Time Metrics Research Articles

Outcomes of patients with pre-existing disability managed by mobile stroke units: A sub-analysis of the BEST-MSU study.

Few data exist on acute stroke treatment in patients with pre-existing disability (PD) since they are usually excluded from clinical trials. A recent trial of mobile stroke units (MSUs) demonstrated faster treatment and improved outcomes, and included PD patients. To determine outcomes with tissue plasminogen activator (tPA), and benefit of MSU versus management by emergency medical services (EMS), for PD patients. Primary outcomes were utility-weighted modified Rankin Scale (uw-mRS). Linear and logistic regression models compared outcomes in patients with versus without PD, and PD patients treated by MSU versus standard management by EMS. Time metrics, safety, quality of life, and health-care utilization were compared. Of the 1047 tPA-eligible ischemic stroke patients, 254 were with PD (baseline mRS 2-5) and 793 were without PD (baseline mRS 0-1). Although PD patients had worse 90-day uw-mRS, higher mortality, more health-care utilization, and worse quality of life than non-disabled patients, 53% returned to at least their baseline mRS, those treated faster had better outcome, and there was no increased bleeding risk. Comparing PD patients treated by MSU versus EMS, 90-day uw-mRS was 0.42 versus 0.36 (p = 0.07) and 57% versus 46% returned to at least their baseline mRS. There was no interaction between disability status and MSU versus EMS group assignment (p = 0.67) for 90-day uw-mRS. PD did not prevent the benefit of faster treatment with tPA in the BEST-MSU study. Our data support inclusion of PD patients in the MSU management paradigm.

Learned spatial suppression is not always proactive.

Learning to ignore distractors is critical for navigating the visual world. Research has suggested that a location frequently containing a salient distractor can be suppressed. How does such suppression work? Previous studies provided evidence for proactive suppression, but methodological limitations preclude firm conclusions. We sought to overcome these limitations with a new search-probe paradigm. On search trials, participants searched for a shape oddball target while a salient color singleton distractor frequently appeared in a high-probability location. On randomly interleaved probe trials, participants discriminated the orientation of a tilted bar presented briefly at one of the search locations, allowing us to index the spatial distribution of attention at the moment the search would have begun. Results on search trials replicated previous findings: reduced attentional capture when a salient distractor appeared in the high-probability location. However, critically, probe discrimination was no different at the high-probability and low-probability locations. We increased the incentive to ignore the high-probability location in Experiment 2 and found, strikingly, that probe discrimination accuracy was greater at the high-probability location. These results suggest that the high-probability location was initially selected before being suppressed, consistent with a reactive mechanism. Overall, the accuracy probe procedure demonstrates that learned spatial suppression is not always proactive, even when response time metrics seem consistent with such an inference. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

An Optimized Hardware Implementation of Modular Multiplication of Binary Ring LWE

Providing end-to-end security is vital for most networks. Emerging quantum computers make it necessary to design secure crypto-systems against quantum attacks. Binary Ring Learning With Error (Ring-Bin LWE) is a Lattice-based cryptography that is hard to solve by quantum computers. Also, this algorithm does not have costly operations in terms of area, making Ring-Bin LWE a suitable algorithm for resource-constraint devices. This work presents a lightweight hardware implementation of Ring-Bin LWE. In the proposed design, a new multiplication method and design for Ring-Bin LWE is introduced which results in latency reduction by a factor of two. Using column-based multiplication, our design processes two consecutive coefficients in each cycle. The architecture is designed based on the proposed multiplication and contains one specific register bank with two sub-bank registers. The design is implemented on the FPGA platforms. The implementation results show an impressive improvement in execution time and Area-Time metrics over previous similar works.

