Scholarly Works, Industrial and Systems Engineering

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https://hdl.handle.net/10919/24292

Research articles, presentations, and other scholarship

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Wrist-worn voice recorders capture the circumstances and context of losses of balance among community-dwelling older adults
Lee, Youngjae; Alexander, Neil B.; Pompeii, Lisa; Nyquist, Linda V.; Madigan, Michael L. (Wiley, 2024-08-16)
Background: Most falls among community-dwelling older adults are due to a loss of balance (LOB) after tripping or slipping. Unfortunately, limited insight is available on the detailed circumstances and context of these LOBs. Moreover, commonly used methods to collect this information is susceptible to limitations of memory recall. The goal of this pilot observational study was to explore the circumstances and context of self-reported LOBs captured by wristworn voice recorders among community-dwelling older adults. Methods: In this pilot observational cohort study, 30 community-dwelling adults with a mean (SD) age of 71.8 (4.4) years were asked to wear a voice recorder on their wrist daily for 3 weeks. Following any naturally-occurring LOB, participants were asked to record their verbal responses to six questions regarding the circumstances and context of each LOB abbreviated with the mnemonic 4WHO: When, Where, What, Why, How, and Outcome. Results: Participants wore the voice recorder 10.9 (0.6) hours per day for 20.7 (0.5) days. One hundred seventy-five voice recordings were collected, with 122 meeting our definition of a LOB. Each participant reported 0–23 LOBs over the 3 weeks or 1.4 (2.1) per participant per week. Across all participants, LOBs were most commonly reported 3 p.m. or later (42%), inside the home (39%), while walking (33%), resulting from a trip (54%), and having induced a stepping response to regain balance (48%). No LOBs resulted in a fall. Conclusions: Among community-dwelling older adults, wrist-worn voice recorders capture the circumstances and context of LOBs thereby facilitating the documentation of detail of LOBs and potentially falls, without reliance on later recall.
In-Depth Insights into the Application of Recurrent Neural Networks (RNNs) in Traffic Prediction: A Comprehensive Review
He, Yuxin; Huang, Ping; Hong, Weihang; Luo, Qin; Li, Lishuai; Tsui, Kwok-Leung (MDPI, 2024-09-06)
Traffic prediction is crucial for transportation management and user convenience. With the rapid development of deep learning techniques, numerous models have emerged for traffic prediction. Recurrent Neural Networks (RNNs) are extensively utilized as representative predictive models in this domain. This paper comprehensively reviews RNN applications in traffic prediction, focusing on their significance and challenges. The review begins by discussing the evolution of traffic prediction methods and summarizing state-of-the-art techniques. It then delves into the unique characteristics of traffic data, outlines common forms of input representations in traffic prediction, and generalizes an abstract description of traffic prediction problems. Then, the paper systematically categorizes models based on RNN structures designed for traffic prediction. Moreover, it provides a comprehensive overview of seven sub-categories of applications of deep learning models based on RNN in traffic prediction. Finally, the review compares RNNs with other state-of-the-art methods and highlights the challenges RNNs face in traffic prediction. This review is expected to offer significant reference value for comprehensively understanding the various applications of RNNs and common state-of-the-art models in traffic prediction. By discussing the strengths and weaknesses of these models and proposing strategies to address the challenges faced by RNNs, it aims to provide scholars with insights for designing better traffic prediction models.
Predicting External Hand Forces During Overhead Work: An Approach Using EMG and Random Forest Regression
Behjati Ashtiani, Mohamad; Freidouny, Mohammadreza; Ojelade, Aanuoluwapo; Kim, Sunwook; Nussbaum, Maury A. (Sage, 2024-08-29)
We developed a predictive model to estimate dynamic external hand forces during overhead tasks while wearing arm-support exoskeletons (ASEs). Despite the reported potential of ASEs to reduce muscle activation during overhead work, challenges in EMG sensor placement hinder comprehensive muscle monitoring. ASE effectiveness can be assessed by estimating shoulder forces through inverse dynamics, which requires external forces and body kinematics. Direct measurement of external forces can be quite challenging in practice. However, a predictive model could support estimating these forces without load cells. Participants completed task simulations using ASEs, while muscle activity and external forces were measured. Employing a random forest algorithm, EMG signals were mapped to force time series, accounting for participant characteristics and task parameters. Mean load cell values were 7.6 (SD 30.5) N, while predicted values were 7.6 (SD 22.7) N, affirming the potential of using EMG signals to estimate external hand forces while using ASEs.
