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Stroke can impair mobility, with deficits more pronounced while simultaneously performing multiple activities. In this study, common clinical tests were instrumented with wearable motion sensors to study motor–cognitive interference effects in stroke survivors (SS). A total of 21 SS and 20 healthy controls performed the Timed Up and Go (TUG), Sit-to-Stand (STS), balance, and 10-Meter Walk (10MWT) tests under single and dual-task (counting backward) conditions. Calculated measures included total time and gait measures for TUG, STS, and 10MWT. Balance tests for both open and closed eyes conditions were assessed using sway, measured using the linear acceleration of the thorax, pelvis, and thighs. SS exhibited poorer performance with slower TUG (16.15 s vs. 13.34 s, single-task p < 0.001), greater sway in the eyes open balance test (0.1 m/s2 vs. 0.08 m/s2, p = 0.035), and slower 10MWT (12.94 s vs. 10.98 s p = 0.01) compared to the controls. Dual tasking increased the TUG time (~14%, p < 0.001), balance thorax sway (~64%, p < 0.001), and 10MWT time (~17%, p < 0.001) in the SS group. Interaction effects were minimal, suggesting similar dual-task costs. The findings demonstrate exaggerated mobility deficits in SS during dual-task clinical testing. Dual-task assessments may be more effective in revealing impairments. Integrating cognitive challenges into evaluation can optimize the identification of fall risks and personalize interventions targeting identified cognitive–motor limitations post stroke. [ABSTRACT FROM AUTHOR]

Clinical tests like Timed Up and Go (TUG) facilitate the assessment of post-stroke mobility, but they lack detailed measures. In this study, 21 stroke survivors and 20 control participants underwent TUG, sit-to-stand (STS), and the 10 Meter Walk Test (10MWT). Tests incorporated single tasks (STs) and motor-cognitive dual-task (DTs) involving reverse counting from 200 in decrements of 10. Eight wearable motion sensors were placed on feet, shanks, thighs, sacrum, and sternum to record kinematic data. These data were analyzed to investigate the effects of stroke and DT conditions on the extracted features across segmented portions of the tests. The findings showed that stroke survivors (SS) took 23% longer for total TUG (p < 0.001), with 31% longer turn time (p = 0.035). TUG time increased by 20% (p < 0.001) from STs to DTs. In DTs, turning time increased by 31% (p = 0.005). Specifically, SS showed 20% lower trunk angular velocity in sit-to-stand (p = 0.003), 21% longer 10-Meter Walk time (p = 0.010), and 18% slower gait speed (p = 0.012). As expected, turning was especially challenging and worsened with divided attention. The outcomes of our study demonstrate the benefits of instrumented clinical tests and DTs in effectively identifying motor deficits post-stroke across sitting, standing, walking, and turning activities, thereby indicating that quantitative motion analysis can optimize rehabilitation procedures. [ABSTRACT FROM AUTHOR]

Background: Falls are common and dangerous for stroke survivors. Current fall risk assessment methods rely on subjective scales. Objective sensor-based methods could improve prediction accuracy. Objective: Develop machine learning models using inertial sensors to objectively classify fall risk in stroke survivors. Determine optimal sensor configurations and clinical test protocols. Methods: 21 stroke survivors performed balance, Timed Up and Go, 10 Meter Walk, and Sit-to-Stand tests with and without dual-tasking. A total of 8 motion sensors captured lower limb and trunk kinematics, and 92 spatiotemporal gait and clinical features were extracted. Supervised models—Support Vector Machine, Logistic Regression, and Random Forest—were implemented to classify high vs. low fall risk. Sensor setups and test combinations were evaluated. Results: The Random Forest model achieved 91% accuracy using dual-task balance sway and Timed Up and Go walk time features. Single thorax sensor models performed similarly to multi-sensor models. Balance and Timed Up and Go best-predicted fall risk. Conclusion: Machine learning models using minimal inertial sensors during clinical assessments can accurately quantify fall risk in stroke survivors. Single thorax sensor setups are effective. Findings demonstrate a feasible objective fall screening approach to assist rehabilitation. [ABSTRACT FROM AUTHOR]

