Speed Measurements Research Articles

Language experience influences performance on the NIH Toolbox Cognition Battery: A cluster analysis

Studies investigating the effects of bilingualism on cognitive function have often yielded conflicting results, which may stem in part from the use of arbitrary criteria to categorize participants into groups based on language experience. The present study addresses this limitation by using a machine learning algorithm, known as cluster analysis, to identify naturally occurring subgroups of participants with similar language profiles. In a sample of 169 participants with varying degrees of first- and second-language proficiencies and ages of acquisition, the cluster analysis yielded four bilingual subgroups: late-unbalanced, early-unbalanced, late-balanced, and early-balanced. All participants completed the NIH Toolbox Cognition Battery. Results revealed that early-balanced and early-unbalanced bilinguals scored higher than late-unbalanced bilinguals on the cognitive flexibility and inhibitory control subtests of the NIH Toolbox Cognition Battery, whereas late-unbalanced bilinguals scored higher than early-balanced bilinguals on the verbal working memory subtest of the NIH Toolbox Cognition Battery. Bilingual language experience did not impact performance on measures of processing speed, episodic memory, and English vocabulary. These findings demonstrate the utility of data-driven approaches to capture the variability in language experience that exists in the real world. We conclude that different bilingual experiences can shape a wide range of cognitive abilities, from working memory to inhibitory control.

Characteristics of patients diagnosed with psychogenic non-epileptic seizures (PNES) who request reinstatement of their driving privileges.

Patients who experience seizures, including PNES, are usually advised to discontinue driving, or have their driving privileges revoked until a determined period of seizure-freedom is achieved. In this retrospective study, patients with PNES who requested driving privileges or reported having resumed driving were compared to those who did not on measures of depression, anxiety, PTSD, and cognitive flexibility/motor speed. DiagnosisofPNESwasconfirmedwithvideo-EEG.Demographicand clinical dataand requests for reinstatement of driving privileges (requiring 6 or more months seizure freedom) and reports of decisions to resume driving were noted. Tests of motor speed and hand eye coordination and self-report questionnaires of depression, anxiety and PTSD administered as part of neuropsychological assessment were analyzed. A total of 403 patients with PNES evaluated in 2010-2020 were identified. Of those, 365 patients were eligible for inclusion, and of those, 60 applied for driving privileges or reported that they resumed driving. When the two groups were compared, the group that applied for driving privileges or decided to resume driving was significantly less depressed (p=0.001) when tested than the group that did not. Furthermore, a significant difference was seen in measures of motor performance between those who requested to resume driving and those who did not (DKEFS T1, p=0.006, DKEFS T2, p=0.001, DKEFS T3, p=0.002, DKEFS T4, p=0.001; GPT dominant, p=0.05, GPT non-dominant, p=0.003). Driving a motor vehicle is a useful measure of improvement for PNES because patients with seizures are required to discontinue driving until seizure-freedom is achieved. This study revealed that lower levels of depression and better fine motor functioning were associated with reported seizure-freedom and driving resumption. Depression is commonly associated with diminished performance (slower motor response times and impaired fine motor movements) on tests of motor functioning, both of which may result in less interest in pursuing permission to resume driving. These findings suggest that mood symptoms (and associated performance on measures of motor speed and coordination) may have prognostic significance in patients diagnosed with PNES. This also suggests that timely treatment of depression in newly diagnosed patients with PNES may be indicated.