A Centralized IOT-Based Process Cycle Time Monitoring System for Line Balancing Study

This study proposes a Centralized IoT-based Process Cycle Time Monitoring System (CTMS) to improve the efficiency and accuracy of line balancing studies in a production line with a conveyor system. The traditional approach of measuring cycle time, typically done by manual observation and recording, can be prone to errors due to human factors such as fatigue or inaccuracies in manual recording. The proposed CTMS utilizes a combination of cutting-edge technologies such as a cloud database, android application, LABVIEW GUI, analogue infrared (IR) sensors, and appropriate controllers to improve the reliability of cycle time measurements. The system continuously monitors the production line and provides real-time data on cycle time, downtime, and other relevant metrics, allowing production engineers to quickly identify bottlenecks and areas for improvement. The results of the study demonstrate the potential of the CTMS to be applied and expanded to actual production lines with conveyor systems, providing a valuable tool for production engineers to develop strategies to reduce cycle time and ensure it is kept in check. The proposed CTMS is expected to have a positive impact on the efficiency and profitability of the production line by reducing downtime and increasing productivity.

Robust Arm Impedocardiography Signal Quality Enhancement Using Recursive Signal Averaging and Multi-Stage Wavelet Denoising Methods for Long-Term Cardiac Contractility Monitoring Armbands.

Impedance cardiography (ICG) is a low-cost, non-invasive technique that enables the clinical assessment of haemodynamic parameters, such as cardiac output and stroke volume (SV). Conventional ICG recordings are taken from the patient's thorax. However, access to ICG vital signs from the upper-arm brachial artery (as an associated surrogate) can enable user-convenient wearable armband sensor devices to provide an attractive option for gathering ICG trend-based indicators of general health, which offers particular advantages in ambulatory long-term monitoring settings. This study considered the upper arm ICG and control Thorax-ICG recordings data from 15 healthy subject cases. A prefiltering stage included a third-order Savitzky-Golay finite impulse response (FIR) filter, which was applied to the raw ICG signals. Then, a multi-stage wavelet-based denoising strategy on a beat-by-beat (BbyB) basis, which was supported by a recursive signal-averaging optimal thresholding adaptation algorithm for Arm-ICG signals, was investigated for robust signal quality enhancement. The performance of the BbyB ICG denoising was evaluated for each case using a 700 ms frame centred on the heartbeat ICG pulse. This frame was extracted from a 600-beat ensemble signal-averaged ICG and was used as the noiseless signal reference vector (gold standard frame). Furthermore, in each subject case, enhanced Arm-ICG and Thorax-ICG above a threshold of correlation of 0.95 with the noiseless vector enabled the analysis of beat inclusion rate (BIR%), yielding an average of 80.9% for Arm-ICG and 100% for Thorax-ICG, and BbyB values of the ICG waveform feature metrics A, B, C and VET accuracy and precision, yielding respective error rates (ER%) of 0.83%, 11.1%, 3.99% and 5.2% for Arm-IG, and 0.41%, 3.82%, 1.66% and 1.25% for Thorax-ICG, respectively. Hence, the functional relationship between ICG metrics within and between the arm and thorax recording modes could be characterised and the linear regression (Arm-ICG vs. Thorax-ICG) trends could be analysed. Overall, it was found in this study that recursive averaging, set with a 36 ICG beats buffer size, was the best Arm-ICG BbyB denoising process, with an average of less than 3.3% in the Arm-ICG time metrics error rate. It was also found that the arm SV versus thorax SV had a linear regression coefficient of determination (R2) of 0.84.

An automatic and personalized recommendation modelling in activity eCoaching with deep learning and ontology

Electronic coaching (eCoach) facilitates goal-focused development for individuals to optimize certain human behavior. However, the automatic generation of personalized recommendations in eCoaching remains a challenging task. This research paper introduces a novel approach that combines deep learning and semantic ontologies to generate hybrid and personalized recommendations by considering “Physical Activity” as a case study. To achieve this, we employ three methods: time-series forecasting, time-series physical activity level classification, and statistical metrics for data processing. Additionally, we utilize a naïve-based probabilistic interval prediction technique with the residual standard deviation used to make point predictions meaningful in the recommendation presentation. The processed results are integrated into activity datasets using an ontology called OntoeCoach, which facilitates semantic representation and reasoning. To generate personalized recommendations in an understandable format, we implement the SPARQL Protocol and RDF Query Language (SPARQL). We evaluate the performance of standard time-series forecasting algorithms [such as 1D Convolutional Neural Network Model (CNN1D), autoregression, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU)] and classifiers [including Multilayer Perceptron (MLP), Rocket, MiniRocket, and MiniRocketVoting] using state-of-the-art metrics. We conduct evaluations on both public datasets (e.g., PMData) and private datasets (e.g., MOX2-5 activity). Our CNN1D model achieves the highest prediction accuracy of 97%, while the MLP model outperforms other classifiers with an accuracy of 74%. Furthermore, we evaluate the performance of our proposed OntoeCoach ontology model by assessing reasoning and query execution time metrics. The results demonstrate that our approach effectively plans and generates recommendations on both datasets. The rule set of OntoeCoach can also be generalized to enhance interpretability.