Purpose: Dynamic hip screw (DHS) plate fabrication has a crucial role in success rate of surgeries for fractures of the proximal femur. Our research focuses on comparing microstructure and mechanical properties of DHS plates fabricated through two distinct processes. We aimed to compare the mechanical cold-worked (CW) DHS plate versus a DHS plate that fabricated through the cold-worked hot-forged (CWH) DHS plate. Methods: The CW and CWH DHS plates had similar topology, however, tested under different loading scenarios. To make the results comparable, the test for the CW DHS plate was simulated and validated utilizing the finite element modeling (FEM) approach. Then, the model (i.e., the CW DHS plate) was analyzed applying the loading scenario of the CWH DHS plate test. Finally, the results of the FEM analysis for CW and the actual test for CWH DHS plates including compression bending stiffness/strength and proof load were compared. Furthermore, the chemical combination of the two plates was analyzed to investigate if they meet the requirements for ASTM F138 standard. Results: Our analysis showed the amount of "Proof Load" and "Compression Bending Strength" of the CWH DHS plate significantly improved by ∼87% in comparison with the CW DHS plate that both fabricated with ASTM F138 material. On comparison between their grain sizes, the CWH DHS plate has a finer grain size (∼55% smaller than CW DHS plate) which leads to larger yield stress. Conclusion: Although the cold-working process according to ASTM F138 leads to enhance the mechanical performance of a DHS plate, adding hot-forging to the process improved the mechanical performance of DHS plate. The outcome of this study could facilitate the development of the optimal fabrication process for surgical implants which in turn will increase the long-term success rate of the relevant surgeries. [ABSTRACT FROM AUTHOR]

The complex mechanical structure of spine is usually simplified in finite element (FE) modes. In this study, different 3D models of L4-L5 spinal segment distinguished by their ligament modelling were developed (1D truss, 2D shell and 3D space truss elements). All models could be considered validated with respect to range of motion and intradiscal pressure, although their ligament stresses/forces were substantially different. The models with 2D shell and 3D space truss ligaments showed the stress distribution and identified the potential failure/injury locations in ligaments. The model with 3D space truss ligaments showed the stress/force direction (representing collagen fiber directions). [ABSTRACT FROM AUTHOR]

Human muscle fatigue is the main result of diminishing muscle capability, leading to reduced performance and increased risk of falls and injury. This study provides a classification model to identify the human fatigue level based on the motion signals collected by a smartphone. 24 participants were recruited and performed the fatiguing exercise (i.e. squatting). Upon completing each set of squatting, they walked for a fixed distance while the smartphone attached to their right shank and the gait data were associated with the Borg's Rating of Perceived Exertion (i.e. data label). Our machine-learning model of two (no- vs. strong-fatigue), three (no-, medium-, and strong-fatigue) and four (no-, low-, medium-, and strong-fatigue) levels of fatigue reached the accuracy of 91, 78, and 64%, respectively. The outcomes of this study may facilitate the accessibility of a fatigue-monitoring tool in the workplace, which improves the workers' performance and reduce the risk of falls and injury. Practitioner Summary: This study aimed to develop a machine-learning model to identify human fatigue level using motion data captured by a smartphone attached to the shank. Our results can facilitate the development of an accessible fatigue-monitoring system that may improve the workers' performance and reduce the risk of falls and injury. Abbreviations: WMSD: work-related musculoskeletal disorders; IMU: inertial measurement unit; RPE: rating of perceived exertion; SVM: support vector machine; IRB: institutional review board; SOM: self-organizing map; LDA: linear discriminant analysis; PCA: principal component analysis; FT: fourier transformation; RBF: radial basis function; CUSUM: cumulative sum; ROM: range of motion; MVC: maximum voluntary contractions [ABSTRACT FROM AUTHOR]

Degenerative disc disease, associated with discrete structural changes in the peripheral annulus and vertebral endplate, is one of the most common pathological triggers of acute and chronic low back pain, significantly depreciating an individual's quality of life and instigating huge socioeconomic costs. Novel emerging therapeutic techniques are hence of great interest to both research and clinical communities alike. Exogenous crosslinking, such as Genipin, and platelet-rich plasma therapies have been recently demonstrated encouraging results for the repair and regeneration of degenerated discs, but there remains a knowledge gap regarding the quantitative degree of effectiveness and particular influence on the mechanical properties of the disc. This study aimed to investigate and quantify the material properties of intact (N = 8), trypsin-denatured (N = 8), Genipin-treated (N = 8), and platelet-rich plasma-treated (N = 8) discs in 32 porcine thoracic motion segments. A poroelastic finite element model was used to describe the mechanical properties during different treatments, while a meta-model analytical approach was used in combination with ex vivo experiments to extract the poroelastic material properties. The results revealed that both Genipin and platelet-rich plasma are able to recover the mechanical properties of denatured discs, thereby affording promising therapeutic modalities. However, platelet-rich plasma-treated discs fared slightly, but not significantly, better than Genipin in terms of recovering the glycosaminoglycans content, an essential building block for healthy discs. In addition to investigating these particular degenerative disc disease therapies, this study provides a systematic methodology for quantifying the detailed poroelastic mechanical properties of intervertebral disc. [ABSTRACT FROM AUTHOR]