A flux tower site attribute dataset intended for land surface modeling

Abstract. Land surface models (LSMs) require reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are widely regarded as the benchmark for LSMs. However, currently available flux tower datasets often require multiple aspects of processing to ensure data quality before application to LSMs. More importantly, these datasets frequently lack site-observed attribute data, such as fractional vegetation cover and leaf area index, which limits their utility as benchmarking data. Here, we conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, energy balance closure (EBC), and external disturbances such as irrigation and deforestation, leading to 90 high-quality sites. For these sites, we collected vegetation, soil, and topography data as well as wind speed, temperature, and humidity measurement heights from literature; regional networks; and Biological, Ancillary, Disturbance, and Metadata (BADM) files. We then compiled the final flux tower attribute dataset by filling in missing attributes with global data and classifying plant functional types (PFTs). This dataset is provided in NetCDF (Network Common Data Form) format with necessary descriptions and reference sources. Model simulations revealed substantial disparities in the output between the attribute data observed at the site and those commonly used by LSMs, underscoring the critical role of site-observed attribute data and increasing the emphasis on flux tower attribute data in the LSM community. The dataset addresses the lack of the site attribute to some extent, reduces uncertainty in LSM data source, and aids in diagnosing parameter and process deficiencies. The dataset is available at https://doi.org/10.5281/zenodo.12596218 (Shi et al., 2024).

Assessing walking speed in persons with a lower-limb amputation: Test-retest reliability of the 10-m walk test.

Preferred walking speed (PWS), maximal walking speed (MWS), and walking speed reserve (WSR)-the difference between MWS and PWS-can be easily obtained from the 10-m walk test (10MWT) to assess walking ability and function. However, their test-retest reliability has not been determined in persons with unilateral lower-limb amputation (LLA). To determine the reliability of the PWS, MWS, and WSR obtained from the 10MWT in persons with LLA. Test-retest with a 48- to 120-h interval between test days. Test-retest relative and absolute reliability was assessed by the intraclass correlation coefficient (ICC) and the %change in mean (%CM) and coefficient of variation (CV), respectively. Preferred walking speed showed excellent reliability between day 1 (familiarization day) and day 2 (ICC = 0.97; %CM = -1.8; CV = 7.7) and a significant speed increase from day 2 to day 3 (ICC = 0.96; %CM = 8.5, p < 0.003; CV = 7.4). Maximal walking speed showed excellent reliability between day 1 and day 2 (ICC = 0.94; %CM = 2.9; CV = 8.3) and between day 2 and day 3 (ICC = 0.94; %CM = 1.8; CV = 8.6). Overall, WSR was poorly reliable between day 1 and day 2 (ICC = 0.65; %CM = 39.2; CV = 39.2) and between day 2 and day 3 (ICC = 0.74; %CM = 30.0; CV = 30.0). Preferred walking speed obtained from a single 10MWT and MWS obtained after a 10MWT familiarization day are reliable walking speed measures for persons with LLA with similar characteristics to those of our sample. Nonetheless, it is critical that both PWS and MWS are obtained after a 10MWT familiarization to obtain a reliable WSR.

Simultaneous Luminal and Hemodynamic Evaluation of the Cervical Arteries Using Nonenhanced 3D Quantitative Quiescent-Interval Slice-Selective Magnetic Resonance Angiography.

