Associating transportation planning-related measures with Mild Cognitive Impairment

• URL: http://arxiv.org/abs/2504.09027v1
• Date: Sat, 12 Apr 2025 00:52:25 GMT
• Title: Associating transportation planning-related measures with Mild Cognitive Impairment
• Authors: Souradeep Chattopadhyay, Guillermo Basulto-Elias, Jun Ha Chang, Matthew Rizzo, Shauna Hallmark, Anuj Sharma, Soumik Sarkar,
• Abstract summary: We computed certain variables that reflect daily driving habits of older drivers in Nebraska using geohashing.<n>The variables were then analyzed using a two-fold approach involving data visualization and machine learning models.<n>The C5.0 model demonstrated robust and stable performance with a median recall of 74%, indicating that our methodology was able to identify cognitive impairment in drivers 74% of the time correctly.
• Score: 6.66498412613475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding the relationship between mild cognitive impairment and driving behavior is essential to improve road safety, especially among older adults. In this study, we computed certain variables that reflect daily driving habits, such as trips to specific locations (e.g., home, work, medical, social, and errands) of older drivers in Nebraska using geohashing. The computed variables were then analyzed using a two-fold approach involving data visualization and machine learning models (C5.0, Random Forest, Support Vector Machines) to investigate the efficiency of the computed variables in predicting whether a driver is cognitively impaired or unimpaired. The C5.0 model demonstrated robust and stable performance with a median recall of 74\%, indicating that our methodology was able to identify cognitive impairment in drivers 74\% of the time correctly. This highlights our model's effectiveness in minimizing false negatives which is an important consideration given the cost of missing impaired drivers could be potentially high. Our findings highlight the potential of life space variables in understanding and predicting cognitive decline, offering avenues for early intervention and tailored support for affected individuals.

Related papers

• Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data [2.3072218701168166]
  By 2030, the senior population aged 65 and older is expected to increase by over 50%, significantly raising the number of older drivers on the road. Drivers over 70 face higher crash death rates compared to those in their forties and fifties.
  arXiv  Detail & Related papers  (2025-01-12T20:01:07Z)
• Masked EEG Modeling for Driving Intention Prediction [27.606175591082756]
  This paper pioneers a novel research direction in BCI-assisted driving, studying the neural patterns related to driving intentions. We propose a novel Masked EEG Modeling framework for predicting human driving intentions, including the intention for left turning, right turning, and straight proceeding. Our model attains an accuracy of 85.19% when predicting driving intentions for drowsy subjects, which shows its promising potential for mitigating traffic accidents related to drowsy driving.
  arXiv  Detail & Related papers  (2024-08-08T03:49:05Z)
• In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment [0.8426358786287627]
  The objectives of this paper include designing low-cost in-vehicle sensing hardware capable of obtaining high-precision positioning and telematics data. Our statistical analysis comparing drivers with mild cognitive impairment (MCI) to those without reveals that those with MCI exhibit smoother and safer driving patterns. Our Random Forest models identified the number of night trips, number of trips, and education as the most influential factors in our data evaluation.
  arXiv  Detail & Related papers  (2023-11-15T15:47:24Z)
• DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding [50.81809690183755]
  Traffic accidents frequently lead to fatal injuries, contributing to over 50 million deaths until 2023. Previous research primarily assessed the importance of individual participants, treating them as independent entities. We introduce Driving scene Relationship self-Understanding transformer (DRUformer) to enhance the important object detection task.
  arXiv  Detail & Related papers  (2023-11-11T07:26:47Z)
• Assessing cognitive function among older adults using machine learning and wearable device data: a feasibility study [3.0872517448897465]
  We developed prediction models to differentiate older adults with normal cognition from those with poor cognition. Activity and sleep parameters were also more strongly associated with processing speed, working memory, and attention compared to other cognitive fluency.
  arXiv  Detail & Related papers  (2023-08-28T00:07:55Z)
• Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study Data [40.43737902900321]
  We tackle the problem of workload estimation from driving performance data. Key environmental factors that induce a high mental workload are identified via video analysis. A supervised learning framework is introduced to profile drivers based on the average workload they experience. A Bayesian filtering approach is then proposed for sequentially estimating, in (near) real-time, the driver's instantaneous workload.
  arXiv  Detail & Related papers  (2023-03-26T13:15:44Z)
• Estimating Driver Personality Traits from On-Road Driving Data [1.4330510916280879]
  This paper focuses on the estimation of a driver's psychological characteristics using driving data for driving assistance systems. We develop a model to estimate drivers' psychological characteristics, such as cognitive function, psychological driving style, and workload sensitivity.
  arXiv  Detail & Related papers  (2023-02-09T20:46:46Z)
• Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities [80.37857025201036]
  Key challenge for robotic systems is to figure out the behavior of another agent. Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally. We propose equipping robots with the necessary tools to conduct observational studies on people.
  arXiv  Detail & Related papers  (2022-01-27T22:15:56Z)
• Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data [52.40058724040671]
  Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data.
  arXiv  Detail & Related papers  (2021-10-19T11:45:01Z)
• Driving-Signal Aware Full-Body Avatars [49.89791440532946]
  We present a learning-based method for building driving-signal aware full-body avatars. Our model is a conditional variational autoencoder that can be animated with incomplete driving signals. We demonstrate the efficacy of our approach on the challenging problem of full-body animation for virtual telepresence.
  arXiv  Detail & Related papers  (2021-05-21T16:22:38Z)
• Robustness to Spurious Correlations via Human Annotations [100.63051542531171]
  We present a framework for making models robust to spurious correlations by leveraging humans' common sense knowledge of causality. Specifically, we use human annotation to augment each training example with a potential unmeasured variable. We then introduce a new distributionally robust optimization objective over unmeasured variables (UV-DRO) to control the worst-case loss over possible test-time shifts.
  arXiv  Detail & Related papers  (2020-07-13T20:05:19Z)
• Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? [104.04999499189402]
  Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment. We propose an uncertainty-aware planning method, called emphrobust imitative planning (RIP) Our method can detect and recover from some distribution shifts, reducing the overconfident and catastrophic extrapolations in OOD scenes. We introduce an autonomous car novel-scene benchmark, textttCARNOVEL, to evaluate the robustness of driving agents to a suite of tasks with distribution shifts.
  arXiv  Detail & Related papers  (2020-06-26T11:07:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.