Changes in forklift driving performance and postures among novices resulting from training using a high-fidelity virtual reality simulator: An exploratory study
Islam, Md Shafiqul; Zahabi, Saman Jamshid Nezhad; Kim, Sunwook; Lau, Nathan; Nussbaum, Maury A.; Lim, Sol (Elsevier, 2024-11-01)
Virtual reality (VR) has emerged as a promising tool for training. Our study focused on training for forklift driving, to address an ongoing worker shortage, and the unknown impact of repeated VR training on task performance and kinematic adaptations. We trained 20 novice participants using a VR forklift simulator over two days, with two trials on each day, and including three different driving lessons of varying difficulties. Driving performance was assessed using task completion time, and we quantified kinematics of the head, shoulder, and lumbar spine. Repeated training reduced task completion time (up to ∼29.8% of initial trial) and decreased both kinematic variability and peak range of motion, though these effects were larger for lessons requiring higher precision than simple driving maneuvers. Our results highlight the potential of VR as an effective training environment for novice drivers and suggest that monitoring kinematics could help track skill acquisition during such training.
A Uniform Error Bound for Stochastic Kriging: Properties and Implications on Simulation Experimental Design
Chen, Xi; Zhang, Yutong; Xie, Guangrui; Zhang, Jingtao (ACM, 2024-08)
In this work, we propose a method to construct a uniform error bound for the SK predictor. In investigating the asymptotic properties of the proposed uniform error bound, we examine the convergence rate of SK's predictive variance under the supremum norm in both fixed and random design settings. Our analyses reveal that the large-sample properties of SK prediction depend on the design-point set determined by the design-point sampling scheme and the budget allocation scheme adopted. Appropriately controlling the order of noise variances through budget allocation is crucial for achieving a desirable convergence rate of SK's approximation error, as quantified by the uniform error bound, and for maintaining SK's numerical stability. Moreover, we investigate the impact of noise variance estimation on the uniform error bound's performance theoretically and numerically. Through comprehensive numerical evaluations, we demonstrate the superiority of the proposed uniform bound to the Bonferroni correction-based simultaneous confidence interval under various experimental settings.
Systems mapping of multilevel factors contributing to dental caries in adolescents
Sadjadpour, Fatima; Hosseinichimeh, Niyousha; Pahel, Bhavna T.; Metcalf, Sara S. (Frontiers Media, 2024-01-31)
Dental caries is a prevalent chronic disease among adolescents. Caries activity increases significantly during adolescence due to an increase in susceptible tooth surfaces, immature permanent tooth enamel, independence in pursuing self-care, and a tendency toward poor diet and oral hygiene. Dental caries in permanent teeth is more prevalent among adolescents in low-income families and racial/ethnic minority groups, and these disparities in adolescent dental caries experience have persisted for decades. Several conceptual and datadriven models have proposed unidirectional mechanisms that contribute to the extant disparities in adolescent dental caries experience. Our objective, using a literature review, is to provide an overview of risk factors contributing to adolescent dental caries. Specifically, we map the interactive relationships of multilevel factors that influence dental caries among adolescents. Such interactive multilevel relationships more closely reflect the complex nature of dental caries experience among the adolescent population. The methods that we use are two-fold: (1) a literature review using PubMed and Cochrane databases to find contributing factors; and (2) the system dynamics approach for mapping feedback mechanisms underlying adolescent dental caries through causal loop diagramming. The results of this study, based on the review of 138 articles, identified individual, family and community-level factors and their interactions contributing to dental caries experience in adolescents. Our results also provide hypotheses about the mechanisms underlying persistence of dental caries among adolescents. Conclusions: Our findings may contribute to a deeper understanding of the multilevel and interconnected factors that shape the persistence of dental caries experience among adolescents.