Background: A stroke often bequeaths surviving patients with impaired neuromusculoskeletal systems subjecting them to increased risk of injury (e.g., due to falls) even during activities of daily living. The risk of injuries to such individuals can be related to alterations in their movement. Using inertial sensors to record the digital biomarkers during turning could reveal the relevant turning alterations. Objectives: In this study, movement alterations in stroke survivors (SS) were studied and compared to healthy individuals (HI) in the entire turning task due to its requirement of synergistic application of multiple bodily systems. Methods: The motion of 28 participants (14 SS, 14 HI) during turning was captured using a set of four Inertial Measurement Units, placed on their sternum, sacrum, and both shanks. The motion signals were segmented using the temporal and spatial segmentation of the data from the leading and trailing shanks. Several kinematic parameters, including the range of motion and angular velocity of the four body segments, turning time, the number of cycles involved in the turning task, and portion of the stance phase while turning, were extracted for each participant. Results: The results of temporal processing of the data and comparison between the SS and HI showed that SS had more cycles involved in turning, turn duration, stance phase, range of motion in flexion–extension, and lateral bending for sternum and sacrum (p-value < 0.035). However, HI exhibited larger angular velocity in flexion–extension for all four segments. The results of the spatial processing, in agreement with the prior method, showed no difference between the range of motion in flexion–extension of both shanks (p-value > 0.08). However, it revealed that the angular velocity of the shanks of leading and trailing legs in the direction of turn was more extensive in the HI (p-value < 0.01). Conclusions: The changes in upper/lower body segments of SS could be adequately identified and quantified by IMU sensors. The identified kinematic changes in SS, such as the lower flexion–extension angular velocity of the four body segments and larger lateral bending range of motion in sternum and sacrum compared to HI in turning, could be due to the lack of proper core stability and effect of turning on vestibular system of the participants. This research could facilitate the development of a targeted and efficient rehabilitation program focusing on the affected aspects of turning movement for the stroke community. [ABSTRACT FROM AUTHOR]

Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today's clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at self-selected pace. Machine learning algorithms (support vector machine (SVM) and multi-layer perceptron (MLP)) were implemented for model development, and SBST results were used as ground truth. The results demonstrated that kinematic data could successfully be used to categorize patients into two main groups: high vs. low-medium risk. Accuracy levels of ~75% and 60% were achieved for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, time-scaled IMU signals yielded the highest accuracy levels (i.e., ~75%). Our findings support the improvement and use of wearable systems in developing diagnostic and prognostic tools for various healthcare applications. This can facilitate development of an improved, cost-effective quantitative NSLBP assessment tool in clinical and home settings towards effective personalized rehabilitation. [ABSTRACT FROM AUTHOR]

Near real time (NRT) remote sensing derived land surface temperature (Ts) data has an utmost importance in various applications of natural hazards and disasters. Space-based instrument MODIS (moderate resolution imaging spectroradiometer) acquired NRT data products of Ts are made available for the users by LANCE (Land, Atmosphere Near real-time Capability) for Earth Observing System (EOS) of NASA (National Aeronautics and Space Administration) free of cost. Such Ts products are swath data with 5 min temporal increments of satellite acquisition, and the average latency is 60-125 min to be available in public domain. The swath data of Ts requires a specialized tool, i.e., HEG (HDF-EOS to GeoTIFF conversion tool) to process and make the data useful for further analysis. However, the file naming convention of the available swath data files in LANCE is not appropriate to download for an area of interest (AOI) to be processed by HEG. In this study, we developed a method/algorithm to overcome such issues in identifying the appropriate swath data files for an AOI that would be able to further processes supported by the HEG. In this case, we used Terra MODIS acquired NRT swath data of Ts, and further applied it to an existing framework of forecasting forest fires (as a case study) for the performance evaluation of our processed Ts. We were successful in selecting appropriate swath data files of Ts for our study area that was further processed by HEG, and finally were able to generate fire danger map in the existing forecasting model. Our proposed method/algorithm could be applied on any swath data product available in LANCE for any location in the world. [ABSTRACT FROM AUTHOR]

Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based medium-term (i.e., four-day) forest fire danger forecasting system (FFDFS) based on an existing framework, and applied the system over the forested regions in the northern Alberta, Canada. Hence, we first employed moderate resolution imaging spectroradiometer (MODIS)-derived daily land surface temperature (Ts) and surface reflectance products along with the annual land cover to generate three four-day composite for Ts, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) at 500 m spatial resolution for the next four days over the forest-dominant regions. Upon generating these four-day composites, we calculated the variable-specific mean values to determine variable-specific fire danger maps with two danger classes (i.e., high and low). Then, by assuming the cloud-contaminated pixels as the low fire danger areas, we combined these three danger maps to generate a four-day fire danger map with four danger classes (i.e., low, moderate, high, and very high) over our study area of interest, which was further enhanced by incorporation of a human-caused static fire danger map. Finally, the four-day scale fire danger maps were evaluated using observed/ground-based forest fire occurrences during the 2015–2017 fire seasons. The results revealed that our proposed system was able to detect about 75% of the fire events in the top two danger classes (i.e., high and very high). The system was also able to predict the 2016 Horse River wildfire, the worst fire event in Albertian and Canadian history, with about 67% agreement. The higher accuracy outputs from our proposed model indicated that it could be implemented in the operational management, which would be very useful for lessening the adverse impact of such fire events. [ABSTRACT FROM AUTHOR]

In this study, our aim was to model forest fire occurrences caused by lightning using the variable of vegetation water content over six fire-dominant forested natural subregions in Northern Alberta, Canada. We used eight-day composites of surface reflectance data at 500-m spatial resolution, along with historical lightning-caused fire occurrences during the 2005–2016 period, derived from a Moderate Resolution Imaging Spectroradiometer. First, we calculated the normalized difference water index (NDWI) as an indicator of vegetation/fuel water content over the six natural subregions of interest. Then, we generated the subregion-specific annual dynamic median NDWI during the 2005–2012 period, which was assembled into a distinct pattern every year. We plotted the historical lightning-caused fires onto the generated patterns, and used the concept of cumulative frequency to model lightning-caused fire occurrences. Then, we applied this concept to model the cumulative frequencies of lightning-caused fires using the median NDWI values in each natural subregion. By finding the best subregion-specific function (i.e., R2 values over 0.98 for each subregion), we evaluated their performance using an independent subregion-specific lightning-caused fire dataset acquired during the 2013–2016 period. Our analyses revealed strong relationships (i.e., R2 values in the range of 0.92 to 0.98) between the observed and modeled cumulative frequencies of lightning-caused fires at the natural subregion level throughout the validation years. Finally, our results demonstrate the applicability of the proposed method in modeling lightning-caused fire occurrences over forested regions. [ABSTRACT FROM AUTHOR]

Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, our objective was to enhance the performance of an existing solely remote sensing-based forest fire danger forecasting system (FFDFS), and its implementation over the northern region of the Canadian province of Alberta. The modified FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived daily surface temperature (Ts) and precipitable water (PW), and 8-day composite of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), where we assumed that cloud-contaminant pixels would reduce the risk of fire occurrences. In addition, we generated ignition cause-specific static fire danger (SFD) maps derived using the historical human- and lightning-caused fires during the period 1961–2014. Upon incorporating different combinations of the generated SFD maps with the modified FFDFS, we evaluated their performances against actual fire spots during the 2009–2011 fire seasons. Our findings revealed that our proposed modifications were quite effective and the modified FFDFS captured almost the same amount of fires as the original FFDFS, i.e., about 77% of the detected fires on an average in the top three fire danger classes of extremely high, very high, and high categories, where about 50% of the study area fell under low and moderate danger classes. Additionally, we observed that the combination of modified FFDFS and human-caused SFD map (road buffer) demonstrated the most effective results in fire detection, i.e., 82% of detected fires on an average in the top three fire danger classes, where about 46% of the study area fell under the moderate and low danger categories. We believe that our developments would be helpful to manage the forest fire in order to reduce its overall impact. [ABSTRACT FROM AUTHOR]

