participant’s body perception was then assessed through subjective reports. The results indicated that when the clapping rhythm was synchronized with the other person, there was a significant reduction in touch referral to the participant’s virtual body. Additionally, there was a trend toward a reduction in ownership. This study demonstrated for the first time that interpersonal synchrony affects body perception. Full article

Background/Objectives: The problematic use of video games (PG), social media (PSMU), and alcohol (PAU) is widespread from adolescence onwards. According to theoretical models, personality traits are relevant for these problematic behavioral patterns; however, only very few longitudinal studies are available. The aim of this longitudinal study was to investigate for the first time whether Big Five personality dimensions (BFPD) are predictors for the development of PG, PSMU, or PAU, or conversely, whether these behavioral patterns are predictive of the BFPD. Methods: Surveys were conducted over three measurement time points (t1 to t3) using standardized instruments on PG, PSMU, PAU, and BFPD. A total of 492 young people (average age: 16.83 years, 44.1% female and 55.9% male) were investigated at t1, 475 persons (mean age: 17.93 years, 44.8% female, 55.2% male) at t2, and 443 cases (average age: 20.11 years, 45.1% female, 54.9% male) at t3. We calculated cross-lagged panel analyses over three measurement points (structural equation modeling). Results: Of the BFPD, lower Conscientiousness and lower Extraversion were predictors of PG, higher Negative Emotionality (Neuroticism) predicted PSMU, and lower Agreeableness was a predictor of PAU. Only PAU was a predictor of a Big Five dimension (lower Agreeableness). Conclusions: The findings were not consistent across the measurement points (t1 to t2 vs. t2 to t3) with one exception in an explorative analysis: problematic gaming was a predictor for both problematic social media use and problematic alcohol use in youth (t1 to t2 and t2 to t3). The influence of lower Conscientiousness was confirmed for PG and initial longitudinal results for PSMU and PAU were observed. These novel findings could be considered when developing or revising preventive measures. Full article

In response to the decreased accuracy in person detection caused by densely populated areas and mutual occlusions in public spaces, a human head-detection approach is employed to assist in detecting individuals. To address key issues in dense scenes—such as poor feature extraction, rough label assignment, and inefficient pooling—we improved the YOLOv7 network in three aspects: adding attention mechanisms, enhancing the receptive field, and applying multi-scale feature fusion. First, a large amount of surveillance video data from crowded public spaces was collected to compile a head-detection dataset. Then, based on YOLOv7, the network was optimized as follows: (1) a CBAM attention module was added to the neck section; (2) a Gaussian receptive field-based label-assignment strategy was implemented at the junction between the original feature-fusion module and the detection head; (3) the SPPFCSPC module was used to replace the multi-space pyramid pooling. By seamlessly uniting CBAM, RFLAGauss, and SPPFCSPC, we establish a novel collaborative optimization framework. Finally, experimental comparisons revealed that the improved model’s accuracy increased from 92.4% to 94.4%; recall improved from 90.5% to 93.9%; and inference speed increased from 87.2 frames per second to 94.2 frames per second. Compared with single-stage object-detection models such as YOLOv7 and YOLOv8, the model demonstrated superior accuracy and inference speed. Its inference speed also significantly outperforms that of Faster R-CNN, Mask R-CNN, DINOv2, and RT-DETRv2, markedly enhancing both small-object (head) detection performance and efficiency. Full article