Luminal and hemodynamic evaluations of the cervical arteries inform the diagnosis and management of patients with cervical arterial disease. To demonstrate a 3D nonenhanced quantitative quiescent interval slice-selective (qQISS) magnetic resonance angiographic (MRA) strategy that provides simultaneous hemodynamic and luminal evaluation of the cervical arteries. Prospective. Six healthy volunteers (3 female, 3 male, age = 35.7 ± 10.3 years) and 14 patients with cerebrovascular disease (12 female, 2 male, age = 56.6 ± 14.0 years). 3 T, ungated 3D tilted-slab qQISS, pulse-gated 2D phase contrast (PC), ungated 3D PC, and 3D time-of-flight (TOF)gradient-echo protocols. Four readers scored 29 arterial segments on 3D qQISS volumes for image quality using a 4-point scale (1: non-diagnostic, 2: fair, 3: good, 4: excellent). Time-averaged arterial flow velocities and volume flow rates obtained with qQISS and PC protocols were compared. Arterial lumen area and radius measures obtained with 3D protocols were compared in a subgroup. Gwet's AC2; intraclass correlation coefficient (ICC); Pearson's correlation; Bland-Altman. P values <0.05 were considered statistically significant. 3D qQISS provided good-to-excellent image quality for depicting the cervical arteries (mean scores of 3.72 ± 0.55, 3.55 ± 0.66, 3.42 ± 0.72, and 3.66 ± 0.73 for readers 1, 2, 3, and 4) with significant inter-reader agreement (AC2 = 0.91, ICC = 0.53) in image scoring, significantly agreed with pulse-gated 2D PC for time-averaged total flow velocity (ICC = 0.83) and volume flow rate (ICC = 0.92), and significantly agreed with 3D PC for total flow velocity (ICC = 0.70), volume flow rate (ICC = 0.91), and component flow velocity (ICC = 0.89). Compared with 3D PC, 3D qQISS better agreed with 3D TOF for arterial lumen area (ICC = 0.97 vs. 0.72) and radius (ICC = 0.94 vs. 0.74). Nonenhanced 3D qQISS provides high-quality sub-1 mm3 spatial resolution imaging of the cervical arteries, excellent agreement of arterial structural measures with respect to 3D TOF, and time-averaged hemodynamic data without the need for additional PC imaging. Magnetic resonance angiography (MRA), a method for depicting blood vessels within the body, can be used to evaluate arterial diseases and disorders of the neck. MRA methods routinely used to evaluate the neck arteries do not measure blood flow speed and volume, while other methods for obtaining this information provide less accurate pictures of arterial structure and are not routinely collected. This article reports a new method for MRA that clearly and efficiently portrays the neck arteries without using injected dyes, and provides measurements of arterial blood flow speed and volume. 2 TECHNICAL EFFICACY: Stage 1.

Who is coming in? Evaluation of physician performance within multi-physician emergency departments.

This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED. A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables. A physician's patients' actual LOSs were compared to the model's predictions to calculate a measurement of that physician's speed. Linear regression models were employed to assess how physician performance changed based on the measured speed of the concurrent ED co-attendings, on outcomes including patient LOS, patients treated per hour, imaging utilization, admission rates, and 72-h ED revisits. Eighty physicians and 212,902 ED visits were included. Overall, patients assigned to the fastest physicians have a 17.8% [13.5%, 22.0%] shorter LOS compared to average-speed attendings. When the fastest physicians work alongside the fastest co-attendings, their LOS benefit is reduced to 14.9%, representing a 2.9% [0.2%, 5.6%] longer LOS than when working without the fastest co-attendings. Similarly, the fastest physicians see 0.21 [0.13, 0.28] more patients per hour compared to average attendings, but this benefit decreases to 0.13 [0.09, 0.17] more patients per hour when the fastest co-attendings are present, reflecting a reduction of 0.08 [0.04, 0.11] patients per hour. The fastest physicians order 0.18 [0.13, 0.23] fewer imaging tests per patient than average-speed attendings; however, this reduction diminishes by 0.05 [0.04, 0.07] imaging tests per patient when the fastest co-attendings are present. Our model found effects of similar magnitudes but in the opposite direction when the slowest co-attendings are present. The speed of co-attendings had no significant association on the attending admission rate or 72-h revisit rate. Additionally, compared to the average attending team speed, slower attending teams, over an 8h shift, experienced increased waiting room volume by 6.4% [4.5%, 8.4%] while there was no difference when staffed by the fastest attending teams (-1.2% [-3.2%,0.7%]). In this exploratory analysis, physicians have slower throughput and order more imaging when faster co-attendings are present, and faster throughput with less imaging ordered when slower co-attendings are present. Administrators might consider these relationships and balancing attending speeds, particularly at the extremes (slowest and fastest), when designing staffing models as a potential strategy to enhance ED operational efficiency. What is already known on this topic: ED throughput is known to be dependent on multiple factors however physician behavior is commonly modeled as single attendings working in the ED. This study examines the association between attending and co-attending speed on physician performance and finds that physicians become faster when a slow co-attending is present and slow down when a fast co-attending is present. How this study might affect research, practice or policy: Physician behavior does not exist in isolation and how an entire ED is staffed may have implications for throughput.

