scholarly journals Real-time, whole-brain, temporally resolved pressure responses in translational head impact

2016 ◽  
Vol 6 (1) ◽  
pp. 20150091 ◽  
Author(s):  
Wei Zhao ◽  
Songbai Ji
Keyword(s):  
Brain Injury ◽  
Real Time ◽  
Real World ◽  
Risk Function ◽  
Computational Cost ◽  
Linear Acceleration ◽  
Tissue Level ◽  
Head Impact ◽  
Whole Brain ◽  
Brain Responses

Theoretical debate still exists on the role of linear acceleration ( a lin ) on the risk of brain injury. Recent injury metrics only consider head rotational acceleration ( a rot ) but not a lin , despite that real-world on-field head impacts suggesting a lin significantly improves a concussion risk function. These controversial findings suggest a practical challenge in integrating theory and real-world experiment. Focusing on tissue-level mechanical responses estimated from finite-element (FE) models of the human head, rather than impact kinematics alone, may help address this debate. However, the substantial computational cost incurred (runtime and hardware) poses a significant barrier for their practical use. In this study, we established a real-time technique to estimate whole-brain a lin -induced pressures. Three hydrostatic atlas pressures corresponding to translational impacts (referred to as ‘brain print’) along the three major axes were pre-computed. For an arbitrary a lin profile at any instance in time, the atlas pressures were linearly scaled and then superimposed to estimate whole-brain responses. Using 12 publically available, independently measured or reconstructed real-world a lin profiles representative of a range of impact/injury scenarios, the technique was successfully validated (except for one case with an extremely short impulse of approx. 1 ms). The computational cost to estimate whole-brain pressure responses for an entire a lin profile was less than 0.1 s on a laptop versus typically hours on a high-end multicore computer. These findings suggest the potential of the simple, yet effective technique to enable future studies to focus on tissue-level brain responses, rather than solely relying on global head impact kinematics that have plagued early and contemporary brain injury research to date.

Practical Challenges in Collecting Head Impact Biomechanics Data in Real-World Scenarios via a Helmet-Based Accelerometer System

2013 ◽  
Author(s):  
Mari A. Allison ◽  
Yun Seok Kang ◽  
Matthew R. Maltese ◽  
John H. Bolte ◽  
Kristy B. Arbogast
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Real World ◽  
Head Impact ◽  
Permanent Damage ◽  
Head Impact Biomechanics ◽  
Impact Biomechanics ◽  
The Brain

Recent studies have shown that mild traumatic brain injury (mTBI) can have long-term neurological consequences and may cause permanent damage to the brain [1,2]. Given estimates that millions of these injuries occur each year [3], this knowledge has created a demand for countermeasures to prevent mTBI. In order to create countermeasures, the biomechanical inputs leading to mTBI, which are still a matter of debate, must be better understood in both children and adults.

scholarly journals Pediatric concussion: biomechanical differences between outcomes of transient and persistent (> 4 weeks) postconcussion symptoms

2017 ◽  
Vol 19 (6) ◽  
pp. 641-651 ◽  
Author(s):  
Andrew Post ◽  
T. Blaine Hoshizaki ◽  
Roger Zemek ◽  
Michael D. Gilchrist ◽  
David Koncan ◽  
...  
Keyword(s):  
Brain Injury ◽  
Adult Population ◽  
Linear Acceleration ◽  
Subjective Symptom ◽  
Head Impact ◽  
Data Set ◽  
Cumulative Strain ◽  
Symptom Load ◽  
Postconcussion Symptoms ◽  
Using Data