Sex differences in acute telestroke care: more to the story.

Previous studies have shown sex differences in stroke care. Female patients have both lower thrombolytic treatment rates with OR reported as low as 0.57 and worse outcomes. With updated standards of care and improved access to care through telestroke, there is potential to reduce or alleviate these disparities. Acute stroke consultations seen by TeleSpecialists, LLC physicians in the emergency department in 203 facilities (23 states) from January 1, 2021 to April 30, 2021 were extracted from the Telecare by TeleSpecialists™ database. The encounters were reviewed for demographics, stroke time metrics, thrombolytics candidate, premorbid modified Rankin Score, NIHSS score, stroke risk factors, antithrombotic use, admitting diagnosis of suspected stroke, and reason not treated with thrombolytic. The treatment rates, door to needle (DTN) times, stroke metric times, and variables of treatment were compared for females and males. There were 18,783 (10,073 female and 8,710 male) total patients included. Of the total, 6.9% of females received thrombolytics compared to 7.9% of males (OR 0.86, 95% CI 0.75-0.97, p = 0.006). Median DTN times were shorter for males than females (38 vs. 41 min, p < 0.001). Male patients were more likely to have an admitting diagnosis of suspected stroke, p < 0.001. Analysis by age showed the only decade with significant difference in thrombolytics treatment rate was 50-59 with increased treatment of males, p = 0.047. When multivariant logistic regression analysis was performed with stroke risk factors, NIHSS score, age, and admitting diagnosis of suspected stroke, the adjusted odds ratio for females was 0.9 (95% CI 0.8, 1.01), p = 0.064. While treatment differences between sexes existed in the data and were apparent in univariate analysis, no significant difference was seen in multivariate analysis once stroke risk factors, age, NIHSS score and admitting diagnosis were taken into consideration in the telestroke setting. Differences in rates of thrombolysis between sexes may therefore be reflective of differences in risk factors and symptomatology rather than a healthcare disparity.

An enhanced African Vulture Optimization Algorithm for solving the Unmanned Aerial Vehicles path planning problem

In recent years, research on Unmanned Aerial Vehicles has become one of the most interesting topics for industry and academics. UAVs path planning is one of the most important issues in terms of guaranteeing good performance in real-world applications. Its main objective is to determine and ensure an optimal and collision-free trajectory (path) between two positions from a starting point (source) to a destination point (target), while dealing with some requirements (e.g. safety, environment complexity, obstacle avoidance, energy consumption, etc.). In view of this topic’s complexity, an efficient path planning algorithm is required. In this paper, we propose an improvement of the meta-heuristic African Vulture Optimization Algorithm (AVOA), named Chaotic Cauchy Opposition-based AVOA (CCO-AVOA), for solving the UAVs path planning problem in a 3D environment. The effectiveness of the proposed CCO-AVOA is validated in different environments with various numbers of waypoints and threats taking into account the fitness value, path cost, height cost, obstacles cost, UAV’s angle cost, and execution time metrics. Compared to ten well-known meta-heuristics, simulation results demonstrate the efficiency of the proposed CCO-AVOA approach in most cases by obtaining a short, smooth, least costly, and collision-free path with better stability for UAVs in complex environments.