Towards Safer Roads: An Automated Traffic Control System with Enhanced Vehicle Speed Detection and Measurement Algorithm

Towards Safer Roads: An Automated Traffic Control System with Enhanced Vehicle Speed Detection and Measurement Algorithm

Baseline predictors of cross-sectional and longitudinal performance on the symbol digit modalities test in individuals with multiple sclerosis.

Cognitive impairment occurs frequently in persons with multiple sclerosis (PwMS) at some point in the course of the disease. However, not all PwMS develop cognitive difficulties suggesting a role for important moderating factors. We examined baseline predictors of cross-sectional and longitudinal change in cognitive performance in PwMS. 680 PwMS enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital who completed the Symbol Digit Modalities Test (SDMT), a brief measure of speed of information processing, at least twice during a 10-year period were identified. Potential baseline demographic (age, education, and sex), clinical (disability, disease duration, and disease category), and patient-reported outcome (PRO) (fatigue, depression, and quality of life) predictors were examined in cross-sectional analyses using linear regression and in longitudinal analyses using linear mixed effects models. In cross-sectional analyses, age, disease duration, and disability each showed associations with SDMT. Group differences were observed between females and males, subjects with and without college degrees, and subjects with relapsing and progressive MS. All PRO measures showed associations with SDMT, and the strongest association was with fatigue. In the longitudinal model, increased baseline age and increased baseline disability were each associated with a greater decline in SDMT performance. None of the baseline PROs were associated with longitudinal change in SDMT. We observed strong associations between baseline demographic, clinical, and PRO measures and concurrent SDMT, but more limited associations between these measures and longitudinal change in SDMT.

An Intelligent Maximum Power Point Tracking Strategy for a Wind Energy Conversion System using Machine Learning Algorithms

Background: Machine Learning (ML) techniques have successfully replaced traditional control algorithms in recent years due to their ability to carry out complicated tasks with significant efficiency and accuracy. Objective: The main objective of the current work is to investigate and compare the performances of different ML models in modeling Maximum Power Point Tracking (MPPT) control for a wind turbine system. The main advantage of the designed MPPT based on ML is that it does not require any detailed mathematical model or prior knowledge of the system, such as turbine parameters or aerodynamic properties, unlike traditional MPPT techniques. Methods: The ML models included in this study were Support Vector Machines, Regression Trees, and Ensemble Trees. Their design was performed through a training process, and their performances were evaluated based on various metrics. During the training phase, the ML models were selected to understand the basic concept of the control strategy and extract essential hidden connections between the inputs and the output of the system. Results: The effectiveness of the control method was investigated using MATLAB/Simulink. The findings of this study revealed that ML models were effective in modeling the MPPT for the studied wind power system, which provides an interesting and sophisticated alternative to classical control methods for wind systems. Conclusion: The ML models designed allow for optimal operation of the system with a simple structure that is independent of system parameters and wind speed measurement and is adaptable for any kind of system.

A Remote Monitoring System for Wind Speed and Direction Based on Non-Contact Triboelectric Nanogenerator