Shoulder kinematics during cyclic overhead work are affected by a passive arm support exoskeleton
Casu, Giulia; Barajas-Smith, Isaiah; Barr, Alan; Phillips, Brandon; Kim, Sunwook; Nussbaum, Maury A.; Rempel, David; Pau, Massimiliano; Harris-Adamson, Carisa (Elsevier, 2024-07-25)
Purpose: We investigated the influence of passive arm-support exoskeleton (ASE) with different levels of torque (50, 75, and 100%) on upper arm osteokinematics. Methods: Twenty participants completed a cyclic overhead drilling task with and without ASE. Task duration, joint angles, and angular acceleration peaks were analyzed during ascent and descent phases of the dominant upper arm. Results: Maximum ASE torque was associated with decreased peak acceleration during ascent (32.2%; SD 17.8; p < 0.001) and descent phases (38.8%; SD 17.8; p < 0.001). Task duration remained consistent. Increased torque led to a more flexed (7.2°; SD 5.5; p > 0.001) and internally rotated arm posture (17.6°; SD 12.1; p < 0.001), with minimal changes in arm abduction. Conclusion: The small arm accelerations and changes in osteokinematics we observed, support the use of this ASE, even while performing overhead cyclic tasks with the highest level of support.
Modeling of drinking and driving behaviors among adolescents and young adults in the United States: Complexities and Intervention outcomes
Hosseinichimeh, Niyousha; MacDonald, Rod; Li, Kaigang; Fell, James C.; Haynie, Denise L.; Simons-Morton, Bruce; Banz, Barbara C.; Camenga, Deepa R.; Iannotti, Ronald J.; Curry, Leslie A.; Dziura, James; Andersen, David F.; Vaca, Federico E. (Elsevier, 2024-07-22)
Alcohol-impaired driving is a formidable public health problem in the United States, claiming the lives of 37 individuals daily in alcohol-related crashes. Alcohol-impaired driving is affected by a multitude of interconnected factors, coupled with long delays between stakeholders’ actions and their impacts, which not only complicate policy-making but also increase the likelihood of unintended consequences. We developed a system dynamics simulation model of drinking and driving behaviors among adolescents and young adults. This was achieved through group model building sessions with a team of multidisciplinary subject matter experts, and a focused literature review. The model was calibrated with data series from multiple sources and replicated the historical trends for male and female individuals aged 15 to 24 from 1982 to 2020. We simulated the model under different scenarios to examine the impact of a wide range of interventions on alcohol-related crash fatalities. We found that interventions vary in terms of their effectiveness in reducing alcohol-related crash fatalities. In addition, although some interventions reduce alcohol-related crash fatalities, some may increase the number of drinkers who drive after drinking. Based on insights from simulation experiments, we combined three interventions and found that the combined strategy may reduce alcohol-related crash fatalities significantly without increasing the number of alcohol-impaired drivers on US roads. Nevertheless, related fatalities plateau over time despite the combined interventions, underscoring the need for new interventions for a sustained decline in alcohol-related crash deaths beyond a few decades. Finally, through model calibration we estimated time delays between actions and their consequences in the system which provide insights for policymakers and activists when designing strategies to reduce alcohol-related crash fatalities.
Review on Lithium-ion Battery PHM from the Perspective of Key PHM Steps
Kong, Jinzhen; Liu, Jie; Zhu, Jingzhe; Zhang, Xi; Tsui, Kwok-Leung; Peng, Zhike; Wang, Dong (2024-07-22)
Prognostics and health management (PHM) has gotten considerable attention in the background of Industry 4.0. Battery PHM contributes to the reliable and safe operation of electric devices. Nevertheless, relevant reviews are still continuously updated over time. In this paper, we browsed extensive literature related to battery PHM from 2018 to 2023 and summarized advances in battery PHM field, including battery testing and public datasets, fault diagnosis and prediction methods, health status estimation and health management methods. The last topic includes state of health estimation methods, remaining useful life prediction methods and predictive maintenance methods. Each of these categories is introduced and discussed in details. Based on this survey, we accordingly discuss challenges left to battery PHM, and provide future research opportunities. This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners.