Utilizing Learning Analytics in Radiology: A Pilot Study of an e-Learning Platform in Medical Student Education

Learning analytics is a rapidly advancing scientific field that enables data-driven insights and personalized learning experiences. However, traditional methods for teaching and assessing radiology skills do not provide the data needed to leverage this technology in radiology education. In this paper, we implemented rapmed.net, an interactive radiology e-learning platform designed to utilize learning analytics tools in radiology education. Second-year medical students' pattern recognition skills were evaluated using time to solve a case, dice score, and consensus score, while their interpretation abilities were assessed through multiple-choice questions (MCQs). Assessments were conducted before and after a pulmonary radiology block to examine the learning progress. Our results show that a comprehensive assessment of students'radiological skills using consensus maps, dice scores, time metrics, and MCQs revealed shortcomings traditional MCQs would not have detected. Learning analytics tools allow for a better understanding of students'radiology skills and pave the way for a data-driven educational approach in radiology. As one of the most important skills for physicians across all disciplines, improving radiology education will contribute to better healthcare outcomes.

Metrics of care and cardiovascular outcomes in patients with ST-elevation myocardial infarction treated with pharmacoinvasive strategy: a decade-long network in a populous city in Brazil

BackgroundPharmacoinvasive strategy is an effective myocardial reperfusion therapy when primary percutaneous coronary intervention (p-PCI) cannot be performed in a timely manner.MethodsAuthors sought to evaluate metrics of care and cardiovascular outcomes in a decade-long registry of a pharmacoinvasive strategy network for the treatment of ST-elevation myocardial infarction (STEMI). Data from a local network including patients undergoing fibrinolysis in county hospitals and systematically transferred to the tertiary center were accessed from March 2010 to September 2020. Numerical variables were described as median and interquartile range. Area under the curve (AUC-ROC) was used to analyze the predictive value of TIMI and GRACE scores for in-hospital mortality.ResultsA total of 2,710 consecutive STEMI patients aged 59 [51–66] years, 815 women (30.1%) and 837 individuals with diabetes (30.9%) were analyzed. The time from symptom onset to first-medical-contact was 120 [60–210] minutes and the door-to-needle time was 70 [43–115] minutes. Rescue-PCI was required in 929 patients (34.3%), in whom the fibrinolytic-catheterization time was 7.2 [4.9–11.8] hours, compared to 15.7 [6.8–22,7] hours in those who had successful lytic reperfusion. All cause in-hospital mortality occurred in 151 (5.6%) patients, reinfarction in 47 (1.7%) and ischemic stroke in 33 (1.2%). Major bleeding occurred in 73 (2.7%) patients, including 19 (0.7%) cases of intracranial bleeding. C-statistic confirmed that both scores had high predictive values for in-hospital mortality, demonstrated by TIMI AUC-ROC of 0.80 [0,77–0.84] and GRACE AUC-ROC of 0.86 [0.83—0.89].ConclusionIn a real world registry of a decade-long network for the treatment of ST-elevation myocardial infarction based on the pharmacoinvasive strategy, low rates of in-hospital mortality and cardiovascular outcomes were observed, despite prolonged time metrics for both fibrinolytic therapy and rescue-PCI. Register Clinicaltrials.gov NCT02090712 date of first registration 18/03/2014.

Association Between Body Fat and Body Mass Index from Incomplete Longitudinal Proportion Data: Findings from the Fels Study

Obesity rates continue to exhibit an upward trajectory, particularly in the US, and is the underlying cause of several comorbidities, including but not limited to high blood pressure, high cholesterol, diabetes, heart disease, stroke, and cancers. To monitor obesity, body mass index (BMI) and proportion body fat (PBF) are two commonly used measurements. Although BMI and PBF changes over time in an individual’s lifespan and their relationship may also change dynamically, existing work has mostly remained cross-sectional, or separately modeling BMI and PBF. A combined longitudinal assessment is expected to be more effective in unravelling their complex interplay. To mitigate this, we consider Bayesian cross-domain latent growth curve models within a structural equation modeling framework, which simultaneously handles issues such as individually varying time metrics, proportion data, and potential missing not at random data for joint assessment of the longitudinal changes of BMI and PBF. Through simulation studies, we observe that our proposed models and estimation method yielded parameter estimates with small bias and mean squared error in general, however, a mis-specified missing data mechanism may cause inaccurate and inefficient parameter estimates. Furthermore, we demonstrate application of our method to a motivating longitudinal obesity study, controlling for both time-invariant (such as, sex), and time-varying (such as diastolic and systolic blood pressure, biceps skinfold, bioelectrical impedance, and waist circumference) covariates in separate models. Under time-invariance, we observe that the initial BMI level and the rate of change in BMI influenced PBF. However, in presence of time-varying covariates, only the initial BMI level influenced the initial PBF. The added-on selection model estimation indicated that observations with higher PBF values were less likely to be missing.

