Grado Department of Industrial and Systems Engineering

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2nd Workshop on Multimodal Motion Sickness Detection and Mitigation Methods for Car Journeys - Finding Consensus in the Field
Pöhlmann, Katharina; Al-Taie, Ammar; Li, Gang; Dam, Abhraneil; Wang, Yu-Kai; Wei, Chun-Shu; Papaioannou, Georgios (ACM, 2023-09-18)
The adoption of automated vehicles will be a positive step towards road safety and environmental benefits. However, one major challenge that still exist is motion sickness. The move from drivers to passengers who will engage in non-driving related tasks as well as the potential change in the layout of the car interior that will come with automated vehicles are expected to result in a worsened experience of motion sickness. The previous workshop [18] highlighted the need for consensus on guidelines regarding study design for motion sickness research. Hence, this workshop will develop a guide for motion sickness research through reflection and discussions on the current methodologies used by experts in the field. Further it will build on the knowledge collected from the previous workshop and will thereby facilitate not only new research ideas and fruitful collaborations but also find a consensus in the field in regard to study design and methodologies.
The 4th Workshop on Localization vs. Internationalization: Creating an International Survey on Automotive User Interfaces
Stojmenova, Kristina; Lee, Seul Chan; De Oliveira Faria, Nayara; Schroeter, Ronald; Jeon, Myounghoon (ACM, 2022-09-17)
International surveys tend to collect data on attitudes, values and behaviors towards a specific topic from users from multiple countries, providing an insight on the differences and similarities across nations, cultures or geo-political structures. Consequently, international surveys provide important information about the diversity of the user's needs, values and preferences, which have to be taken into consideration when creating products and services as widely used as the personal automobile. The workshop will focus on the design and development of an international survey on automotive user interfaces on a global scale. It will try to identify the most important aspects related to automotive user interfaces, which should be addressed in the survey. It will also prepare a strategy for its international distribution and create a plan for comprehensive data collection. Lastly, it will try to outline venues and communication channels for the survey dissemination, with the goal of achieving wide visibility.
5G Opportunities in Warehousing
George, Roshan; Cherbaka, Natalie; Ellis, Kimberly (2023)
As capabilities of fifth generation wireless technology (5G) improve, adoption will go beyond current urban cellular networks into industrial settings enabling the IoT landscape. 5G primarily delivers value by enhancing mobile broadband through ultra-reliable, low-latency signals and massive machine-type communications. With the concurrent development of 5G and industrial automation, replacing Wi-Fi and LTE services with 5G networks offers an opportunity to enhance scheduling, latency, jitters, and redundancy in demanding applications. Additionally, the equipment redesigns and upgrades to operate in 5G will pave the way for innovation in operational strategies previously constrained by network capabilities. In this paper, we consider the warehouse operations and functions that are most likely to benefit from 5G adoption. The areas 5G will impact in warehousing are robotic operations, such as AGVs/AMRs; augmented reality devices for picking, training, and maintenance; inventory management through real time asset tracking; equipment battery life from network slicing; and data security. In general, the capacity and low-latency available through 5G will support continuous data transfer that is sufficient to support real-time analytics and decision-making. Knowing which functions will benefit most from 5G will provide strategic guidance for upgrading equipment and operations and aid in developing the factory of the future.
Acoustofluidic particle trapping, manipulation, and release using dynamic-mode cantilever sensors
Johnson, Blake N.; Mutharasan, Raj (Royal Society of Chemistry, 2016-11-23)
We show here that dynamic-mode cantilever sensors enable acoustofluidic fluid mixing and trapping of suspended particles as well as the rapid manipulation and release of trapped micro-particles via mode switching in liquid. Resonant modes of piezoelectric cantilever sensors over the 0 to 8 MHz frequency range are investigated. Sensor impedance response, flow visualization studies using dye and micro-particle tracers (100 mum diameter), and finite element simulations of cantilever modal mechanics and acoustic streaming show fluid mixing and particle trapping configurations depend on the resonant mode shape. We found trapped particles could be: (1) rapidly manipulated on millimeter length scales, and (2) released from the cantilever surface after trapping by switching between low- and high-order resonant modes (less than 250 kHz and greater than 1 MHz, respectively). Such results suggest a potentially promising future for dynamic-mode cantilevers in separations, pumping and mixing applications as well as acoustofluidic-enhanced sensing applications.
