Subjective Metrics Research Articles

Is Robotic Console Time a Surrogate for Resident Operative Autonomy?

Robotic-assisted surgery is an increasing part of general surgery training, but resident autonomy on the robotic platform can be hard to quantify. Robotic console time (RCT), the percentage of time the resident controls the console, may be an appropriate measure of resident operative autonomy. This study aims to characterize the correlation between objective resident RCT and subjectively scored operative autonomy. Using a validated resident performance evaluation instrument, we collected resident operative autonomy ratings from residents and attendings performing robotic cholecystectomy (RC) and robotic inguinal hernia repair (IH) at a university-based general surgery program between 9/2020-6/2021. We then extracted RCT data from the Intuitive surgical system. Descriptive statistics, t-tests and ANOVA were performed. A total of 31 robotic operations (13 RC, 18 IH) performed by 4 attending surgeons and 8 residents (4 junior, 4 senior) were matched and included. 83.9% of cases were scored by both attending and resident. The average RCT per case was 35.6%(95% CI 13.0%,58.3%) for junior residents (PGY 2-3) and 59.7%(CI 51.1%,68.3%) for senior residents (PGY 4-5). The mean autonomy evaluated by residents was 3.29(CI 2.85,3.73) out of a maximum score of 5, while the mean autonomy evaluated by attendings was 4.12(CI 3.68,4.55). RCT significantly correlated with subjective evaluations of resident autonomy (r=0.61, p=0.0003). RCT also moderately correlated with resident training level (r=0.5306, p<0.0001). Neither attending robotic experience nor operation type significantly correlated with RCT or autonomy evaluation scores. Our study suggests that resident console time is a valid surrogate for resident operative autonomy in robotic cholecystectomy and inguinal hernia repair. RCT may be a valuable measure in objective assessment of residents' operative autonomy and training efficiency. Future investigation into how RCT correlates with subjective and objective autonomy metrics such as verbal guidance or distinguishing critical operative steps is needed to validate the study findings further.

0673 Sleep in PTSD: A Subjective Concern

Abstract Introduction Sleep complaints are a hallmark of posttraumatic stress disorder (PTSD). Prior studies examining sleep abnormalities in PTSD are inconsistent, with some reporting objective sleep changes including associated obstructive sleep apnea (OSA), possibly due to moderating variables including advancing age, traumatic brain injury (TBI), and medications. We sought to compare sleep disorders and objective and subjective sleep metrics in service members with and without PTSD while controlling for these potential confounders. Methods Case-controlled observational cohort study performed at an academic military sleep center. Detailed sleep history, sleep-related questionnaires, trauma history, and video-polysomnography was performed. Patients with a history of TBI, abnormal neuroimaging, or psychoactive medication use (antidepressants, antipsychotics, anxiolytics) were excluded. Results Patients (N=169, 124 males) were active duty service members (median age 37.1 years) reporting a sleep complaint. Of these, 43 (25.4%) had PTSD vs 126 (74.6%) without PTSD. In patients with PTSD vs no PTSD, there was no significant difference in the rate of OSA, mean total sleep time, sleep efficiency, mean apnea-hypopnea index, sleep stage percentage, or any other polysomnogram (PSG) variable. Stage N3 sleep was non-significantly increased in patients with PTSD (18% vs 14%, p=0.078). Subjective sleepiness was similar between those with and without PTSD (Epworth Sleepiness Score 12.9 vs 11.9, p=0.35). However, subjective insomnia symptoms (Insomnia Severity Index 20.4 vs 15.2, p&amp;lt; 0.0001) and perceived sleep quality (Pittsburgh Sleep Quality Index 15.4 vs 11.1, p&amp;lt; 0.0001) were worse in those with PTSD vs no PTSD. Conclusion In a relatively young active duty cohort with no history of psychoactive medication use or TBI, patients with PTSD had more subjective sleep complaints than those without PTSD but objective sleep parameters, including PSG variables and OSA rate, did not differ significantly. The higher stage N3 (slow wave) sleep percentage, though not statistically significant, suggests that PTSD patients sleep better in a monitored sleep lab than in their habitual sleeping environment. Support (if any)