Background: Stress affects 45% of NHS staff. More research is needed to explore how to develop resilient mental health nurses who face multiple workplace stressors, including interacting with distressed clients. Higher Education Institutions are uniquely placed to introduce coping skills that help reduce anxiety and increase confidence for pre-registration nurses entering placements for the first time. Methods: A convenience sample of first year mental health student nurses (whole cohort), recruited before their first clinical placement, were invited to participate. Following a mixed methods design, we developed a 360-degree virtual reality (VR) video, depicting a distressed service user across three scenes, filmed in a real-life decommissioned in-patient ward. Participants followed the service user through the scenes, as though in real life. We used the video alongside a cognitive reappraisal/solution-focused/VERA worksheet and supportive clinical supervision technique to explore students’ experiences of VR as an educative tool and to help build emotional coping skills. Results: N = 21 mental health student nurses were recruited to the study. Behavioural responses to the distressed patient scenario were varied. Students that had prior experience in health work were more likely to feel detached from the distress of the service user. Although for some students VR provided a meaningful learning experience in developing emotional awareness, other students felt more like a ‘fly on the wall’ than an active participant. Empathetic and compassionate responses were strongest in those who perceived a strong immersive effect. Overall, the supportive supervision appeared to decrease the anxiety of the small sample involved, but confidence was not affected. Conclusion: The use of 360-degree VR technology as an educative, classroom-based tool to moderate anxiety and build confidence in pre-placement mental health nursing students was partially supported by this study. The effectiveness of such technology appeared to be dependent on the degree to which ‘immersion’ and a sense of presence were experienced by students. Our cognitive reappraisal intervention proved useful in reducing anxiety caused by ‘the patient in distress scenario’ but only for students who achieved a deep immersive effect. Students with prior exposure to distressing events (in their personal lives and in clinical settings) might have developed other coping mechanisms (e.g., detachment). These findings support the idea that ‘presence’ is a subjective VR experience and can vary among users. Full article

The perception of moving speed in navigation video images differs from that in real-world environments due to the reduced availability of sensory cues. Previous studies have indicated that speed perception in first-person perspective videos is more linear in spaces filled with objects than in sparse environments. However, the impact of object density on the linearity of speed perception remains unclear. This study investigates the effect of object density on the perception of moving speed in first-person perspective videos. A user study involving 44 participants was conducted, where they viewed a movie navigating through a hallway, and their speed perception was assessed across six levels of object density using the psychophysical method of magnitude estimation. An analysis based on Stevens’ power law revealed a positive correlation between the object density and perceived speed. In particular, the perceived speeds increased with the object density up to a moderate density level. The highest linearity of speed perception was observed at moderate densities. In contrast, overly dense environments exhibited diminished linearity, similar to conditions with sparse or no objects. These findings suggest the existence of a critical density threshold for maintaining linear speed perception in moving images, providing insights for the design of videos, such as navigation information. Full article

Objectives: Having achieved a consolidated in-hospital experience with enhanced recovery after cardiac surgery, we explored the feasibility of expanding our protocol to pre-admission and post-discharge periods. Methods: A multidisciplinary team including cardiac surgeons, anaesthetists/intensivists, physiatrists, physiotherapists, perfusionists, nurses, psychiatrists, and engineers, elaborated a new therapeutic offer, based on current ERAS evidence and using telerehabilitation, to enhance preoperative communication and education and improve pre- and postoperative health and psychological state. Results: An institutional web-based platform for remote rehabilitation will host digital content that covers three main areas, including information and communication, prehabilitation and rehabilitation with the offer of respiratory and muscular exercises and aerobic activities, and psychological and patient experience evaluations. These interventions will be achieved through purposely developed video tutorials that present the hospital environments, the relevant healthcare personnel, and their role during the in-hospital patient’s journey, and illustrate tailored prehabilitation activities. A series of questionnaires will be administered to evaluate and follow the patient’s psychological state and collect patient-reported experience measures. The platform was activated in September 2024 and this service will initially involve 100 patients undergoing minimally invasive mitral valve surgery. A first review of compliance and engagement will be carried out after four months and a complete review of the results after the first year. Conclusions: ERAS is associated with improved surgical outcomes. A person-centred treatment should also address the health and psychological difficulties that patients face before hospitalisation and after discharge. Telemedicine is a valid tool to expand treatment and monitoring outside the hospital. This experience may give new insights into the feasibility and effectiveness of providing home-based remote interventions aimed at a global improvement in results throughout the overall cardiac surgery journey. Full article

Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields false detection or missed detection in dense crowds, and it is still difficult to detect small targets. In this paper, we propose a Mamba-based human pose estimation. First, we design a GMamba structure to be used as a backbone network to extract human keypoints. A gating mechanism is introduced into the linear layer of Mamba, which allows the model to dynamically adjust the weights according to the different input images to locate the human keypoints more precisely. Secondly, GMamba as the backbone network can effectively solve the long-sequence problem. The direct use of convolutional downsampling reduces selectivity for different stages of information flow. We used slice downsampling (SD) to reduce the resolution of the feature map to half the original size, and then fused local features from four different locations. The fusion of multi-channel information helped the model obtain rich pose information. Finally, we introduced an adaptive threshold focus loss (ATFL) to dynamically adjust the weights of different keypoints. We assigned higher weights to error-prone keypoints to strengthen the model’s attention to these points. Thus, we effectively improved the accuracy of keypoint identification in cases of occlusion, complex background, etc., and significantly improved the overall performance of attitude estimation and anti-interference ability. Experimental results showed that the AP and AP50 of the proposed algorithm on the COCO 2017 validation set were 72.2 and 92.6. Compared with the typical algorithm, it was improved by 1.1% on AP50. The proposed method can effectively detect the keypoints of the human body, and provides stronger robustness and accuracy for the estimation of human posture in complex scenes. Full article

Using biometric data for user authentication is a frequently addressed subject within the context of computer security. Despite significant advancements in technology, handwriting analysis continues to be the most common method of identifying individuals. There are two distinct types of handwriting recognition: offline and online. The first type involves the identification and interpretation of handwritten content obtained from an image, such as digitized human handwriting. The latter pertains to the identification of handwriting derived from digital writing performed on a touchpad or touchscreen. This research paper provides a comprehensive overview of the proposed apparatus specifically developed for collecting handwritten data. The acquisition of biometric information is conducted using a touchscreen device equipped with a variety of integrated and external sensors. In addition to acquiring signatures, the sensor-fusion-based configuration accumulates handwritten phrases, words, and individual letters to facilitate online user authentication. The proposed system can collect an extensive array of data. Specifically, it is possible to capture data related to stylus pressure, magnetometer readings, images, videos, and audio signals associated with handwriting executed on a tablet device. The study incorporates instances of gathered records, providing a graphical representation of the variation in handwriting among distinct users. The data obtained were additionally analyzed with regard to inter-person variability, intra-person variability, and classification potential. Initial findings from a limited sample of users demonstrate favorable results, intending to gather data from a more extensive user base. Full article

This study investigates how individual predispositions toward Virtual Reality (VR) affect user experiences in collaborative VR environments, particularly in workplace settings. By adapting the Video Game Pursuit Scale to measure VR predisposition, we aim to establish the reliability and validity of this adapted measure in assessing how personal characteristics influence engagement and interaction in VR. Two studies, the first correlational and the second quasi-experimental, were conducted to examine the impact of environmental features, specifically the differences between static and mobile VR platforms, on participants’ perceptions of time, presence, and task motivation. The findings indicate that individual differences in VR predisposition significantly influence user experiences in virtual environments with important implications for enhancing VR applications in training and team collaboration. This research contributes to the understanding of human–computer interaction in VR and offers valuable insights for organizations aiming to implement VR technologies effectively. The results highlight the importance of considering psychological factors in the design and deployment of VR systems, paving the way for future research in this rapidly evolving field. Full article

Recently, the research community has taken great interest in human activity recognition (HAR) due to its wide range of applications in different fields of life, including medicine, security, and gaming. The use of sensory data for HAR systems is most common because the sensory data are collected from a person’s wearable device sensors, thus overcoming the privacy issues being faced in data collection through video cameras. Numerous systems have been proposed to recognize some common activities of daily living (ADLs) using different machine learning, image processing, and deep learning techniques. However, the existing techniques are computationally expensive, limited to recognizing short-term activities, or require large datasets for training purposes. Since an ADL is made up of a sequence of smaller actions, recognizing them directly from raw sensory data is challenging. In this paper, we present a computationally efficient two-level hierarchical framework for recognizing long-term (composite) activities, which does not require a very large dataset for training purposes. First, the short-term (atomic) activities are recognized from raw sensory data, and the probabilistic atomic score of each atomic activity is calculated relative to the composite activities. In the second step, the optimal features are selected based on atomic scores for each composite activity and passed to the two classification algorithms: random forest (RF) and support vector machine (SVM) due to their well-documented effectiveness for human activity recognition. The proposed method was evaluated on the publicly available CogAge dataset that contains 890 instances of 7 composite and 9700 instances of 61 atomic activities. The data were collected from eight sensors of three wearable devices: a smartphone, a smartwatch, and smart glasses. The proposed method achieved the accuracy of 96.61% and 94.1% by random forest and SVM classifiers, respectively, which shows a remarkable increase in the classification accuracy of existing HAR systems for this dataset. Full article