Age-related strength loss affects non-stepping balance recovery
Koushyar, Hoda; Bieryla, Kathleen A.; Nussbaum, Maury A.; Madigan, Michael L. (Public Library of Science, 2019-01-18)
Aging is associated with a higher risk of falls, and an impaired ability to recover balance after a postural perturbation is an important contributing factor. In turn, this impaired recovery ability likely stems from age-related decrements in lower limb strength. The purpose of this study was to investigate the effects of age-related strength loss on non-stepping balance recovery capability after a perturbation while standing, without constraining movements to the ankle as in prior reports. Two experiments were conducted. In the first, five young adults (ages 20–30) and six community-dwelling older adults (ages 70–80) recovered their balance, without stepping, from a backward displacement of a support surface. Balance recovery capability was quantified as the maximal backward platform displacement that a subject could withstand without stepping. The maximal platform displacement was 27% smaller among the older group (11.8±2.1 cm) vs. the young group (16.2±2.6 cm). In the second experiment, forward dynamic simulations of a two-segment, rigid-body model were used to investigate the effects of manipulating strength in the hip extensors/flexors and ankle plantar flexors/dorsiflexors. In these, typical age-related reductions in strength were included. The model predicted lower maximal platform displacements with age-related reductions only in plantar flexion and hip flexion strength. These findings support the previously reported age-related loss of balance recovery ability, and an important role for plantar flexor strength in this ability. © 2019 Koushyar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Agent-Based Simulation Framework for Epidemic Forecasting during Hajj Seasons in Saudi Arabia
Alshammari, Sultanah Mohammed; Ba-Aoum, Mohammed Hassan; Alganmi, Nofe Ateq; Allinjawi, Arwa AbdulAziz (MDPI, 2021-08-12)
The religious pilgrimage of Hajj is one of the largest annual gatherings in the world. Every year approximately three million pilgrims travel from all over the world to perform Hajj in Mecca in Saudi Arabia. The high population density of pilgrims in confined settings throughout the Hajj rituals can facilitate infectious disease transmission among the pilgrims and their contacts. Infected pilgrims may enter Mecca without being detected and potentially transmit the disease to other pilgrims. Upon returning home, infected international pilgrims may introduce the disease into their home countries, causing a further spread of the disease. Computational modeling and simulation of social mixing and disease transmission between pilgrims can enhance the prevention of potential epidemics. Computational epidemic models can help public health authorities predict the risk of disease outbreaks and implement necessary intervention measures before or during the Hajj season. In this study, we proposed a conceptual agent-based simulation framework that integrates agent-based modeling to simulate disease transmission during the Hajj season from the arrival of the international pilgrims to their departure. The epidemic forecasting system provides a simulation of the phases and rituals of Hajj following their actual sequence to capture and assess the impact of each stage in the Hajj on the disease dynamics. The proposed framework can also be used to evaluate the effectiveness of the different public health interventions that can be implemented during the Hajj, including size restriction and screening at entry points.
An Algorithm for Fast Generation of Bivariate Poisson Random Vectors
Shin, K.; Pasupathy, R. (INFORMS, 2010)
We present the "trivariate reduction extension" (TREx)-an exact algorithm for the fast generation of bivariate Poisson random vectors. Like the normal-to-anything (NORTA) procedure, TREx has two phases: a preprocessing phase when the required algorithm parameters are identified, and a generation phase when the parameters identified during the preprocessing phase are used to generate the desired Poisson vector. We prove that the proposed algorithm covers the entire range of theoretically feasible correlations, and we provide efficient-computation directives and rigorous bounds for truncation error control. We demonstrate through extensive numerical tests that TREx, being a specialized algorithm for Poisson vectors, has a preprocessing phase that is uniformly a hundred to a thousand times faster than a fast implementation of NORTA. The generation phases of TREx and NORTA are comparable in speed, with that of TREx being marginally faster. All code is publicly available.