Evaluation of a Passive Arm-Support Exoskeleton for Surgical Team Members: Results from Live Surgeries
Cha, Jackie S.; Athanasiadis, Dimitrios; Asadi, Hamed; Stefanidis, Dimitrios; Nussbaum, Maury A.; Yu, Denny (Elsevier)
Background: Musculoskeletal symptoms and injuries adversely impact the health of surgical team members and their performance in the operating room (OR). Though ergonomic risks in surgery are well-recognized, mitigating these risks is especially difficult. In this study, we aimed to assess the impacts of an exoskeleton when used by OR team members during live surgeries. Methods: A commercial passive arm-support exoskeleton was used. One surgical nurse, one attending surgeon, and five surgical trainees participated. Twenty-seven surgeries were completed, 12 with and 15 without the exoskeleton. Upper-body postures and muscle activation levels were measured during the surgeries using inertial measurement units and electromyography sensors, respectively. Postures, muscle activation levels, and self-report metrics were compared between the baseline and exoskeleton conditions using non-parametric tests. Results: Using the exoskeleton significantly decreased the percentage of time in demanding postures (>45° shoulder elevation) for the right shoulder by 7% and decreased peak muscle activation of the left trapezius, right deltoid, and right lumbar erector spinae muscles, by 7%, 8%, and 12%, respectively. No differences were found in perceived effort, and overall scores on usability ranged from “OK” to “excellent.” Conclusions: Arm-support exoskeletons have the potential to assist OR team members in reducing musculoskeletal pain and fatigue indicators. To further increase usability in the OR, however, better methods are needed to identify the surgical tasks for which an exoskeleton is effective.
Formal Inconsistencies of Expertise Aggregation Techniques Commonly Employed in Engineering Teams
Stephen, Cynthia; Kannan, Hanumanthrao; Salado, Alejandro (MDPI, 2024-05-18)
Engineering managers leverage the expertise of engineers in their teams to inform decisions. Engineers may convey their expertise in the form of opinions and/or judgements. Given a decision, it is common to elicit and aggregate the expertise from various engineers to capture a broader set of experiences and knowledge. Establishing an internally and externally consistent aggregation framework is therefore paramount to yield a meaningful aggregation, that is, to make sure that the expertise of each engineer is accounted for reasonably. However, we contend that most de facto aggregation techniques lack such consistency and lead to the inadequate use and aggregation of engineering expertise. In this paper, we investigate the consistency or lack thereof of various expertise aggregation techniques. We derive implications of such inconsistencies and provide recommendations about how they may be overcome. We illustrate our discussion using safety decisions in engineering as a notional case.
Understanding Multi-user, Handheld Mixed Reality for Group-based MR Games
Bautista Isaza, Carlos Augusto; Enriquez, Daniel; Moon, Hayoun; Jeon, Myounghoon; Lee, Sang Won (ACM, 2024-04-23)
Research has identified applications of handheld-based VR, which utilizes handheld displays or mobile devices, for developing systems that involve users in mixed reality (MR) without the need for head-worn displays (HWDs). Such systems can potentially accommodate large groups of users participating in MR. However, we lack an understanding of how group sizes and interaction methods affect the user experience.} In this paper, we aim to advance our understanding of handheld-based MR in the context of multiplayer, co-located games. We conducted a study (N = 38) to understand how user experiences vary by group size (2, 4, and 8) and interaction method (proximity-based or pointing-based). For our experiment, we implemented a multiuser experience for up to ten users. We found that proximity-based interaction that encouraged dynamic movement positively affected social presence and physical/temporal workload. In bigger group settings, participants felt less challenged and less positive. Individuals had varying preferences for group size and interaction type. The findings of the study will advance our understanding of the design space for handheld-based MR in terms of group sizes and interaction schemes. To make our contributions explicit, we conclude our paper with design implications that can inform user experience design in handheld-based mixed reality contexts.