OBJECTIVECurrently, little is known about the biomechanics of head impact for concussion in youths (ages 5 to 18 years). Even less is known about the biomechanical characteristics and variables related to head impacts that may be useful in differentiating between transient and persistent postconcussion symptoms in a youth population. The purpose of this research was to examine the differences in biomechanics of youth head impact for transient postconcussion symptoms (TPCSs) and persistent postconcussion symptoms (PPCSs) by using data from a hospital population.METHODSIn a laboratory setting and using physical, computational, and finite element models, the authors reconstructed falling events in a large cohort of patients who had sustained a brain injury that resulted in transient or persistent postconcussion symptoms. The falling events and resulting concussions for the TPCS and PPCS patient groups were analyzed in terms of force, energy, peak resultant linear and rotational accelerations, and maximum principal strain in the gray and white matter of the brain, as well as measurements of cumulative strain damage.RESULTSThe results indicated that there were no significant differences between the groups for any of the variables analyzed.CONCLUSIONSWith methods derived for use in an adult population, the magnitudes of peak linear acceleration for the youth data set were determined to be above the 50% risk of injury. The youth data set showed higher brain tissue strain responses for lower energy and impact velocities than measured in adults, suggesting that youths are at higher risk of concussive injury at lower event severities. A trend shown by some variables indicated that larger magnitudes of response were associated with PPCSs, but no single measurement variable consistently differentiated between the TPCS and PPCS groups. It is possible that using the biomechanics of head and brain responses to predict a subjective symptom load may not be appropriate. To enhance future biomechanical analyses, further investigations should include the use of quantifiable measures of brain injury linked to clinical outcomes and possible confounding factors such as history of brain injury and patient predisposition.

Can people with brain injury transfer route knowledge from a virtual environment to the real world?

2004 ◽  
Author(s):  
Roy C. Davies ◽  
Gerd Johansson ◽  
Anita Linden ◽  
Kersin Boschian ◽  
Berigt Sonesson ◽  
...  
Keyword(s):  
Brain Injury ◽  
Virtual Environment ◽  
Real World ◽  
Route Knowledge ◽  
The Real

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

2020 ◽  
Vol 2020 (14) ◽  
pp. 378-1-378-7
Author(s):  
Tyler Nuanes ◽  
Matt Elsey ◽  
Radek Grzeszczuk ◽  
John Paul Shen
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Computational Cost ◽  
False Positives ◽  
Compact Model ◽  
High Quality ◽  
False Negatives ◽  
Trade Off ◽  
Depth Reconstruction ◽  
Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

A Simple and Practical Method for Incorporating Augmented Reality into the Classroom and Laboratory

2018 ◽  
Author(s):  
Kyle Plunkett
Keyword(s):  
Organic Chemistry ◽  
Augmented Reality ◽  
Real Time ◽  
Real World ◽  
Smart Phone ◽  
Practical Method ◽  
Virtual Expert ◽  
Video Projection

This manuscript provides two demonstrations of how Augmented Reality (AR), which is the projection of virtual information onto a real-world object, can be applied in the classroom and in the laboratory. Using only a smart phone and the free HP Reveal app, content rich AR notecards were prepared. The physical notecards are based on Organic Chemistry I reactions and show only a reagent and substrate. Upon interacting with the HP Reveal app, an AR video projection shows the product of the reaction as well as a real-time, hand-drawn curved-arrow mechanism of how the product is formed. Thirty AR notecards based on common Organic Chemistry I reactions and mechanisms are provided in the Supporting Information and are available for widespread use. In addition, the HP Reveal app was used to create AR video projections onto laboratory instrumentation so that a virtual expert can guide the user during the equipment setup and operation.

scholarly journals A Novel Rail-Network Hardware Simulator for Embedded System Programming

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Balaji M ◽  
Chandrasekaran M ◽  
Vaithiyanathan Dhandapani
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Real Time ◽  
Learning Outcomes ◽  
Real World ◽  
Time Constraints ◽  
Network Simulator ◽  
Practical Applications ◽  
Rail Network ◽  
Knowledge Enhancement

A Novel Rail-Network Hardware with simulation facilities is presented in this paper. The hardware is designed to facilitate the learning of application-oriented, logical, real-time programming in an embedded system environment. The platform enables the creation of multiple unique programming scenarios with variability in complexity without any hardware changes. Prior experimental hardware comes with static programming facilities that focus the students’ learning on hardware features and programming basics, leaving them ill-equipped to take up practical applications with more real-time constraints. This hardware complements and completes their learning to help them program real-world embedded systems. The hardware uses LEDs to simulate the movement of trains in a network. The network has train stations, intersections and parking slots where the train movements can be controlled by using a 16-bit Renesas RL78/G13 microcontroller. Additionally, simulating facilities are provided to enable the students to navigate the trains by manual controls using switches and indicators. This helps them get an easy understanding of train navigation functions before taking up programming. The students start with simple tasks and gradually progress to more complicated ones with real-time constraints, on their own. During training, students’ learning outcomes are evaluated by obtaining their feedback and conducting a test at the end to measure their knowledge acquisition during the training. Students’ Knowledge Enhancement Index is originated to measure the knowledge acquired by the students. It is observed that 87% of students have successfully enhanced their knowledge undergoing training with this rail-network simulator.