Am I Really Angry? The Influence of Anger Intensities on Young Drivers' Behaviors
Wang, Manhua; Jeon, Myounghoon (ACM, 2023-09-18)
Anger can lead to aggressive driving and other negative behaviors. While previous studies treated anger as a single dimension, the present research proposed that anger has distinct intensities and aimed to understand the effects of different anger intensities on driver behaviors. After developing the anger induction materials, we conducted a driving simulator study with 30 participants and assigned them to low, medium, and high anger intensity groups. We found that drivers with low anger intensity were not able to recognize their emotions and exhibited speeding behaviors, while drivers with medium and high anger intensities might be aware of their anger along with its adverse effects and then adjusted their longitudinal control. However, angry drivers generally exhibited compromised lateral control indicated by steering and lane-keeping behaviors. Our findings shed light on the potentially different influences of anger intensities on young drivers’ behaviors, especially the importance of anger recognition for intervention solutions.
Applying variance reduction ideas in queuing simulations
Ross, S. M.; Lin, K. Y. (Cambridge University Press, 2001)
Variance reduction techniques are often underused in simulation studies. In this article, we indicate how certain ones can be efficiently employed when analyzing queuing models. The first technique considered is that of dynamic stratified sampling; the second is the utilization of multiple control variates; the third concerns the replacement of random variables by their conditional expectations when trying to estimate the expected value of a sum of random variables.
Approximate Positively Correlated Distributions and Approximation Algorithms for D-optimal Design
Singh, Manoj; Xie, W. (2018-02-26)
Experimental design is a classical problem in statistics and has also found new applications in machine learning. In the experimental design problem, the aim is to estimate an unknown vector x in m-dimensions from linear measurements where a Gaussian noise is introduced in each measurement. The goal is to pick k out of the given n experiments so as to make the most accurate estimate of the unknown parameter x. Given a set S of chosen experiments, the most likelihood estimate x' can be obtained by a least squares computation. One of the robust measures of error estimation is the D-optimality criterion which aims to minimize the generalized variance of the estimator. This corresponds to minimizing the volume of the standard confidence ellipsoid for the estimation error x-x'. The problem gives rise to two natural variants depending on whether repetitions are allowed or not. The latter variant, while being more general, has also found applications in the geographical location of sensors. In this work, we first show that a 1/e-approximation for the D-optimal design problem with and without repetitions giving us the first constant factor approximation for the problem. We also consider the case when the number of experiments chosen is much larger than the dimension of the measurements and provide an asymptotically optimal approximation algorithm.
AR DriveSim: An Immersive Driving Simulator for Augmented Reality Head-Up Display Research
Gabbard, Joseph L.; Smith, Missie; Tanous, Kyle; Kim, Hyungil; Jonas, Bryan (Frontiers, 2019-10-23)
Optical see-through automotive head-up displays (HUDs) are a form of augmented reality (AR) that is quickly gaining penetration into the consumer market. Despite increasing adoption, demand, and competition among manufacturers to deliver higher quality HUDs with increased fields of view, little work has been done to understand how best to design and assess AR HUD user interfaces, and how to quantify their effects on driver behavior, performance, and ultimately safety. This paper reports on a novel, low-cost, immersive driving simulator created using a myriad of custom hardware and software technologies specifically to examine basic and applied research questions related to AR HUDs usage when driving. We describe our experiences developing simulator hardware and software and detail a user study that examines driver performance, visual attention, and preferences using two AR navigation interfaces. Results suggest that conformal AR graphics may not be inherently better than other HUD interfaces. We include lessons learned from our simulator development experiences, results of the user study and conclude with limitations and future work.