Temporal Trends and Contemporary Regional Variation in Management of Patients Undergoing Carotid Endarterectomy.

The purpose of this study is to investigate regional variation and temporal trends in seven quality metrics amongst CEA patients: discharge on antiplatelet after CEA; discharge on statin after CEA; protamine administration during CEA; patch placement at conventional CEA site; continued statin usage at the time of most recent follow-up; continued antiplatelet usage at the time of most recent follow-up; and smoking cessation at the time of long term follow up. There are 19 de-identified regions within the VQI database in the United States. Patients were placed into one of three temporal eras based on the time of their CEA: 2003-2008; 2009-2015; and 2016-2022. We first investigated temporal trends across the seven quality metrics for all regions combined on a national basis. The percentage of patients in each time era with the presence/absence of each metric was identified. Chi-squared testing was performed to confirm statistical significance of the differences across eras. Next, analysis was performed within each region and within each time metric. We separated out the 2016-2022 patients within each region to serve as the status of each metric application in the most modern era. We then compared the frequency of metric non-adherence in each region utilizing Chi-squared testing. There was statistically significant improvement in achievement of all seven metrics between the initial 2003-2008 era and the modern 2016-2022 era. The most marked change in practice pattern was noted for lack of protamine usage at surgery (decreased from 48.7% to 25.9%), discharge home postoperatively without statin (decreased from 50.6% to 15.3%), and lack of statin usage confirmed at time of most recent long term follow up (decreased from 24% to 8.9%). Significant regional variation exists across all metrics (P < .01 for all). Lack of patch placement at the time of conventional endarterectomy ranges from 1.9% to 17.8% across regions in the modern era. Lack of protamine utilization ranges from 10.8% to 49.7%. Lack of antiplatelet and statin at the time of discharge varies from 5.5% to 8.2% and 4.8% to 14.4% respectively. Adherence to the various measures at the time of most recent follow up are more tightly aligned across regions with ranges of: 5.3% to 7.5% for lack of antiplatelet usage; 6.6% to 11.7% lack of statin utilization; and 13.3 to 15.4% for persistent smoking. Prior studies and societal initiatives on CEA documenting the beneficial effects of patch angioplasty, protamine use at surgery, smoking cessation, antiplatelet utilization and statin compliance have positively impacted adherence to these measures over time. In the modern 2016-2022 era the widest regional variation is noted in patch placement, protamine utilization and discharge medications allowing individual geographic areas to identify areas for potential improvement via internal VQI administrative feedback.

Augmented-reality swim goggles accurately and reliably measure swim performance metrics in recreational swimmers.

Swimmers commonly access performance metrics such as lap splits, distance, and pacing information between work bouts while they rest. Recently, a new category of tracking devices for swimming was introduced with the FORM Smart Swim Goggles (FORM Goggles). The goggles have a built-in see-through display and are capable of tracking and displaying distance, time splits, stroke, and pace metrics in real time using machine learning and augmented reality through a heads-up display. The purpose of this study was to assess the validity and reliability of the FORM Goggles compared with video analysis for stroke type, pool length count, pool length time, stroke rate, and stroke count in recreational swimmers and triathletes. A total of 36 participants performed mixed swimming intervals in a 25-m pool across two identical 900-m swim sessions performed at comparable intensities with 1 week interval. The participants wore FORM Goggles during their swims, which detected the following five swim metrics: stroke type, pool length time, pool length count, stroke count, and stroke rate. Four video cameras were positioned on the pool edges to capture ground truth video footage, which was then manually labeled by three trained individuals. Mean (SD) differences between FORM Goggles and ground truth were calculated for the selected metrics for both sessions. The absolute mean difference and mean absolute percentage error were used to assess the differences of the FORM Goggles relative to ground truth. The test-retest reliability of the goggles was assessed using both relative and absolute reliability metrics. Compared with video analysis, the FORM Goggles identified the correct stroke type at a rate of 99.7% (N = 2,354 pool lengths, p < 0.001), pool length count accuracy of 99.8%, and mean differences (FORM Goggles-ground truth) for pool length time: -0.10 s (1.49); stroke count: -0.63 (1.82); and stroke rate: 0.19 strokes/min (3.23). The test-retest intra-class correlation coefficient (ICC) values between the two test days were 0.793 for pool length time, 0.797 for stroke count, and 0.883 for stroke rate. Overall, for pool length time, the residuals were within ±1.0s for 65.3% of the total pool lengths, for stroke count within ±1 stroke for 62.6% of the total pool lengths, and for stroke rate within ±2 strokes/min for 66.40% of the total pool lengths. The FORM Goggles were found valid and reliable for the tracking of pool length time, pool length count, stroke count, stroke rate, and stroke type during freestyle, backstroke, and breaststroke swimming in recreational swimmers and triathletes when compared with video analysis. This opens perspectives for receiving real-time information on performance metrics during swimming.