A joint sparse representation and low rank prior regularization model for image deblocking

When the compression quality factor (QF) is low, JPEG images are usually accompanied by very severe block artifacts. The conventional image deblocking models based on low-rank regularization focus on eliminating block artifacts, but lack the capacity of recovering texture details on image edges. In this paper, an image deblocking model which combines sparse representation and low-rank regularization based on fractional norm is proposed. To avoid the abrupt increase in computational complexity, multiple orthogonal dictionary learning is proposed to enhance the ability to sparsify diverse image structures and eliminate correlations among the atoms in the overcomplete dictionary. To further improve the image deblocking effect, a quantization constraint and fractional norm are used to more effectively enforce the low-rank property. Finally, an alternating minimization strategy is applied to solve the derived optimization problem. The model proposed in this paper preserves more texture details while effectively removing image block artifacts compared with the mainstream image deblocking model utilizing low-rank regularization alone. Experimental results show that the proposed method outperforms the current state-of-the-art image deblocking models in terms of both subjective metrics and objective perception.

CUCN: continuously updated connection network for low-light image enhancement

Low-light (LL) images make subsequent computer vision tasks difficult due to its low contrast. To solve this problem, the LL image enhancement problem is regarded as narrowing the gap between LL images and normal-light images in the process of iterative learning. A continuously updated connection network (CUCN) that is inspired by the recent development of convolutional neural networks is proposed. The proposed CUCN is composed of a continuously updated 4-units module (CU4UM), a feature fusion module (FFM), and a color enhancement module (CEM). The CU4UM adopts a connection method to combine four U-shaped structures with continuously updated parameters. To retain the global and local details while brightening an image, the FFM effectively combines the features of different layers in different U-shaped structures and assigns appropriate weights to each channel. The CEM takes the residuals of multiple features of different scales and performs iterative weighting to obtain more natural color information. The proposed method is evaluated on different public datasets, and the results show that the proposed CUCN method is superior to other state-of-the-art methods in terms of both subjective and objective metrics.

LL-CSFormer: A Novel Image Denoiser for Intensified CMOS Sensing Images under a Low Light Environment

Intensified complementary metal-oxide semiconductor (ICMOS) sensors can capture images under extremely low-light conditions (≤0.01 lux illumination), but the results exhibit spatially clustered noise that seriously damages the structural information. Existing image-denoising methods mainly focus on simulated noise and real noise from normal CMOS sensors, which can easily mistake the ICMOS noise for the latent image texture. To solve this problem, we propose a low-light cross-scale transformer (LL-CSFormer) that adopts multi-scale and multi-range learning to better distinguish between the noise and signal in ICMOS sensing images. For multi-scale aspects, the proposed LL-CSFormer designs parallel multi-scale streams and ensures information exchange across different scales to maintain high-resolution spatial information and low-resolution contextual information. For multi-range learning, the network contains both convolutions and transformer blocks, which are able to extract noise-wise local features and signal-wise global features. To enable this, we establish a novel ICMOS image dataset of still noisy bursts under different illumination levels. We also designed a two-stream noise-to-noise training strategy for interactive learning and data augmentation. Experiments were conducted on our proposed ICMOS image dataset, and the results demonstrate that our method is able to effectively remove ICMOS image noise compared with other image-denoising methods using objective and subjective metrics.