Wearing a back-support exoskeleton alters lower-limb joint kinetics during single-step recovery following a forward loss of balance
Park, Jang-Ho; Madigan, Michael L.; Kim, Sunwook; Nussbaum, Maury A.; Srinivasan, Divya (Elsevier, 2024-03-31)
We assessed the effects of a passive, back-support exoskeleton (BSE) on lower-limb joint kinetics during the initiation and swing phases of recovery from a forward loss of balance. Sixteen (8M, 8F) young, healthy participants were released from static forward-leaning postures and attempted to recover their balance with a single-step while wearing a BSE (backX^TM) with different levels of support torque and in a control condition. The BSE provided ∼ 15-20 Nm of external hip extension torque on the stepping leg at the end of initiation and beginning of swing phases. Participants were unable to generate sufficient hip flexion torque, power, and work to counteract this external torque, although they sustained hip flexion torque for a more prolonged period, resulting in slightly increased hip contribution to positive leg work (compared to control). However, net positive leg work, and the net contribution of hip joint (human + BSE) to total leg work decreased with BSE use. While all participants had changes in hip joint kinetics, a significant compensatory increase in ankle contribution to positive leg work was observed only among females. Our results suggest that BSE use adversely affects reactive stepping by decreasing the stepping leg kinetic energy for forward propulsion, and that the relative contributions of lower-limb joints to total mechanical work done during balance recovery are altered by BSE use. BSEs may thus need to be implemented with caution for dynamic tasks in occupational settings, as they may impair balance recovery following a forward loss of balance.
Bridging the Gap: Early Education on Robot and AI Ethics through the Robot Theater Platform in an Informal Learning Environment
Mitchell, Jennifer; Dong, Jiayuan; Yu, Shuqi; Harmon, Madison; Holstein, Alethia; Shim, Joon Hyun; Choi, Koeun; Zhu, Qin; Jeon, Myounghoon (ACM, 2024-03-11)
With the rapid advancement of robotics and AI, educating the next generation on ethical coexistence with these technologies is crucial. Our research explored the potential of a child-robot theater afterschool program in introducing and discussing robot and AI ethics with elementary school children. Conducted with 30 participants from a socioeconomically underprivileged school, the program blended STEM (Science, Technology, Engineering & Mathematics) with the arts, focusing on ethical issues in robotics and AI. Using interactive scenarios and a theatrical performance, the program aimed to enhance children’s understanding of major ethical issues in robotics and AI, such as bias, transparency, privacy, usage, and responsibility. Preliminary findings indicate the program’s success in engaging children in meaningful ethical discussions, demonstrating the potential of innovative, interactive educational methods in early education. This study contributes significantly to integrating ethical robotics and AI in early learning, preparing young minds for a technologically advanced and socially responsible future.
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)
Human-AI Collaborative Innovation in Design
Song, Binyang; Zhu, Qihao; Luo, Jianxi (2024)
Human-AI collaboration (HAIC) is a promising strategy to transform engineering design and innovation, yet how to design artificial intelligence (AI) to boost HAIC remains unclear. Accordingly, this paper provides a new, unified, and comprehensive scheme for classifying AI roles. On this basis, we develop an AI design framework that outlines expected AI capabilities, interactive attributes, and trust enablers across various HAIC scenarios, offering guidance for integrating AI into human teams effectively. We also discuss current advancements, challenges, and prospects for future research.
