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Dementia is a progressive neurodegenerative disorder affecting millions of people worldwide. Early prediction of dementia, especially during the mild cognitive impairment (MCI) stage, is crucial for timely intervention and management. Gait analysis provides indicators of cognitive decline and help identify individuals at risk of progression. This study aims to extract gait features from video recordings and apply machine learning models (Support Vector Machine (SVM), XGBoost, and Logistic Regression (LR)) to predict conversion from MCI to dementia. The dataset consists of videos from 62 individuals with MCI, of whom 31 later converted to dementia at 2-year follow-up. Participants performed the Timed Up and Go (TUG) test under single-task and dual-task (TUGdt) conditions, including animal naming (TUGdt-NA) and reciting months in reverse order (TUGdt-MB). The results showed that SVM achieved the highest performance with an accuracy of 70% and F1 score of 69%. These findings show that gait-based machine learning models, particularly SVM, show promise for early prediction of dementia conversion in individuals with MCI.

Background Dementia disorders are affecting millions of people globally, characterized by memory loss, communication difficulties, and motor function decline. Accurate and early dementia detection is crucial for effective management and treatment. Gait analysis offers a non-invasive method for dementia detection by identifying subtle changes in walking patterns that often precede cognitive symptoms.

Objective This study aims to evaluate the clinical utility of video-based gait analysis using the Timed Up and Go (TUG) test under single and dual-task conditions (TUGdt) for distinguishing individuals with dementia disorders from healthy controls (HCs).

Method The study implemented three machine learning models: Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), to discriminate between persons with dementia and HCs. The dataset consists of a cohort of 64 people with dementia (47 with Alzheimer's disease) and 67 HCs. The participants performed the TUG test as a single and dual-task (TUGdt). In the TUGdt, participants performed the TUG test while simultaneously completing an additional cognitive task (i.e., animal naming (TUGdt-NA) or reciting months in reverse order (TUGdt-MB)).

Results The results showed that dual-task classification outperformed the single-task. The SVM algorithm achieved the highest accuracy in the TUGdt-NA task (accuracy of 87% ± 5.1 and recall of 86.6% ± 3.2) using 5-fold cross-validation and accuracy of 85.5% and recall of 89.5% using Leave-One-Out Cross-Validation (LOOCV) in the TUGdt-MB task.

Conclusions In summary, video-based gait features effectively distinguish people with dementia from HCs, particularly under dual-tasking, offering cost-effective, automated, and non-invasive pre-screening to complement clinical assessments.

Dementia is a neurodegenerative disorder that affects millions of individuals around the world. Early diagnosis of dementia is crucial to provide timely intervention and management. Gait analysis offers insights into the cognitive impairments, which are early signs of dementia. This study aims to extract several relevant features for gait analysis and utilize these features in machine learning algorithms, specifically Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM), for multiclass classification of dementia (Mild cognitive impairment (MCI), subjective cognitive impairment (SCI), dementia, and healthy controls (HCs)). The dataset used in this study consists of videos of 64 people with dementia, 63 with MCI, 64 with SCI, and 67 HCs. Participants performed the Timed Up and Go (TUG) test under single-task and dualtask conditions (either animal naming (TUGdt-NA) or reciting months in reverse order (TUGdt-MB)). The findings reveal that dual-task gait consistently outperformed single task. Moreover, the XGBoost algorithm achieved the highest accuracy of 87 % in the naming animals dual-task (TUGdtNA).

BACKGROUND: Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer's disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection.

OBJECTIVE: Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods.

METHODS: A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review.

RESULTS: The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results.

CONCLUSIONS: The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.

Parkinson's disease (PD) is a neurodegenerative disorder that affects millions of people worldwide, causing various motor and non-motor symptoms. Early diagnosis of PD is crucial for timely intervention and management. Gait analysis provides insights into the motor impairments with PD, aiding in early detection. In this study, different deep learning models such as CNN, LSTM, and CNN-LSTM with varying neural network depths were explored to classify PD using gait data acquired through sensor technology. The study then compared the results of deep learning models with machine learning algorithms (Random Forest (RF) and Decision Trees (DT)). The dataset used in this study consists of 93 persons with PD and 73 healthy controls (HC) collected through sensor technology. The findings reveal that the RF algorithm achieved the highest accuracy of 96%, followed by the CNN-LSTM model of 95.49 %. © 2024 IEEE.