0759 Multidimensional Sleep Health is Associated with Emotional Well-being in Adolescents

Abstract Introduction Multidimensional sleep health (MSH) is a population-based approach to capture the 24-hour experience of sleep by incorporating measures of nighttime sleep and daytime functioning that promote physical and mental well-being. We have updated the existing adolescent MSH model to incorporate objective and subjective sleep measures and, in the present study, examined its utility in identifying associations between MSH and behavioral and emotional well-being. Methods We studied 377 adolescents (16.4 ± 2.3 yr; 46.4% female; 21.5% racial/ethnic minority) from the Penn State Child Cohort, a randomly-selected population-based sample. Using the RU-SATED framework – regularity, satisfaction, alertness, timing, efficiency, and duration – we derived an MSH composite score based on actigraphy-measured and self-reported sleep data. We examined the associations between MSH and scores on both the adolescent version of the Child Behavior Checklist (CBCL) and on the Pediatric Behavior Scale (PBS). CBCL’s internalizing, externalizing, thought and attention problems scores were self-reported, while similar behavioral and emotional cluster scores on the PBS were parent-reported. Results Higher MSH scores were associated with lower thought (r=-0.21, p&amp;lt; 0.001), externalizing (r=-0.18, p&amp;lt; 0.001), attention (r=-0.16, p&amp;lt; 0.01) or internalizing (r=-0.12, p&amp;lt; 0.05) problems. Higher MSH scores were associated with lower scores on the rule-breaking (r=-0.27, p&amp;lt; 0.001) and aggressive (r=-0.21, p&amp;lt; 0.001) externalizing subscales. For the internalizing subscales, higher MSH scores were associated with lower anxious-depressed (r = -0.17, p&amp;lt; 0.01), withdrawn-depressed (r=-0.14, p&amp;lt; 0.01), and somatic complaints (r=-0.12, p&amp;lt; 0.05) scores. Analyses based on parent reports on the more fine-grained PBS cluster scores replicated CBCL’s associations; for example, higher MSH scores were associated with lower depression (r=-0.19, p&amp;lt; 0.01) and inattention (r=-0.12, p&amp;lt; 0.05) as observed by parents. Conclusion Optimal sleep health, when considered as a multidimensional construct, is associated with emotional well-being and better behavioral outcomes in adolescents. Our MSH model, derived from objective and subjective sleep metrics, is able to capture similar trends from self-report and parent-report measures of emotion and behavior. Support (if any) National Institutes of Health (R01HL136587, UL1TR000127)

Be prepared: How training and emergency type affect evacuation behaviour

AbstractBackgroundVideo‐based training has been widely adopted by private organizations and public authorities to educate occupants on various types of building emergencies. However, the effectiveness of video‐based training for preparing occupants for building emergencies has not been rigorously studied nor has the impact of emergency type been investigated on training effectiveness.ObjectivesThis study examines whether video‐based training is an effective method to prepare occupants for building emergencies and how the effectiveness differs in the context of different building emergencies.MethodsWe simulated fire and active shooter emergencies in a virtual office building and conducted evacuation experiments to examine participants' emergency responses using both objective and subjective metrics. A total of 108 participants were recruited and responded to the fire or active shooter incident with or without video‐based training.Results and ConclusionsThe results revealed that participants with video‐based training more often chose to follow other recommendations when responding to building emergencies instead of simply following others. Results from ANOVA showed that training increased participants' self‐efficacy significantly, especially for those in the active shooter group. Moreover, participants in the active shooter simulation had a higher level of response efficacy than those in the fire emergency simulation. Our results also demonstrated the influence of emergency type on participants' final decisions and considerations of the recommendations.ImplicationsOur results suggested that video‐based training is effective in improving participants' emergency preparedness and changing their behaviour patterns to a certain extent such as reducing following behaviour and encouraging safe evacuations. Additionally, statistically significant interactions between video‐based training and emergency types suggested that training effectiveness should be considered in accordance with the emergency type.

Mentorship is Critical: An Analysis of the 2022 Plastic Surgery Match.

As the second cycle impacted by COVID-19, the 2022 Plastic and Reconstructive Surgery (PRS) Match maintained virtual interviews while offering a modest lift of subinternship restrictions. The residency application process continues to evolve, with changes such as pass/fail United States Medical Licensing Examination Step 1 reporting prompting programs to reconsider metrics of applicant success. It is critical to address the impact of board scores, mentorship, and resource availability on a diverse applicant population in the PRS Match. A survey was electronically administered to students applying to a single institutional PRS residency program. The survey inquired about demographics, application statistics, mentorship experience, and match outcomes. Logistic regressions were modeled to assess for odds of matching into plastic surgery. In total, 151 responses were analyzed, a 49.7% response rate. Most participants were female (52.3%), White (68.9%), and not Hispanic/Latino (84.8%). The largest percentage of respondents had a faculty mentor only from their home institution (55.0%) and a resident mentor from only their home institution (32.3%). Participants with a faculty mentor from both a home and outside institution had 7.4 times the odds of matching into PRS ( P = 0.02) than students with no faculty mentorship. Students with dual-institution resident mentorship had 18.5 times higher the odds of matching compared with students with no resident mentorship ( P < 0.001). Subjective metrics, rather than objective scores, had the most influence on successfully matching into plastic surgery. As the PRS Match continues to become increasingly competitive, it behooves programs to provide equitable access to resources such as mentorship.