Data-driven Car Drag Coefficient Prediction with Depth and Normal Renderings
Song, Binyang; Yuan, Chenyang; Permenter, Frank; Arechiga, Nikos; Ahmed, Faez (American Society of Mechanical Engineers, 2024)
Generative AI models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization, evaluating engineering metrics is crucial. To make generative models performance-aware and enable them to create high-performing designs, surrogate modeling of these metrics is necessary. However, the currently used representations of 3D shapes either require extensive computational resources to learn or suffer from significant information loss, which impairs their effectiveness in surrogate modeling. To address this issue, we propose a new 2D representation of 3D shapes. We develop a surrogate drag model based on this representation to verify its effectiveness in predicting 3D car drag. We construct a diverse dataset of 4,535 high-quality 3D car meshes labeled by drag coefficients computed from computational fluid dynamics simulations to train our model. Our experiments demonstrate that our model can accurately and efficiently evaluate drag coefficients with an R^2 value above 0.84 for various car categories. Our model is implemented using deep neural networks, making it compatible with recent AI image generation tools (such as Stable Diffusion) and a significant step towards the automatic generation of drag-optimized car designs. Moreover, we demonstrate a case study using the proposed surrogate model to guide a diffusion-based deep generative model for drag-optimized car body synthesis. We have made the dataset and code publicly available at https://decode.mit.edu/projects/dragprediction.
Generative Design for Manufacturing: Integrating Generation with Optimization Using a Guided Voxel Diffusion Model
Song, Binyang; Chilukuri, Premith Kumar; Kang, Sungku; Jin, Ran (2024)
In digital manufacturing, converting advanced designs into quality products is hampered by manufacturers' limited design knowledge, restricting the adoption and enhancement of innovative solutions. This paper addresses this challenge through a novel generative denoising diffusion model (DDM) trained on historical 3D design data, enabling the creation of voxel-based designs that meet manufacturing standards. By integrating a surrogate model for evaluating the manufacturability of generated designs, the proposed DDM is able to optimize manufacturability during the generative process. This paper takes a leap forward from the predominant 2D focus of existing generative models towards 3D generative design, which not only broadens manufacturers' design capabilities but also accelerates the development of practical and optimized products. We demonstrate the efficacy of this approach via a case study on Microbial Fuel Cell (MFC) anode design, illustrating how this method can significantly enhance manufacturing workflows and outcomes. Our research offers a path for manufacturers to deepen their design expertise and foster innovation in digital manufacturing.
Drag-guided Diffusion Models for Vehicle Image Generation
Arechiga, Nikos; Permenter, Frank; Song, Binyang; Yuan, Chenyang (2023-12-15)
Denoising diffusion models trained at web-scale have revolutionized image generation. The application of these tools to engineering design holds promising potential but is currently limited by their inability to understand and adhere to concrete engineering constraints. In this paper, we take a step toward the goal of incorporating quantitative constraints into diffusion models by proposing physics-based guidance, which enables the optimization of a performance metric (as predicted by a surrogate model) during the generation process. As a proof-of-concept, we add drag guidance to Stable Diffusion, which allows this tool to generate images of novel vehicles while simultaneously minimizing their predicted drag coefficients.
Teleoperator-Robot-Human Interaction in Manufacturing: Perspectives from Industry, Robot Manufacturers, and Researchers
Kim, Sunwook; Hernandez, Ivan; Nussbaum, Maury A.; Lim, Sol (Informa, 2024-02-08)
OCCUPATIONAL APPLICATIONS: Industrial robots have become an important aspect in modern industry. In the context of human-robot collaboration, enabling teleoperated robots to work in close proximity to local/onsite humans can provide new opportunities to improve human engagement in a distributed workplace. Interviews with industry stakeholders highlighted several potential benefits of such teleoperator-robot-human collaboration (tRHC), including the application of tRHC to tasks requiring both expertise and manual dexterity (e.g., maintenance and highly skilled tasks in sectors including construction, manufacturing, and healthcare), as well as opportunities to expand job accessibility for individuals with disabilities and older individuals. However, interviewees also indicated potential challenges of tRHC, particularly related to human perception (e.g., perceiving remote environments), safety, and trust. Given these challenges, and the current limited information on the practical value and implementation of tRHC, we propose several future research directions, with a focus on human factors and ergonomics, to help realize the potential benefits of tRHC.

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