Wind speed and wind direction sensors are crucial sensor categories in the Internet of Things (IoT), particularly vital in fields such as meteorological monitoring, construction engineering, transportation engineering, and ocean engineering. However, power supply remains a key limiting factor for the widespread application of these sensors in large-scale sensor networks. In this paper, a self-powered, non-contact wind speed and direction sensor based on the triboelectric nanogenerator (SD-TENG) is proposed. The optimized wind speed measurement structure has a start-up wind speed as low as 0.2m/s and exhibits good linearity in the range of 0.2-29m/s. Additionally, the sensor demonstrates high temperature and humidity resistance, with a voltage attenuation of 2.4% at 45°C ambient temperature and 9.8% at 95% relative humidity. For wind direction measurement, the design of non-uniform electrodes enhances the recognition capability of different channels. To meet the demands of remote monitoring, we have designed an advanced signal processing circuit that can directly convert the raw output of a wind speed sensor into wind speed information and upload it to a cloud platform via a host. This system not only records and displays wind speed data in real-time but also features historical data storage and alarm functionalities, enhancing the intelligence and automation levels of wind speed monitoring. Additionally, users can access and analyze wind speed data through both computer and mobile devices, ensuring the system's efficiency and reliability. This work provides crucial technical support for the advancement of smart cities, clean energy, and environmental monitoring, thereby promoting the application and dissemination of IoT technology in the environmental field.

EXPLORING THE IMPACT OF ATTENDANCE IN AN OUTDOOR WALKING PROGRAM ON HEALTH IN OLDER ADULTS WITH MOBILITY LIMITATIONS

Abstract Walking in an outdoor environment offers numerous health benefits for older adults with mobility limitations. Effects may vary, however, depending on the level of attendance. This study aimed to investigate whether a dose-response relationship existed between attendance in an outdoor walk group (OWG) program and improvement in health outcomes among older adults with mobility limitations. We analyzed data from a randomized controlled trial conducted across four cities in Canada and focused specifically on community-dwelling older adults assigned to a 10-week OWG program (n=79, age=74.7±6.6 years, 72% female). Participants were categorized into three groups based on attendance tertiles, with the first, second, and third tertiles corresponding to attending 0–9, 10–15, and 16–20 outdoor walking sessions in total. Outcomes were operationalized as change in scores on measures of walking endurance, comfortable and fast gait speed, balance, lower extremity strength, walking self-confidence, and emotional well-being pre- to post-intervention. Results from multiple linear regression models showed that, compared to those who attended 0–9 OWG sessions, participants who attended 16–20 sessions exhibited a 52.7-meter greater improvement in walking endurance (95% CI:12.3, 93.1); 0.15-meter/second greater improvement in comfortable gait speed (95% CI:0.00, 0.29); and 0.17-meter/second greater improvement in fast gait speed (95% CI:0.02, 0.33), after adjusting for sex and study site. No significant dose-response relationships for balance, lower extremity strength, walking self-confidence, and emotional well-being were observed. The role of attendance highlighted in this study provides critical information relevant to the design and implementation of outdoor walking interventions aimed at improving health among older adults.

에너지 절감을 위한 소형 선박용 전기 추진체인 고효율·고토크용 다상 BLDC 모터 설계에 관한 연구

In this study, a high-efficiency multiphase brushless direct current (BLDC) used in small electric thrusters is developed. It enables high-efficiency ship design because electric-propulsion ships possess a better sense of boarding and offer better adjustment than mechanical propulsion methods used for diesel engines, as well as because of the high flexibility of ship internal arrangement and linear design. Therefore, clean energy should be used from an environmental perspective. Meanwhile, for energy saving and cost reduction, an electric-propulsion chain motor is essential for small electric ships, in addition to a high-efficiency motor with low noise BLDC propellant development and energy saving. Therefore, this study proposes the Ziegles-Nichols method, which determines proportionality, integration, and differential constants in the time domain with a multiphase BLDC motor and controller with low noise, high efficiency, and high torque. Subsequently, this method is verified through the actual measurement of the propulsion motor speed of the electric propellant via FOC control.

