Browsing by Author "Karki, Shashank"

Now showing 1 - 4 of 4
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    Enhancing Digital Twins with Human Movement Data: A Comparative Study of Lidar-Based Tracking Methods
    Karki, Shashank; Pingel, Thomas J.; Baird, Timothy D.; Flack, Addison; Ogle, J. Todd (MDPI, 2024-09-18)
    Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as “flickering” in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy.
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    Implications for spatial non-stationarity and the neighborhood effect averaging problem (NEAP) in green inequality research: evidence from three states in the USA
    Gyanwali, Sophiya; Karki, Shashank; Jang, Kee Moon; Crawford, Thomas W.; Zhang, Mengxi; Kim, Junghwan (Springer, 2024-09-04)
    Recent studies on green space exposure have argued that overlooking human mobility could lead to erroneous exposure estimates and their associated inequality. However, these studies are limited as they focused on single cities and did not investigate multiple cities, which could exhibit variations in people’s mobility patterns and the spatial distribution of green spaces. Moreover, previous studies focused mainly on large-sized cities while overlooking other areas, such as small-sized cities and rural neighborhoods. In other words, it remains unclear the potential spatial non-stationarity issues in estimating green space exposure inequality. To fill these significant research gaps, we utilized commute data of 31,862 people from Virginia, West Virginia, and Kentucky. The deep learning technique was used to extract green spaces from street-view images to estimate people’s home-based and mobility-based green exposure levels. The results showed that the overall inequality in exposure levels reduced when people’s mobility was considered compared to the inequality based on home-based exposure levels, implying the neighborhood effect averaging problem (NEAP). Correlation coefficients between individual exposure levels and their social vulnerability indices demonstrated mixed and complex patterns regarding neighborhood type and size, demonstrating the presence of spatial non-stationarity. Our results underscore the crucial role of mobility in exposure assessments and the spatial non-stationarity issue when evaluating exposure inequalities. The results imply that local-specific studies are urgently needed to develop local policies to alleviate inequality in exposure precisely.
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    Navigating Disparities in Dental Health—A Transit-Based Investigation of Access to Dental Care in Virginia
    Kim, Junghwan; Karki, Shashank; Brickhouse, Tegwyn; Vujicic, Marko; Nasseh, Kamyar; Wang, Changzhen; Zhang, Mengxi (2024-10-30)
    Objective: To identify vulnerable areas and populations with limited access to dental care in Virginia, the study aimed (1) to calculate travel time and accessibility scores to dental care in Virginia using a transit-based accessibility model for all dental clinics and dental clinics participating in the Medicaid dental program and (2) to estimate factors associated with accessibility to dental clinics participating in the Medicaid dental program in Virginia. Methods: The study used building footprints as origins of transit trips to dental care services (or destinations). The study then computed transit-based origin–destination travel time matrices based on the detailed trip information, including in-vehicle and out-of- vehicle travel time. Accessibility scores were calculated by counting the number of dental clinics that can be reached within 60 min. Regression analysis was used to measure factors associated with accessibility scores to dental clinics participating in Medicaid. Results: Residents in smaller regions spent longer travel time to dental clinics by public transit compared with those who resided in larger regions. Medicaid participants also faced longer travel time compared with the general population. Residents spent more than three-fourths of the time waiting for public transit and walking to clinics regardless of where they live and what type of insurance they have. Associations between sociodemographic factors and accessibility scores to dental clinics participating in the Medicaid dental program varied across regions. Conclusions: Disparities in dental care accessibility exist depending on the size of regions and Medicaid participation in Virginia. The disparities in transit-based access to dental clinics and a disproportionate amount of time spent waiting for public transit and walking to dental clinics could be improved through tailored interventions taking into account the sociodemographic and geographic characteristics of each region.
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    Tracking Human Movement Indoors Using Terrestrial Lidar
    Karki, Shashank (Virginia Tech, 2024-06-03)
    Recent developments in surveying and mapping technologies have greatly enhanced our ability to model and analyze both outdoor and indoor environments. This research advances the traditional concept of digital twins—static representations of physical spaces—by integrating real-time data on human occupancy and movement to develop a dynamic digital twin. Utilizing the newly constructed mixed-use building at Virginia Tech as a case study, this research leverages 11 terrestrial lidar sensors to develop a dynamic digital model that continuously captures human activities within public spaces of the building. Three distinct object detection methodologies were evaluated: deep learning models, OpenCV-based techniques, and Blickfeld's lidar perception software, Percept. The deep learning and OpenCV techniques analyzed projected 2D raster images, while Percept utilized real-time 3D point clouds to detect and track human movement. The deep learning approach, specifically the YOLOv5 model, demonstrated high accuracy with an F1 score of 0.879. In contrast, OpenCV methods, while less computationally demanding, showed lower accuracy and higher rates of false detections. Percept, operating on real-time 3D lidar streams, performed well but was susceptible to errors due to temporal misalignment. This study underscores the potential and challenges of employing advanced lidar-based technologies to create more comprehensive and dynamic models of indoor spaces. These models significantly enhance our understanding of how buildings serve their users, offering insights that could improve building design and functionality.