Impact of imaging nurse navigation on breast interventions: Direct and closed-loop patient communication and documentation

ObjectiveA breast imaging nurse navigator (NN) was established with the goals to enhance the patient experience after biopsy, improve care timeliness, accuracy, and coordination, facilitate direct communication to patients, and increase care retention within our system. Our aim was to determine the impact of NN on patient care time metrics, communication, documentation, compliance, and care retention at our institution after breast biopsy. MethodsRetrospective review of a six-month period before (5/1/17–10/31/17) and after (5/1/19–10/31/19) establishment of a nurse navigator in our breast imaging department was performed, including 498 patients in the pre-navigation (pre-NN) group and 526 patients in the post-navigation (post-NN) group. Data was gathered from the electronic medical record and collected using REDCap. ResultsBiopsy pathology results were communicated directly to the patient more often post-NN (71%, 374/526) compared to pre-NN (4%, 21/498) (p < 0.0001), without change in overall time of result communication (p = 0.08). Due to factors outside of imaging, most care time metrics were longer post-NN, including days from biopsy to pathology report (p < 0.001), result communication to initiation of care (p < 0.001), and biopsy to surgery (p < 0.001). There was no difference and high compliance (p = 1) and care retention (p = 0.015) in both groups. There was improved documentation of pathology results, recommendations, and communication post-NN (0/526 vs 10/498, p = 0.001). ConclusionImaging nurse navigation added greatest value by communicating breast biopsy results and recommendations directly to patients and ensuring documentation. Compliance and retention were high in both groups. Factors outside of Radiology influenced time metrics, requiring further investigation of multidisciplinary collaboration.

Leopard seal optimization (LSO): A natural inspired meta-heuristic algorithm

The main objective of this paper is to introduce a new NIO algorithm inspired from the hunting strategy of the leopard seals called Leopard Seal Optimization (LSO)to provide a simple swarm intelligence algorithm that have a high flexibility to solve real-time engineering problems in a fast and more accurate manner without falling into local optima problem. LSO is compared against recent NIO algorithms considering feature selection for disease diagnosing as the underlying optimization problem. In experimental results, LSO has been statistically tested against a recent six swarm intelligence algorithms using Wilcoxon test and t-test with significance level equals 0.05 based on the common five benchmark functions. These recent algorithms are called Tuna Swarm Optimization (TSO), Pelican Optimization Algorithm (POA), Cat and Mouse-Based Optimization (CMBO), Aphid–Ant Mutualism (AAM), White Shark Optimizer (WSO), and Red Piranha Optimization (RPO). The statistical analysis proved that LSO outperforms other recent techniques in most cases where the most tested results are less than 0.05. Then, LSO has been tested against the same algorithms in binary version as feature selection algorithms using two metrics called accuracy and execution time. It is noted that LSO is faster and more accurate than other algorithms to determine the optimal set of features at all numbers of search agents. Finally, it is concluded that LSO outperforms other techniques using accuracy, execution time, Wilcoxon test, and t-test metrics. Compared to the recent techniques using the maximum iteration number=400, LSO provided the maximum accuracy value that equals 97.62% at the search agents number=120 and the minimum execution time that equals 1314 at the search agents number= 40.