Operational Capability of Drone‐Based Meteorological Profiling in an Urban Area

AbstractDuring the Uncrewed Aircraft Systems Demonstration Campaign (UAS‐DC), led by the World Meteorological Organization (WMO), twice‐daily meteorological profiling was conducted for 2 months at the Meteorological Research Institute in Tsukuba City (Japan), which is identified as a densely inhabited district. This campaign was instigated to assess the feasibility of obtaining continuous daily measurements for the long term (over a period of more than 1 month), to distribute the data in a format designated for numerical weather prediction, and to evaluate data quality compared to conventional meteorological data. Three types of uncrewed aircraft systems were utilized, that is, a meteorological medium‐sized hexacopter and medium‐ and small‐sized commercial drones with meteorological sensors attached. The maximum flight height was limited to 900 m above ground level owing to airspace regulations around the observation site. Compared with routine radiosonde data, the bias of air temperature, relative humidity, and wind speed measurements was less than 0.3 K, 1.5%, and 0.6 m , respectively, thereby meeting WMO requirements. Moreover, data transfer to the WMO‐prepared repository was completed within 30 min after measurement acquisition. Based on user experience, several aspects regarding the UAS‐DC campaign were discussed from the perspective of sustainable operation and atmospheric boundary layer research.

Model-based compensation and uncertainty analysis of support bearing losses in a chain drive dynamometer

Abstract Efficiency testing of chain drives has received renewed attention in recent years from sectors including elite cycling, e-mobility and automotive engineering. This is because of a desire to maximise performance, reduce energy consumption and minimise wear rate at a component level. In particular, chain and lubricant manufacturers have developed test rigs that replicate transmission drives in order to measure the efficiency and wear behaviour of the chain and sprocket. This paper explores the impact that compensating parasitic losses from test rigs has on the measurement uncertainty. The analytical process of propagating measurement uncertainty for a dynamometer rig is presented, based on the use of modelled bearing friction losses in the rig and quoted datasheet values for the measurement transducers. Two commonly used bearing models are used to illustrate the process. It is found that the measurement uncertainty is dominated by the transducer uncertainties, particularly speed measurement and repeated experimental results corroborate this trend. However, experimental experience also suggests that the uncertainty derived using GUM Method B is conservative compared to the actual system. The research reported also highlights the importance of validating models of parasitic losses for measurement hardware.

Inherent stochasticity, noise and limits of detection in continuous and time-gated fluorescence systems

We present a model for the noise and inherent stochasticity of fluorescence signals in both continuous wave (CW) and time-gated (TG) conditions. When the fluorophores are subjected to an arbitrary excitation photon flux, we apply the model and compute the evolution of the probability mass function (pmf) for each quantum state comprising a fluorophore’s electronic structure, and hence the dynamics of the resulting emission photon flux. Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. The implications of the model on the design of biomolecular fluorescence detection systems are explored in three relevant numerical examples. For a given system, the quantum-limited signal-to-noise ratio (QSNR) and limits of detection are computed to demonstrate how key design tradeoffs are quantified. We find that as systems scale down to micro- and nano- dimensions, the interplay between the fluorophore’s photophysical qualities and use of CW or TG has ramifications on optimal design strategies when considering optical component selection, measurement speed, and system energy requirements. While CW systems remain a gold standard, TG systems can be leveraged to overcome cost and system complexity hurdles when paired with the appropriate fluorophore.

Frontiers in medical physics: material classification and blood pressure measurement of wearable piezoelectric sensors

Wearable piezoelectric sensors, as an emerging tool for blood pressure measurement, have attracted much attention at the forefront of medical physics and have broad application prospects due to their portability, real-time monitoring and low interference with human activities. However, the development of piezoelectric materials is currently a key factor restricting the development of wearable piezoelectric sensors. In order to continuously improve the accuracy and speed of blood pressure measurements by wearable piezoelectric sensors, new measurement methods need to be designed in addition to the development of high-performance piezoelectric materials. We present the advantages and disadvantages of different types of piezoelectric materials for wearable piezoelectric sensors, illustrate their future development directions, and discuss the current new strategies and the latest applied research of piezoelectric sensors applied to blood pressure measurement. In addition, the challenges and future prospects of wearable piezoelectric sensors for blood pressure measurement are revealed, providing new ideas for future applications of high-performance wearable piezoelectric sensors for health monitoring.