Development of acoustic denoising learning network for communication enhancement in construction sites

This paper presents a novel denoising approach based on deep learning and signal processing to improve communication efficiency. Construction activities take place when different trades come to the site for overlapped periods to perform their works, which may easily produce hazardous noise levels. The existence of noise affects workers' health issues, especially hearing and rhythm of the heart, and impacts communication efficiency between workers. The proposed approach employs signal processing technique to transform the noisy audio into image and utilize neural networks to extract noisy features and denoise the image. The denoised image is then converted to obtain the denoised audio. Experiments on reducing the side effect of several common noises in construction sites were conducted, compared with the performance of denoising using conventional wavelet transform. Standard objective measures, such as signal-to-noise ratio (SNR), and subjective measures, such as listening tests are used for evaluations. Our experimental results show that the proposed algorithm achieved significant improvements over the traditional method, as evidenced by the following quantitative results of median value: MSE of 0.002, RMSE of 0.049, SNR of 5.7 dB, PSNR of 25.8 dB, and SSR of 8.Results indicate that the proposed algorithm outperforms conventional denoising methods in terms of both objective and subjective evaluation metrics and have the potential to facilitate communication between site workers when facing different noise sources inevitably.

АНАЛІЗ СУБ’ЄКТИВНИХ МЕТРИК І ЕКСПЕРТНИХ МЕТОДІВ ОЦІНЮВАННЯ ЯКОСТІ ЗОБРАЖЕНЬ

An analysis of subjective metrics and expert methods of image quality assessment is provided

Fuzzy Rule Based Adaptive Block Compressive Sensing for WSN Application

Transmission of high volume of data in a restricted wireless sensor network (WSN) has come up as a challenge due to high-energy consumption and larger bandwidth requirement. To address the issues of high-energy consumption and efficient data transmission adaptive block compressive sensing (ABCS) is one of the optimum solution. ABCS framework is well capable to adapt the sampling rate depending on the block’s features information that offers higher sampling rate for less compressible blocks and lower sampling rate for more compressible blocks In this paper, we have proposed a novel fuzzy rule based adaptive compressive sensing approach by leveraging the saliency and the edge features of the image making the sampling rate selection completely automatic. Adaptivity of the block sampling ratio has been decided based on the fuzzy logic system (FLS) by considering two important features i.e., edge and saliency information. The proposed framework is experimented on standard dataset, Kodak data set, CCTV images and the Set5 data set images. It achieved an average PSNR of 34.26 and 33.2 and an average SSIM of 0.87 and 0.865 for standard images and CCTV images respectively. Again for high resolution Kodak data set and Set 5 dataset images, it achieved an average PSNR of 32.95 and 31.72 and SSIM of 0.832 and 0.8 respectively. The experiments and the result analysis show that proposed method is efficacious than the state of the art methods in both subjective and objective evaluation metrics.

The Challenge of Multiple Thermal Comfort Prediction Models: Is TSV Enough?