June 2023 Stroke Highlights.

June 2023 Stroke Highlights.

Joint Offloading and Resource Allocation With Diverse Battery Level Consideration in MEC System

Most of existing works related to mobile edge computing (MEC) focused on the total energy consumption or task completion time. However, for the applications with multiple players as a whole, the diverse battery levels of players should be considered to achieve better quality of experience (QoE), which is reflected in the metrics of task completion time and the lifespan of service. In this paper, we focus on QoE and diversity of devices. By jointly considering the individual battery level of different devices, we formulate an optimization problem to minimize the task completion time of the system. Firstly, we propose two necessary conditions to achieve the optimal solution of the formulated problem. In addition, we transform the formulated problem into another problem, which can be used to judge whether a given value is feasible for the formulated problem based on the proposed optimal conditions and bisection searching (BSS) algorithm. Then, we propose a method named sub-channel (SC) matching based on virtual computation resource (SMVCR) to solve the transformed problem with low computational complexity. Simulation results indicate that the BSS-SMVCR algorithm has a good performance in task completion time compared with some existing algorithms, and also can significantly extend the lifespan of the corresponding service, which is reflected in the supporting number of offloading tasks.

Wearable Activity Monitors to Support Physical Activity Interventions in Neurodegenerative Disease: A Feasibility Study

Aim: To evaluate the feasibility of using activity monitors in a physical activity (PA) intervention in people with Parkinson's (PD) and Huntington's disease (HD). Materials &amp; methods: People with early-stage PD (n = 13) and HD (n = 14) enrolled in a 4-month coaching program, wore a Fitbit, and were guided through a behavioral intervention to facilitate PA uptake. Wear time, wear habits and activity metrics (e.g., steps) were analyzed. Results: Retention rate was 85% and participants had an average 92.3% (±9.2) valid wear days. Daily wear time was 18.4 (±4.5) h. Day &amp; night Fitbit wearers showed improvements in steps (d = 1.02) and MET×min/week (d = 0.69) compared with day-only wearers. Conclusion: Implementing wearables in a coaching intervention was feasible and provided insights into PA behavior.

Heart rate variability changes with respect to time and exercise intensity during heart-rate-controlled steady-state treadmill running

The aim of this work was to investigate the time and exercise intensity dependence of heart rate variability (HRV). Time-dependent, cardiovascular-drift-related increases in heart rate (HR) were inhibited by enforcing a constant heart rate throughout the exercise with a feedback control system. Thirty-two healthy adults performed HR-stabilised treadmill running exercise at two distinct exercise intensity levels. Standard time and frequency domain HRV metrics were computed and served as outcomes. Significant decreases were detected in 8 of the 14 outcomes for the time dependence analysis and in 6 of the 7 outcomes for the exercise intensity dependence analysis (excluding the experimental speed-signal frequency analysis). Furthermore, metrics that have been reported to reach an intensity-dependent near-zero minimum rapidly (usually at moderate intensity) were found to be near constant over time and only barely decreased with intensity. Taken together, these results highlight that HRV generally decreases with time and with exercise intensity. The intensity-related reductions were found to be greater in value and significance compared to the time-related reductions. Additionally, the results indicate that decreases in HRV metrics with time or exercise intensity are only detectable as long as their metric-specific near-zero minimum has not yet been reached.

Virtual Footwear Try-on in Augmented Reality using Deep Learning Models

Abstract Customization is an increasing trend in fashion product industry to reflect individual lifestyles. Previous studies have examined the idea of virtual footwear try-on in augmented reality (AR) using a depth camera. However, the depth camera restricts the deployment of this technology in practice. This research proposes to estimate the 6-DoF pose of a human foot from a color image using deep learning models to solve the problem. We construct a training dataset consisting of synthetic and real foot images that are automatically annotated. Three convolutional neural network models (DOPE, DOPE2, and YOLO6d) are trained with the dataset to predict the foot pose in real-time. The model performances are evaluated using metrics for accuracy, computational efficiency, and training time. A prototyping system implementing the best model demonstrates the feasibility of virtual footwear try-on using a RGB camera. Test results also indicate the necessity of real training data to bridge the reality gap in estimating the human foot pose.