Enhanced gait tracking measures for individuals with stroke using leg-worn inertial sensors

BackgroundClinical gait analysis plays a pivotal role in diagnosing and treating walking impairments. Inertial measurement units (IMUs) offer a low-cost, portable, and practical alternative to traditional gait analysis equipment, making these techniques more accessible beyond specialized clinics. Previous work and algorithms developed for specific clinical populations, like in individuals with Parkinson’s disease, often do not translate effectively to other groups, such as stroke survivors, who exhibit significant variability in their gait patterns. The Salarian gait segmentation algorithm (SGSA) has demonstrated the potential to detect gait events and subsequently estimate clinical measures of gait speed, stride time, and other temporal parameters using two leg-worn IMUs in individuals with Parkinson’s disease. However, the distinct gait impairments in stroke survivors, including hemiparesis, spasticity, and muscle weakness, can interfere with SGSA performance. Thus, the objective of this study was to develop and test an enhanced gait segmentation algorithm (EGSA) to capture temporal gait parameters in individuals with stroke.MethodsForty-one individuals with stroke were recruited from two acute rehabilitation settings and completed brief walking bouts with two leg-worn IMUs. We compared foot-off (FO), foot contact (FC), and temporal gait parameters computed from the SGSA and EGSA against ground truth measurements from an instrumented mat.ResultsThe EGSA demonstrated greater accuracy than the SGSA when detecting gait events within one second, for both FO (96% vs. 90%) and FC (94% vs. 91%). The EGSA also demonstrated lower error than the SGSA when detecting paretic FC, and FO events in slow, asymmetrical, and non-paretic footfalls. Temporal gait parameters from the EGSA had high reliability (ICC > 0.90) for stride time, step time, stance time, and double support time across gait speeds and levels of asymmetry.ConclusionThis approach has the potential to enhance the accuracy and validity of IMU-based gait analysis in individuals with stroke, thereby enhancing clinicians’ ability to monitor and intervene for gait impairments in a rehabilitation setting and beyond.

Reliability and Sensitivity of Using Isometric Strength and Sprint Speed Measures in Adolescent Female Athletes.

Salter, J, Forsdyke, D, Dawson, Z, Rymer, J, Walsh, L, and Mundy, P. Reliability and sensitivity of using isometric strength and sprint speed measures in adolescent female athletes. J Strength Cond Res XX(X): 000-000, 2024-The aim of this study was to establish the reliability and sensitivity of isometric mid-thigh pull (IMTP) and sprint speed (5 m, 40 m, and maximal sprint speed) in adolescent women, before exploring the stability of this across maturation to provide maturity-specific benchmarks. A total of 147 female athletes (age: 13.8 ± 2.8 years; stature: 157.1 ± 13.1 cm; body mass: 51.2 ± 15.3 kg; percentage of predicated adult height: 94.3 ± 6.6) performed IMTP and sprint trials on 2 occasions, separated by 7-days. Within- and between-session reliability was determined using intraclass correlation coefficient (ICC), coefficient of variation, and Bland-Altman limits of agreement, with sensitivity detected by signal-to-noise ratios for small (SWC0.2) and moderate (SWC0.5) worthwhile change. A between-group analysis of variance and Cohen's d-effect sizes determined differences between biological maturity groups (pre-, mid-, and post-peak height velocity [PHV]). All isometric strength and sprint performance markers demonstrated either "moderate" or "acceptable" within-session reliability, except for time to peak force (PF) and 40 m sprint. Despite metrics all having "high" or above ICC (0.55-0.98), only PF offered a "good" sensitivity when using SWC0.2, with most offering better sensitivity with SWC0.5. Noise was higher between sessions, resulting in "poor" signal-noise ratios, likely associated with the bias favoring retest trials. Reliability and sensitivity findings were consistent across maturational stages, demonstrating either "moderate" or "acceptable" reliability. There were clear differences between maturity groups for all measures, particularly between mid-PHV and post-PHV. Practitioners can be assured that IMTP and sprint performance measures are reliable within this population but require thorough familiarization processes before confidence in interpreting meaningful changes.