In neuropsychology and clinical psychology, the efficacy of virtual reality (VR) experiences for knowledge acquisition and the potential for modifying conduct are well documented. Consequently, the scope of VR experiences for educational purposes has expanded in the health field in recent years. In this study, we sought to assess the effectiveness of ViveDe in a psychoeducational caregiver program. ViveDe is a VR application that presents users with possible daily life situations from the perspective of individuals with dementia. These situations can be experienced in immersive mode through 360° video. This research aimed to ascertain the associations between the sense of presence that can be achieved in VR and some users’ psychological characteristics, such as distress and empathetic disposition. The study involved 36 informal caregivers of individuals with Alzheimer’s disease. These participants were assessed using scales of anxiety and depression, perceived stress, empathy, and emotional regulation. They were asked to participate in a six-session psychoeducation program conducted online on dementia topics, in addition to experiencing the ViveDe application. The immersive VR sessions enabled the caregivers to directly experience the symptoms of dementia (e.g., spatial disorientation, agnosia, difficulty in problem-solving, and anomia) in everyday and social settings. The results indicated that although the experience in ViveDe (evaluated using the XRPS scale and five questions about emotional attunement) showed efficacy in producing a sense of first-person participation in the symptoms of dementia, further research is needed to confirm this. The structural equation model provided evidence that the characteristics of individuals who enjoy the VR experience play a determining role in the perceived sense of presence, which in turn affects the efficacy of the VR experience as a psychoeducational tool. Further research will be conducted to ascertain the potential role of these elements in conveying change in the caregivers of people with dementia. This will help us study the long-term effectiveness of a large-scale psychoeducation program in VR. Full article

First-person VR- and MR-based Action Observation research has thus far yielded both positive and negative findings in studies observing such tools’ potential to teach motor skills. Teaching drumming, particularly polyrhythms, is a challenging motor skill to learn and has remained largely unexplored in the field of Action Observation. In this contribution, a multimodal tool designed to teach rudimental and polyrhythmic drumming was developed and tested in a 20-subject study. The tool presented subjects with a first-person MR perspective via a head-mounted display to provide users with visual exposure to both virtual content and their physical surroundings simultaneously. When compared against a control group practicing via video demonstrations, results showed increased rhythmic accuracy across four exercises. Specifically, a difference of 239 ms (z-ratio = 3.520, p < 0.001) was found between the timing errors of subjects who practiced with our multimodal mixed reality development compared to subjects who practiced with video, demonstrating the potential of such affordances. This research contributes to ongoing work in the fields of Action Observation and Mixed Reality, providing evidence that Action Observation techniques can be an effective practice method for drumming. Full article

Persons with Multiple Sclerosis (pwMS) are at a high risk of falling, with abnormal gait pattern. The upper limbs play an important role in postural control and gait stability. The presence of arm swing changes during walking in pwMS, especially in the early period, may be an indicator of balance problems. The current study aimed to assess arm swing during walking in early MS. A total of 18 pwMS were evaluated in two time points. The first time was after their first (stable) diagnosis (pre-evaluation) and the second time was 3 months after the pre-evaluation. In addition, 10 healthy controls were evaluated once. Arm swing analysis during walking, using video recording, was applied to both groups. Additionally, the MS group performed the Two-Minute Walk Test, Timed Up and Go, and Timed 25-Foot Walk Test. The pwMS showed similar joint angles at both the first and second evaluations. Only the elbow ROM value on the least affected side was lower in pwMS than healthy controls at the second evaluation (p = 0.027). The early MS patients showed altered arm swing pattern. As walking speed and mobility scores did not change over time, the decrease in elbow amplitude over a 3-month period indicates that the arm swing may present a pattern resulting from MS-specific disorders rather than being a compensatory mechanism in walking. From the earliest stages of the disease, variations in arm swing movements during walking may be considered as a disease progression-predictor for MS. Full article