2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET)最新文献

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Abnormal Driving Detection using GPS Data. 利用 GPS 数据检测异常驾驶。
Charles Boateng, Kwangsoo Yang, Seyedeh Gol Ara Ghoreishi, Jinwoo Jang, Muhammad Tanveer Jan, Joshua Conniff, Borko Furht, Sonia Moshfeghi, David Newman, Ruth Tappen, Jinnan Zhai, Monica Rosseli
{"title":"Abnormal Driving Detection using GPS Data.","authors":"Charles Boateng, Kwangsoo Yang, Seyedeh Gol Ara Ghoreishi, Jinwoo Jang, Muhammad Tanveer Jan, Joshua Conniff, Borko Furht, Sonia Moshfeghi, David Newman, Ruth Tappen, Jinnan Zhai, Monica Rosseli","doi":"10.1109/honet59747.2023.10374718","DOIUrl":"10.1109/honet59747.2023.10374718","url":null,"abstract":"<p><p>Given a GPS dataset comprising driving records captured at one-second intervals, this research addresses the challenge of Abnormal Driving Detection (ADD). The study introduces an integrated approach that leverages data preprocessing, dimensionality reduction, and clustering techniques. Speed Over Ground (SOG), Course Over Ground (COG), longitude (lon), and latitude (lat) data are aggregated into minute-level segments. We use Singular Value Decomposition (SVD) to reduce dimensionality, enabling K-means clustering to identify distinctive driving patterns. Results showcase the methodology's effectiveness in distinguishing normal from abnormal driving behaviors, offering promising insights for driver safety, insurance risk assessment, and personalized interventions.</p>","PeriodicalId":518005,"journal":{"name":"2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET)","volume":"2023 ","pages":"210-215"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10979306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140338493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment. 针对有轻度认知障碍的老年驾驶员的车载传感和数据分析。
Sonia Moshfeghi, Muhammad Tanveer Jan, Joshua Conniff, Seyedeh Gol Ara Ghoreishi, Jinwoo Jang, Borko Furht, Kwangsoo Yang, Monica Rosselli, David Newman, Ruth Tappen, Dana Smith
{"title":"In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment.","authors":"Sonia Moshfeghi, Muhammad Tanveer Jan, Joshua Conniff, Seyedeh Gol Ara Ghoreishi, Jinwoo Jang, Borko Furht, Kwangsoo Yang, Monica Rosselli, David Newman, Ruth Tappen, Dana Smith","doi":"10.1109/HONET59747.2023.10374639","DOIUrl":"10.1109/HONET59747.2023.10374639","url":null,"abstract":"<p><p>Driving is a complex daily activity indicating age and disease-related cognitive declines. Therefore, deficits in driving performance compared with ones without mild cognitive impairment (MCI) can reflect changes in cognitive functioning. There is increasing evidence that unobtrusive monitoring of older adults' driving performance in a daily-life setting may allow us to detect subtle early changes in cognition. The objectives of this paper include designing low-cost in-vehicle sensing hardware capable of obtaining high-precision positioning and telematics data, identifying important indicators for early changes in cognition, and detecting early-warning signs of cognitive impairment in a truly normal, day-to-day driving condition with machine learning approaches. Our statistical analysis comparing drivers with MCI to those without reveals that those with MCI exhibit smoother and safer driving patterns. This suggests that drivers with MCI are cognizant of their condition and tend to avoid erratic driving behaviors. Furthermore, our Random Forest models identified the number of night trips, number of trips, and education as the most influential factors in our data evaluation.</p>","PeriodicalId":518005,"journal":{"name":"2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET)","volume":"2023 ","pages":"140-145"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10982740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140338494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Anomalous Behavior Detection in Trajectory Data of Older Drivers. 从老年驾驶员的轨迹数据中发现异常行为。
Seyedeh Gol Ara Ghoreishi, Sonia Moshfeghi, Muhammad Tanveer Jan, Joshua Conniff, KwangSoo Yang, Jinwoo Jang, Borko Furht, Ruth Tappen, David Newman, Monica Rosselli, Jiannan Zhai
{"title":"Anomalous Behavior Detection in Trajectory Data of Older Drivers.","authors":"Seyedeh Gol Ara Ghoreishi, Sonia Moshfeghi, Muhammad Tanveer Jan, Joshua Conniff, KwangSoo Yang, Jinwoo Jang, Borko Furht, Ruth Tappen, David Newman, Monica Rosselli, Jiannan Zhai","doi":"10.1109/HONET59747.2023.10374878","DOIUrl":"https://doi.org/10.1109/HONET59747.2023.10374878","url":null,"abstract":"<p><p>Given a road network and a set of trajectory data, the anomalous behavior detection (ABD) problem is to identify drivers that show significant directional deviations, hard-brakings, and accelerations in their trips. The ABD problem is important in many societal applications, including Mild Cognitive Impairment (MCI) detection and safe route recommendations for older drivers. The ABD problem is computationally challenging due to the large size of temporally-detailed trajectories dataset. In this paper, we propose an Edge-Attributed Matrix that can represent the key properties of temporally-detailed trajectory datasets and identify abnormal driving behaviors. Experiments using real-world datasets demonstrated that our approach identifies abnormal driving behaviors.</p>","PeriodicalId":518005,"journal":{"name":"2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET)","volume":"2023 ","pages":"146-151"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10985541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140874426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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