Investigating whole‐brain metabolite abnormalities in the chronic stages of moderate or severe traumatic brain injury

PM&R ◽  
2021 ◽  
Author(s):  
Joanne C Lin ◽  
Christina Mueller ◽  
Kelsey A Campbell ◽  
Halle H Thannickal ◽  
Altamish F Daredia ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Severe Traumatic Brain Injury ◽  
Whole Brain ◽  
Brain Metabolite

ViFin

2021 ◽  
Vol 5 (1) ◽  
pp. 1-25
Author(s):  
Wenqiang Chen ◽  
Lin Chen ◽  
Meiyi Ma ◽  
Farshid Salemi Parizi ◽  
Shwetak Patel ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Wearable Devices ◽  
User Studies ◽  
Finger Movement ◽  
Writing System ◽  
Time System ◽  
Real Time System ◽  
Input Devices ◽  
A New Technique

Wearable devices, such as smartwatches and head-mounted devices (HMD), demand new input devices for a natural, subtle, and easy-to-use way to input commands and text. In this paper, we propose and investigate ViFin, a new technique for input commands and text entry, which harness finger movement induced vibration to track continuous micro finger-level writing with a commodity smartwatch. Inspired by the recurrent neural aligner and transfer learning, ViFin recognizes continuous finger writing, works across different users, and achieves an accuracy of 90% and 91% for recognizing numbers and letters, respectively. We quantify our approach's accuracy through real-time system experiments in different arm positions, writing speeds, and smartwatch position displacements. Finally, a real-time writing system and two user studies on real-world tasks are implemented and assessed.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

Assessing reinforcing versus aversive consequences in a real-time secondhand smoke intervention

2021 ◽  
Author(s):  
Vincent Berardi ◽  
John Bellettiere ◽  
Benjamin Nguyen ◽  
Neil E Klepeis ◽  
Suzanne C Hughes ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Secondhand Smoke ◽  
Relative Effectiveness ◽  
Feedback System ◽  
Control Group ◽  
Sensing Technology ◽  
Control Versus ◽  
Particle Counts ◽  
Post Hoc

Abstract Few studies have examined the relative effectiveness of reinforcing versus aversive consequences at changing behavior in real-world environments. Real-time sensing devices makes it easier to investigate such questions, offering the potential to improve both intervention outcomes and theory. This research aims to describe the development of a real-time, operant theory-based secondhand smoke (SHS) intervention and compare the efficacy of aversive versus aversive plus reinforcement contingency systems. Indoor air particle monitors were placed in the households of 253 smokers for approximately three months. Participants were assigned to a measurement-only control group (N = 129) or one of the following groups: 1.) aversive only (AO, N = 71), with aversive audio/visual consequences triggered by the detection of elevated air particle measurements, or 2.) aversive plus reinforcement (AP, N = 53), with reinforcing consequences contingent on the absence of SHS added to the AO intervention. Residualized change ANCOVA analysis compared particle concentrations over time and across groups. Post-hoc pairwise comparisons were also performed. After controlling for Baseline, Post-Baseline daily particle counts (F = 6.42, p = 0.002), % of time >15,000 counts (F = 7.72, p < 0.001), and daily particle events (F = 4.04, p = 0.02) significantly differed by study group. Nearly all control versus AO/AP pair-wise comparisons were statistically significant. No significant differences were found for AO versus AP groups. The aversive feedback system reduced SHS, but adding reinforcing consequences did not further improve outcomes. The complexity of real-world environments requires the nuances of these two contingency systems continue to be explored, with this study demonstrating that real-time sensing technology can serve as a platform for such research.

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