Efficacy and safety of a non-immersive virtual reality-based neuropsychological intervention for cognitive stimulation and relaxation in patients with critical illness: study protocol of a randomized clinical trial (RGS-ICU)

BackgroundExperiencing a critical illness may be a stressful life event that is also associated with cognitive dysfunction during and after the intensive care unit (ICU) stay. A deep-tech solution based on non-immersive virtual reality, gamification and motion capture called Rehabilitation Gaming System for Intensive Care Units (RGS-ICU) has been developed that includes both cognitive stimulation and relaxation protocols specifically designed for patients with critical illness. This study aims to evaluate whether the cognitive and relaxation protocols of the RGS-ICU platform are 1) effective in improving neuropsychological outcomes during and after ICU stay and 2) safe for patients with critical illness.MethodsThis is a study protocol for a multicenter longitudinal randomized clinical trial. At least 80 patients with critical illness will be included: 40 experimental subjects and 40 control subjects. Patients in the experimental group will receive daily 20-min sessions of cognitive stimulation and relaxation with the RGS-ICU platform adjuvant to standard ICU care in their own rooms during the ICU stay and until discharge from the ICU or up to a maximum of 28 days after randomization, provided they are alert and calm. Patients in the experimental group will be constantly monitored as part of standard ICU care to ensure the safety of the intervention and that no avoidable adverse events occur. Patients in the control group will receive standard ICU care. The primary outcome is objective cognition 12 months after ICU discharge, assessed with a composite index including measures of attention, working memory, learning/memory, executive function and processing speed. The secondary outcome is the safety of the intervention, assessed by considering the number of sessions terminated early due to unsafe events in physiological parameters. Other outcomes are comfort experienced during the ICU stay, and subjective cognition, mental health (anxiety, depression and post-traumatic stress disorder), functionality and health-related quality of life 12 months after ICU discharge.DiscussionThe expected results are 1) better neuropsychological outcomes during and after the ICU stay in patients in the experimental group compared to patients in the control group and 2) that the cognitive and relaxation protocols of the RGS-ICU platform are safe for patients with critical illness.Trial registrationClinicaltrials.gov NCT06267911. Registered on February 20, 2024.

Optimization of a direct-detection UV wind lidar architecture for 3D wind reconstruction at high altitude

Abstract. An architecture for a UV wind lidar dedicated to measuring vertical and lateral wind in front of an aircraft for gust load alleviation is presented. To optimize performance and robustness, it includes a fiber laser architecture and a Quadri Mach–Zehnder (QMZ) interferometer with a robust design to spectrally analyze the backscattered light. Different lidar parameters have been selected to minimize the standard deviation of wind speed measurement projected onto the laser axis, calculated through end-to-end simulations of the instrument. The optimization involves selecting an emission–reception telescope to maximize the number of collected photons backscattered between 100 and 300 m, a background filter to reduce noise from the scene, and photomultiplier tubes (PMTs) to minimize detection noise. Simulations were performed to evaluate lidar performance as a function of laser parameters. This study led to the selection of three laser architectures, a commercial solid-state laser, a design of a fiber laser, and a hybrid fiber laser, resulting in standard deviations of projected wind speed of 0.17, 0.16, and 0.09 m s−1, respectively, at 10 km altitude. To reconstruct the vertical and lateral wind on the flight path, the lidar is directed along four different directions to measure four different projections of the wind. We analytically calculate (and validate through simulations) the directed angle with respect to the flight direction that minimizes the root mean square error (RMSE) between the reconstructed vertical and lateral wind components and the actual ones, assuming turbulence that follows the von Kármán turbulence model. We found that the optimum angle for an estimation at 100 m is about 50°, resulting in an improvement of about 50 % compared to an angle of 15–30° typically used in current studies.