This study addresses the critical safety issue of declining retroreflectivity values of road traffic signs, which can lead to unsafe driving conditions, especially at night. The paper aims to predict the retroreflectivity coefficient values of these signs and to classify their status as acceptable or rejected (in need of replacement) using machine learning models. Moreover, logistic regression and survival analysis are used to predict the median lifespans of road traffic signs across various geographical locations, focusing on signs in Croatia and Sweden as case studies. The results indicate high accuracy in the predictive models, with classification accuracy at 94% and an R2 value of 94% for regression analysis. A significant finding is that a considerable number of signs maintain acceptable retroreflectivity levels within their warranty period, suggesting the feasibility of extending maintenance checks and warranty periods to 15 years which is longer than the current standard of 10 years. Additionally, the study reveals notable variations in the median lifespans of signs based on color and location. Blue signs in Croatia and Sweden exhibit the longest median lifespans (28 to 35 years), whereas white signs in Sweden and red signs in Croatia show the shortest (16 and 10 years, respectively). The high accuracy of logistic regression models (72–90%) for lifespan prediction confirms the effectiveness of this approach. These findings provide valuable insights for road authorities regarding the maintenance and management of road traffic signs, enhancing road safety standards. © 2024 Tongji University and Tongji University Press

The color of road signs is a critical aspect of road safety, as it helps drivers quickly and accurately identify and respond to these signs. Properly colored road signs improve visibility during the day and make it easier for drivers to make informed decisions while driving. In order to ensure the safety and efficiency of road traffic, it is essential to maintain the appropriate color level of road signs. The objective of this study was to analyze the color status and daylight chromaticity of in-use road signs using supervised machine learning models, and to explore the correlation between road sign's age and daylight chromaticity. Three algorithms were employed: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The data used in this study was collected from road signs that were in-use on roads in Sweden. The study employed classification models to assess the color status (accepted or rejected) of the road signs based on minimum acceptable color levels according to standards, and regression models to predict the daylight chromaticity values. The correlation between road sign's age and daylight chromaticity was explored through regression analysis. Daylight chromaticity describes the color quality of road signs in daylight, that is expressed in terms of X and Y chromaticity coordinates. The study revealed a linear relationship between the road sign's age and daylight chromaticity for blue, green, red, and white sheeting, but not for yellow. The lifespan of red signs was estimated to be around 12 years, much shorter than the estimated lifespans of yellow, green, blue, and white sheeting, which are 35, 42, 45, and 75 years, respectively. The supervised machine learning models successfully assessed the color status of the road signs and predicted the daylight chromaticity values using the three algorithms. The results of this study showed that the ANN classification and ANN regression models achieved high accuracy of 81% and R2 of 97%, respectively. The RF and SVM models also performed well, with accuracy values of 74% and 79% and R2 ranging from 59% to 92%. The findings demonstrate the potential of machine learning to effectively predict the status and daylight chromaticity of road signs and their impact on road safety in the Swedish context. © 2023 International Association of Traffic and Safety Sciences

AbstractRoad signs play a vital role in providing drivers with crucial information for safe driving in both day and nighttime. The color of road signs enhances visibility during daylight hours, while retroreflectivity is essential for improving visibility during nighttime conditions. Road authorities, responsible for maintaining road signs, primarily consider the levels of retroreflectivity when deciding to replace them, ensuring optimal visibility for drivers. This study focuses on examining the degradation of road signs based on retroreflectivity and color to ensure safe driving through adequate visibility in both day and nighttime conditions. The study underscores the significance of regulating the deterioration of road sign colors to enhance visibility and legibility, while minimizing maintenance and replacement costs. The primary objective of this paper is to predict the age (service life) of road signs by considering both retroreflectivity and color status and using logistic regression. The results indicate that the age of road signs can be influenced by either retroreflectivity or color. For instance, approximately 50% of red road signs are projected to lose their color after 16 years, while their retroreflectivity remains acceptable. Similarly, around 50% of yellow and white road signs experience retroreflectivity degradation after 20 and 16 years, respectively, while their color remains acceptable. Finally, blue road signs demonstrate acceptable retroreflectivity and color levels even after 35 years.

The road traffic signs in Sweden have no inventory system and it is unknown when a sign has reached the end of its service life and needs to be replaced. As a result, the road authorities do not have a systematic maintenance program for road traffic signs, and many signs which are not in compliance with the minimum retroreflectivity performance requirements are still found on the roads. Therefore, it is very important to find an inexpensive, safe, easy, and highly accurate method to judge the retroreflectivity performance of road signs. This will enable maintenance staff to determine the retroreflectivity of road signs without requiring measuring instruments for retroreflectivity or colors performance. As a first step toward the above goal, this paper aims to identify factors affecting the retroreflectivity of road signs. Two different datasets were used, namely, the VTI dataset from Sweden and NMF dataset from Denmark. After testing different models, two logarithmic regression models were found to be the best-fitting models, with R2 values of 0.50 and 0.95 for the VTI and NMF datasets, respectively. The first model identified the age, direction, GPS positions, color, and class of road signs as significant predictors, while the second model used age, color, and the class of road signs. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.