Assessing elderly’s functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests
Yu, Lisha; Zhao, Yang; Wang, Hailiang; Sun, Tien-Lung; Murphy, Terrence E.; Tsui, Kwok-Leung (2021-03-25)
Background Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly’s functional balance based on Short Form Berg Balance Scale (SFBBS) score. Methods Data were collected from a waist-mounted tri-axial accelerometer while participants performed a timed up and go test. Clinically relevant variables were extracted from the segmented accelerometer signals for fitting SFBBS predictive models. Regularized regression together with random-shuffle-split cross-validation was used to facilitate the development of the predictive models for automatic balance estimation. Results Eighty-five community-dwelling older adults (72.12 ± 6.99 year) participated in our study. Our results demonstrated that combined clinical and sensor-based variables, together with regularized regression and cross-validation, achieved moderate-high predictive accuracy of SFBBS scores (mean MAE = 2.01 and mean RMSE = 2.55). Step length, gender, gait speed and linear acceleration variables describe the motor coordination were identified as significantly contributed variables of balance estimation. The predictive model also showed moderate-high discriminations in classifying the risk levels in the performance of three balance assessment motions in terms of AUC values of 0.72, 0.79 and 0.76 respectively. Conclusions The study presented a feasible option for quantitatively accurate, objectively measured, and unobtrusively collected functional balance assessment at the point-of-care or home environment. It also provided clinicians and elderly with stable and sensitive biomarkers for long-term monitoring of functional balance.
Asymmetries in Potential for Partisan Gerrymandering
Goedert, Nicholas; Hildebrand, Robert; Travis, Laurel; Pierson, Matthew (2024)
This paper investigates the effectiveness of potential partisan gerrymandering of the U.S. House of Representatives across a range of states. We use a heuristic algorithm to generate district maps that optimize for multiple objectives, including compactness, partisan benefit, and competitiveness. While partisan gerrymandering is highly effective for both sides, we find that the majority of states are moderately biased toward Republicans when optimized for either compactness or partisan benefit, meaning that Republican gerrymanders have the potential to be more effective. However, we also find that more densely populated and more heavily Hispanic states show less Republican bias or even Democratic bias. Additionally, we find that in almost all cases we can generate reasonably compact maps with very little sacrifice to partisan objectives through a mixed objective function. This suggests that there is a strong potential for stealth partisan gerrymanders that are both compact and beneficial to one party. Nationwide, partisan gerrymandering is capable of swinging over one hundred seats in the U.S. House, even when compact districts are simultaneously sought.
Automatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validation
Hsu, Yu-Cheng; Wang, Hailiang; Zhao, Yang; Chen, Frank; Tsui, Kwok-Leung (JMIR Publications, 2021-12-20)
Background: Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest. Objective: The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults. Methods: In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals. Results: The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360 degrees, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360 degrees, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN. Conclusions: The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community's burden of continuous health monitoring.
Automating context dependent gaze metrics for evaluation of laparoscopic surgery manual skills
Deng, Shiyu; Kulkarni, Chaitanya; Parker, Sarah J.; Barnes, Laura E.; Wang, Tianzi; Hartman-Kenzler, Jacob; Safford, Shawn; Lau, Nathan (2022-03)
An Autonomous Task Assignment Paradigm for Autonomous Robotic In-Space Assembly
Hildebrand, Robert; Komendera, Erik; Moser, Joshua; Hoffman, Julia (Frontiers, 2022-02-25)
The development of autonomous robotic systems is a key component in the expansion of space exploration and the development of infrastructures for in-space applications. An important capability for these robotic systems is the ability to maintain and repair structures in the absence of human input by autonomously generating valid task sequences and task to robot allocations. To this end, a novel stochastic problem formulation paired with a mixed integer programming assembly schedule generator has been developed to articulate the elements, constraints, and state of an assembly project and solve for an optimal assembly schedule. The developed formulations were tested with a set of hardware experiments that included generating an optimal schedule for an assembly and rescheduling during an assembly to plan a repair. This formulation and validation work provides a path forward for future research in the development of an autonomous system capable of building and maintaining in-space infrastructures.