Introduction: Previous studies have compared various technology-based devices, such as ultrasonography (USG), near-infrared (NIR), and transilluminator (TI), with standard care (SC) to facilitate peripheral intravenous cannulation (PIVC) in pediatric patients. This study aims to investigate the efficacy of these interventions on the first-attempt success rate (FASR) of PIVC in pediatric patients with difficult intravenous access (DIVA) using network meta-analysis (NMA)., Methods: We conducted a comprehensive literature search in databases to identify randomized clinical trials comparing the effects of different devices on the FASR of PIVC from inception until August 2023. Pooled relative risks with 95% credible intervals were estimated using pairwise and network meta-analysis with random effects. To rank the efficacy of devices, we calculated the probabilities of the surface under the cumulative ranking curve (SUCRA)., Results: A total of 18 studies were included in the final analysis. The results of pairwise meta-analysis showed that the use of devices increased the FASR for PIVC by 13% (RR: 1.13, 95% CI: [0.98, 1.30]) compared to SC. The ranking of interventions based on efficacy from highest to lowest was as follows: USG (SUCRA: 1), NIR (SUCRA: 0.6), SC (SUCRA: 0.3), and TI (SUCRA: 0.1), with a very low confidence estimate., Conclusion: Based on our findings, the prioritization of device usage to increase the FASR of PIVC in pediatric patients with DIVA is as follows: USG, NIR, SC, and TI, respectively. However, due to inconsistencies in the network, existence of an overall high risk of bias in the included studies, and very low confidence estimate, further clinical trials are required., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction: Human resources development, especially faculty members who play a substantial role in education, is of great importance and can lead to enhanced competence and participation of employees in university affairs. Mentoring is one of the programs that have attracted the attention of activists in this field today. This review aims to integrate the evidence about the goals, methods, implementation steps, and consequences of the mentoring methods for faculty member development in higher education institutions., Methods: We used a systematic review in this study. Keywords related to the mentoring program were searched in gateways and databases such as PubMed, Scopus, Web of Science, and ERIC from 2000 to 2021. In the initial search, 638 articles were found, and 16 studies were reviewed after excluding those unrelated to the research objective., Results: The results showed that the mentoring program included three stages: "Targeting and Familiarization with the Implementation of the Mentoring Program", "Mentoring Program Implementation", and "Evaluation of the Mentoring Program". The implementation approaches included Traditional One-to-one Mentoring Program, Peer Mentoring Program, and Distance Education Mentoring Program., Conclusion: This study identified the stages and types of mentoring programs and revealed that their employment, especially the distance education mentoring program, led to the advancement of faculty members in various fields. A mixed-method approach to program evaluation can provide more appropriate views of the effects of these programs., Competing Interests: None Declared., (Copyright: © Journal of Advances in Medical Education & Professionalism.)

Stroke survivors often experience reduced movement capabilities due to alterations in their neuromusculoskeletal systems. Modern sensor technologies and motion analyses can facilitate the determination of these changes. Our work aims to assess the potential of using wearable motion sensors to analyze the movement of stroke survivors and identifying the affected functions. We recruited 10 participants (5 stroke survivors, 5 healthy individuals) and conducted a controlled laboratory evaluation for two of the most common daily activities: turning and walking. Among the extracted kinematic parameters, range of trunk and sacrum lateral bending in turning were significantly larger in stroke survivors (p-value<0.02). However, no statistical difference in mean angular velocity and range of motion for trunk/sacrum/shank flexion-extension were obtained in the turning task. Our results also indicated that during walking, while there was no difference in swing time, double support portion of gait among the stroke group was significantly larger (p-value = 0.001). Outcomes of this investigation may help in designing new rehabilitation programs for stroke and other neurological disorders and/or in improving the efficacy of such programs.Clinical Relevance- This study may provide a better insight on the detailed functional differences between stroke survivors and healthy individuals which in turn could be used to develop a more efficient rehabilitation program for stroke community.

A single-degree-of-freedom model is considered for flexible exercise bars based on the lumped-element approach. By considering the side segment of a flexible bar as a cantilever beam with an equivalent mass at the free end, its free-vibration response, as well as the forced response under the excitation of the grip, are expressed parametrically. Experiments are performed on a particular flexible bar (FLEXI&#95;BAR) in order to obtain numerical values for quantifying the model's parameters. The model is also computationally simulated to study the response of the flexible bar to various excitations. The results are imported into a multi-segment musculoskeletal software (AnyBody), where the effect of different initial hand positions on the lumbar disc and back muscle forces is investigated (including Longissimus, Iliocostalis, and Transversus) during up-down exercises. The results show that all intervertebral discs and muscles forces are more sensitive to the horizontal position of the bar as compared to its vertical position.