Classroom thermal comfort has a direct effect on student health and educational outcomes. However, measuring thermal comfort (TC) is a non-trivial task. It is represented by several subjective metrics e.g., Thermal Sensation Vote, Thermal Comfort Vote, Thermal Preference Vote, etc. Since machine learning (ML) is being increasingly used to predict occupant comfort, multiple TC metrics for the same indoor space may yield contradictory results. This poses the challenge of selecting the most suitable single TC metric or the minimal TC metric combination for a given indoor space. Ideally, it will be a metric that can be used to predict all other TC metrics and occupant behavior with high accuracy. This work addresses this problem by using a primary student thermal comfort dataset gathered from 11 schools and over 500 unique students. A comprehensive evaluation is carried out through hundreds of TC prediction models using several ML algorithms. It evaluates the ability of TC metrics to predict (a) other TC metrics, and (b) the adaptive behavior of primary students. An algorithm is proposed to select the most suitable single TC metric or the minimal TC metric input combination. Results show that ML models can accurately predict all TC metrics and occupant-adaptive behavior using a small subset of TC metrics with an average accuracy as high as 79%. This work also found Thermal Sensation Vote to be the most significant single TC predictor, followed by Thermal Satisfaction Level. Interestingly, satisfaction with clothing was found to be as equally relevant as thermal preference. Furthermore, the impact of seasons and choice of ML algorithms on TC metric and occupant behavior prediction is shown.

MOFA: A novel dataset for Multi-modal Image Fusion Applications

Multi-modal image fusion is widely used in various fields, especially in military, medical, industrial detection and other fields. Image fusion can integrate redundant and complementary information of two or more multi-modal images into one image, so that the fused image contains more useful information. In this paper, we construct a novel dataset for Multi-modal Image Fusion Applications (MOFA), including four modals: visible, near-infrared (NIR), long-wavelength infrared (LWIR) and polarization. The MOFA dataset contains 1062 images of 118 groups, in which 450 are indoor and 612 are outdoor. The dataset is applied to different image fusion applications, including general multi-modal image fusion, fusion based image super-resolution and image restoration. Multiple image fusion methods are compared and analyzed on this dataset with a qualitative assessment of subjective and objective metrics. Based on the experiments, the advantages and disadvantages of different methods are discussed. Moreover, the challenging problems of image fusion are concluded.

Defining and quantifying fatigue in the rugby codes.

The rugby codes (i.e., rugby union, rugby league, rugby sevens [termed 'rugby']) are team-sports that impose multiple complex physical, perceptual, and technical demands on players which leads to substantial player fatigue post-match. In the post-match period, fatigue manifests through multiple domains and negatively influences recovery. There is, however, currently no definition of fatigue contextualised to the unique characteristics of rugby (e.g., locomotor and collision loads). Similarly, the methods and metrics which practitioners consider when quantifying the components of post-match fatigue and subsequent recovery are not known. The aims of this study were to develop a definition of fatigue in rugby, to determine agreement with this common definition of fatigue, and to outline which methods and metrics are considered important and feasible to implement to quantify post-match fatigue. Subject matter experts (SME) undertook a two-round online Delphi questionnaire (round one; n = 42, round two; n = 23). SME responses in round one were analysed to derive a definition of fatigue, which after discussion and agreement by the investigators, obtained 96% agreement in round two. The SME agreed that fatigue in rugby refers to a reduction in performance-related task ability which is underpinned by time-dependent negative changes within and between cognitive, neuromuscular, perceptual, physiological, emotional, and technical/tactical domains. Further, there were 33 items in the neuromuscular performance, cardio-autonomic, or self-report domains achieved consensus for importance and/or feasibility to implement. Highly rated methods and metrics included countermovement jump force/power (neuromuscular performance), heart rate variability (cardio-autonomic measures), and soreness, mood, stress, and sleep quality (self-reported assessments). A monitoring system including highly-rated fatigue monitoring objective and subjective methods and metrics in rugby is presented. Practical recommendations of objective and subjective measures, and broader considerations for testing and analysing the resulting data in relation to monitoring fatigue are provided.

Expressing appreciation is linked to interpersonal closeness and inter-brain coherence, both in person and over Zoom.