Benefits of integrated screening and vaccination for infection control
Rabil, Marie Jeanne; Tunc, Sait; Bish, Douglas R.; Bish, Ebru K. (2021-12)
Importance: Screening and vaccination are essential in the fight against infectious diseases, but need to be integrated and customized based on community and disease characteristics. Objective: To develop effective screening and vaccination strategies, customized for a college campus, to reduce COVID-19 infections, hospitalizations, deaths, and peak hospitalizations. Design, Setting, and Participants: We construct a compartmental model of disease spread for vaccination and routine screening, and study the efficacy of four mitigation strategies (routine screening only, vaccination only, vaccination with partial routine screening, vaccination with full routine screening), and a no-intervention strategy. The study setting is a hypothetical college campus of 5,000 students and 455 faculty members, with 11 undetected, asymptotic SARS-CoV-2 infections at the start of an 80-day semester. For sensitivity analysis, we vary the screening frequency, daily vaccination rate, initial vaccination coverage, and screening and vaccination compliance; and consider three scenarios that represent low/medium/high transmission rates and test efficacy. Model parameters come from publicly available or published sources. Results: With low initial vaccination coverage, even aggressive vaccination and screening result in a high number of infections: 1,024/2,040 (1,532/1,773) with routine daily (every other day) screening of the unvaccinated; 275/895 with daily screening extended to the newly vaccinated in base- and worst-case scenarios, with reproduction numbers 4.75 and 6.75, respectively, representative of COVID-19 Delta variant. With the emergence of the Omicron variant, the reproduction number may increase and/or effective vaccine coverage may decrease if a booster shot is needed to maximize vaccine efficacy. Conclusion: Integrated vaccination and routine screening can allow for a safe opening of a college when initial vaccination coverage is sufficiently high. The interventions need to be customized considering the initial vaccination coverage, estimated compliance, screening and vaccination capacity, disease transmission and adverse outcome rates, and the number of infections/peak hospitalizations the college is willing to tolerate.
Benefits of integrated screening and vaccination for infection control
Rabil, Marie Jeanne; Tunc, Sait; Bish, Douglas R.; Bish, Ebru K. (PLOS, 2022-04-21)
Importance: Screening and vaccination are essential in the fight against infectious diseases, but need to be integrated and customized based on community and disease characteristics. Objective: To develop effective screening and vaccination strategies, customized for a college campus, to reduce COVID-19 infections, hospitalizations, deaths, and peak hospitalizations. Design, setting, and participants: We construct a compartmental model of disease spread under vaccination and routine screening, and study the efficacy of four mitigation strategies (routine screening only, vaccination only, vaccination with partial or full routine screening), and a no-intervention strategy. The study setting is a hypothetical college campus of 5,000 students and 455 faculty members during the Fall 2021 academic semester, when the Delta variant was the predominant strain. For sensitivity analysis, we vary the screening frequency, daily vaccination rate, initial vaccine coverage, and screening and vaccination compliance; and consider scenarios that represent low/medium/high transmission and test efficacy. Model parameters come from publicly available or published sources. Results: With low initial vaccine coverage (30% in our study), even aggressive vaccination and screening result in a high number of infections: 1,020 to 2,040 (1,530 to 2,480) with routine daily (every other day) screening of the unvaccinated; 280 to 900 with daily screening extended to the newly vaccinated in base- and worst-case scenarios, which respectively consider reproduction numbers of 4.75 and 6.75 for the Delta variant. Conclusion: Integrated vaccination and routine screening can allow for a safe opening of a college when both the vaccine effectiveness and the initial vaccine coverage are sufficiently high. The interventions need to be customized considering the initial vaccine coverage, estimated compliance, screening and vaccination capacity, disease transmission and adverse outcome rates, and the number of infections/peak hospitalizations the college is willing to tolerate.
Beyond Finding Change: multitemporal Landsat for forest monitoring and management
Wynne, Randolph H.; Thomas, Valerie A.; Brooks, Evan B.; Coulston, J. O.; Derwin, Jill M.; Liknes, Greg C.; Yang, Z.; Fox, Thomas R.; Ghannam, S.; Abbott, A. Lynn; House, M. N.; Saxena, R.; Watson, Layne T.; Gopalakrishnan, Ranjith (2017-07)
Take homes

Tobler’s Law still in effect with time series – spatial autocorrelation in temporal coherence can help in both preprocessing and estimation

Continual process improvement in extant algorithms needed

Need additional means by which variations within (parameterization) and across algorithms addressed (the Reverend…)

Time series improving higher order products (example with NLCD TCC) enabling near continuous monitoring
Big Data, Smart Buildings, Post-Covid Office Real Estate Decision Making, and Multi-Disciplinary Undergraduate Learning: A Case Study in Discovery Thinking
Kretser, Michael; Cherbaka, Natalie (2024-02-22)

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