Expressing appreciation is essential for establishing interpersonal closeness, but virtual interactions are increasingly common and create social distance. Little is known about the neural and inter-brain correlates of expressing appreciation and the potential effects of virtual videoconferencing on this kind of interaction. Here, we assess inter-brain coherence with functional near-infrared spectroscopy while dyads expressed appreciation to one another. We scanned 36 dyads (72 participants) who interacted in either an in-person or virtual (Zoom®) condition. Participants reported on their subjective experience of interpersonal closeness. As predicted, expressing appreciation increased interpersonal closeness between dyad partners. Relative to 3 other cooperation tasks (i.e. problem-solving task, creative-innovation task, socio-emotional task), we observed increased inter-brain coherence in socio-cognitive areas of the cortex (anterior frontopolar area, inferior frontal gyrus, premotor cortex, middle temporal gyrus, supramarginal gyrus, and visual association cortex) during the appreciation task. Increased inter-brain coherence in socio-cognitive areas during the appreciation task was associated with increased interpersonal closeness. These findings support the perspective that expressing appreciation, both in-person and virtually, increases subjective and neural metrics of interpersonal closeness.

Despite Equivalent Clinical Outcomes, Patients Report Less Satisfaction With Telerehabilitation Versus Standard In-Office Rehabilitation After Arthroscopic Meniscectomy: A Randomized Controlled Trial

To evaluate functional outcomes and satisfaction in patients who underwent telerehabilitation (telerehab) compared with in-person rehabilitation after arthroscopic meniscectomy. A randomized-controlled trial was conducted including patients scheduled to undergo arthroscopic meniscectomy for meniscal injury by 1 of 5 fellowship-trained sports medicine surgeons between September 2020 and October 2021. Patients were randomized to receive telerehab, defined as exercises and stretches provided by trained physical therapists over a synchronous face-to-face video visit or in-person rehabilitation for their postoperative course. International Knee Documentation Committee Subjective Knee Form (IKDC) score and satisfaction metrics were collected at baseline and 3 months postoperatively. Analysis was conducted on 60 patients with 3-month follow-up outcomes. There were no significant differences in IKDC scores between groups at baseline (P= .211) and 3 months' postoperatively (P= .065). Patients were more likely to report being satisfied with their rehabilitation group 73% vs. 100% (P= .044) if there were in the in-person group. Satisfaction differed significantly between the 2 groups at the end of their rehabilitation course, and only 64% of those in the telerehab group would elect to undergo telerehab again for future indications. Furthermore, they believed that future rehabilitation would benefit from a hybrid model. Telerehab showed no difference versus traditional in-person rehabilitation in terms of functional outcomes up to 3 months after arthroscopic meniscectomy. However, patients were less satisfied with telerehab. I, randomized controlled trial.

Analysis of preferred weight configuration for barebow shooting style

The barebow archery shooting style limits the stabilisation system to weights connected to the riser, which is the central part of the bow. No scientific study is available to guide the barebow archer in the choice of the best weight configuration. In the following work, a human-centred methodology was developed to address this problem from both a scientific and user-oriented perspective. The proposed methodology is based on subjective metrics (archers’ sensations using the equipment), objective metrics (measurable quantities) and precision. The study sought a correlation between the subjective and objective metrics to provide insight into the problem of configuring a bow. Eleven weight configurations were considered. The vibration was measured at five positions on the bow and the 3D dynamic motion was measured using an inertial measurement unit. Seven archers completed a questionnaire to evaluate the subjective shooting sensations. The results show that the two best weight configurations are the 250–200 g and the 350–200 g, at the central and lower attachment points, respectively. A strong correlation exists between the vibration level of the bow and the feeling archers associated with the shooting, and between the dynamic motion of the bow and the comfort of the shooting. No correlation was found between the vibration time and the physical sensations felt by the archers. Determining the influence of the weight configuration on shooting accuracy, with statistical significance, would require an impractically large number of arrows to be shot for each weight configuration. A new methodology was developed to correlate the archers’ physical sensations to measurable parameters and this was applied to understand the barebow weight selection problem. This methodology could be further developed to correlate additional subjective and objective metrics. An understanding of these correlations would allow objective metrics, which can be simulated in the design process, to be used to ensure satisfaction of subjective metrics by the final design.

Subjective and Objective Measurement of Sleep Quality and Activity in Glaucoma.

Glaucoma patients exhibit worse indices of sleep function by both objective and subjective metrics compared with controls. The purpose of this study is to characterize the sleep parameters and physical activity levels of glaucoma patients compared with controls. A total of 102 patients with a diagnosis of glaucoma in at least 1 eye and 31 control subjects were enrolled in the study. Participants completed the Pittsburgh Sleep Quality Index (PSQI) during enrollment and then wore wrist actigraphs for 7 consecutive days to characterize circadian rhythm, sleep quality, and physical activity. The primary outcomes of the study were subjective and objective metrics of sleep quality using the PSQI and actigraphy devices, respectively. The secondary outcome was physical activity, measured by the actigraphy device. From the PSQI survey, glaucoma patients had higher (worse) scores compared with controls for sleep latency, sleep duration, and subjective sleep quality, whereas scores for sleep efficiency were lower (better), suggesting more time spent in bed asleep. By actigraphy, time in bed was significantly higher in glaucoma patients as was time awake after sleep onset. Interdaily stability, quantifying the synchronization to the 24-hour light-dark cycle, was lower in glaucoma patients. There were no other significant differences between glaucoma and control patients with regard to rest-activity rhythms or physical activity metrics. In contrast to the survey data, findings from the actigraphy demonstrated that there were no significant associations between the study group and controls regarding sleep efficiency, onset latency, or total sleep time. In this study, patients with glaucoma demonstrated several subjective and objective differences in sleep function when compared with controls, whereas physical activity metrics were similar.

Smart algorithm in wireless networks for video streaming based on adaptive quantization

SummaryNowadays, smart multimedia network services have become crucial in the healthcare system. The network parameters of Quality of Service (QoS) are widely affecting the efficiency and accuracy of multimedia streaming in wireless environments. This paper proposes an adaptation framework model, which makes a relation between the QP (quantization parameter) in H.264 and H.265 codecs and the QoS of 5G wireless technology. Besides, the effect of QP and packet loss have been studied because of their impact on video streaming. Packet loss of 5G wireless network characteristic is emulated to determine the impact of QP on the received video quality using objective and subjective quality metrics such as PSNR (peak signal to noise ratio), SSIM (structure similarity), and DMOS (differential mean opinion score). In this research, a Testbed is implemented to stream the encoded video from the server to the end users. The application model framework has automatically evaluated the QoE (Quality of Experience). Accordingly, the model detects the defect of network packet loss and selects the optimum QP value to enhance the QoE by the end‐users. The application has been tested on low and high video motions with full high definition (HD) resolution (1920 × 1080) which were taken from ( https://www.xiph.org/downloads/). Test results based on the objective and subjective quality measurements indicate that an optimal QP = 35 and QP = 30 have been chosen for low and high motion respectively to satisfy user QoE requirements.

Relationship between Objective and Subjective Fatigue Monitoring Tests in Professional Soccer.

Studying fatigue is challenging because it is influenced by physiological, psychological, and sociological states. Fatigue can be assessed objectively or subjectively, but the literature has difficulty understanding how an analytical test relates to a response via a questionnaire. Thus, the purpose of this study was to evaluate the relationships between objective fatigue variables (Squat Jump (SJ) and Countermovement Jump (CMJ)) measured on day-2 to the game and subjective fatigue (Rating Perceived Exertion (RPE) measured on day-3 to the game and Hooper Index (HI) measured on day-2). The sample comprised 32 professional football players from the First Portuguese League aged 25.86 ± 3.15 years. The Spearman correlations and regression analyses were used to study the relationships between the variables. The results showed statistically significant (p &lt; 0.05) but small correlations (0.113-0.172) between several objective metrics and the subjective metrics evaluated. In addition, we found two weak models with statistical significance (p &lt; 0.05) between the dependent objective variables (contact time, height, and elasticity index) and the HI (R2 = 3.7%) and RPE (R2 = 1.6%). Also, nine statistically significant (p &lt; 0.05) but weak models were observed between the subjective dependent variables (HI and RPE) and contact time (R2 = 1.8-2.7%), flight time (R2 = 1.1-1.9%), height (R2 = 1.2-2.3%), power (R2 = 1.4%), pace (R2 = 1.2-2.1%), and elasticity index (R2 = 1.6%). In conclusion, objective and subjective fatigue-monitoring tests in professional soccer do not measure identical but rather complementary aspects of fatigue, and therefore, both need to be considered to gain a holistic perspective.