Using Phone Sensors and an Artificial Neural Network to Detect Gait Changes During Drinking Episodes in the Natural Environment

Phone sensors could be useful in assessing changes in gait that occur with alcohol consumption. This study determined (1) feasibility of collecting gait-related data during drinking occasions in the natural environment, and (2) how gait-related features measured by phone sensors relate to estimated blood alcohol concentration (eBAC). Ten young adult heavy drinkers were prompted to complete a 5-step gait task every hour from 8pm to 12am over four consecutive weekends. We collected 3-xis accelerometer, gyroscope, and magnetometer data from phone sensors, and computed 24 gait-related features using a sliding window technique. eBAC levels were calculated at each time point based on Ecological Momentary Assessment (EMA) of alcohol use. We used an artificial neural network model to analyze associations between sensor features and eBACs in training (70% of the data) and validation and test (30% of the data) datasets. We analyzed 128 data points where both eBAC and gait-related sensor data was captured, either when not drinking (n=60), while eBAC was ascending (n=55) or eBAC was descending (n=13). 21 data points were captured at times when the eBAC was greater than the legal limit (0.08 mg/dl). Using a Bayesian regularized neural network, gait-related phone sensor features showed a high correlation with eBAC (Pearson's r > 0.9), and >95% of estimated eBAC would fall between -0.012 and +0.012 of actual eBAC. It is feasible to collect gait-related data from smartphone sensors during drinking occasions in the natural environment. Sensor-based features can be used to infer gait changes associated with elevated blood alcohol content

Assessing the Impact of Multi-variate Steering-rate Vehicle Control on Driver Performance in a Simulation Framework

When a driver turns a steering-wheel, he or she normally expects the vehicle\u27s steering system to communicate an equivalent amount of signal to the road-wheels. This relationship is linear and occurs regardless of the steering-wheel\u27s position within its rotational travel. The linear steering paradigm in passenger vehicles has gone largely unchanged since mass production of passenger vehicles began in 1901. However, as more electronically-controlled steering systems appear in conjunction with development of autonomous steering functions in vehicles, an opportunity to advance the existing steering paradigms arises. The following framework takes a human-factors approach toward examining and evaluating alternative steering systems by using Modeling and Simulation methods to track and score human performance. Present conventional steering systems apply a linear relationship between the steering-wheel and the road wheels of a vehicle. The rotational travel of the steering-wheel is 900° and requires two-and-a-half revolutions to travel from end-stop to opposite end-stop. The experimental steering system modeled and employed in this study applies a dynamic curve response to the steering input within a shorter, 225° rotational travel. Accommodation variances, based on vehicle speed and steering-wheel rotational position and acceleration, moderate the apparent steering input to augment a more-practical, effective steering rate. This novel model follows a paradigm supporting the full range of steering-wheel actuation without necessitating hand repositioning or the removal of the driver\u27s hands from the steering-wheel during steering maneuvers. In order to study human performance disparities between novel and conventional steering models, a custom simulator was constructed and programmed to render representative models in a test scenario. Twenty-seven males and twenty-seven females, ranging from the ages of eighteen to sixty-five were tested and scored using the driving simulator that presented two successive driving test vignettes: One vignette using conventional 900° steering with linear response and the other employing the augmented 225° multivariate, non-linear steering. The results from simulator testing suggest that both males and females perform better with the novel system, supporting the hypothesis that drivers of either gender perform better with a system augmented with 225° multivariate, non-linear steering than with a conventional steering system. Further analysis of the simulated-driving scores indicates performance parity between male and female participants, supporting the hypothesis positing no significant difference in driver performance between male and female drivers using the augmented steering system. Finally, composite data from written questionnaires support the hypothesis that drivers will prefer driving the augmented system over conventional steering. These collective findings support justification for testing and refining novel steering systems using Modeling and Simulation methods. As a product of this particular study, a tested and open-sourced simulation framework now exists such that researchers and automotive designers can develop, as well as evaluate their own steering-oriented products within a valid human-factors construct. The open-source nature of this framework implies a commonality by which otherwisedisparate research and development work can be associated. Extending this framework beyond basic investigation to reach applications requiring morespecialized parameters may even impact drivers having special needs. For example, steeringsystem functional characteristics could be comparatively optimized to accommodate individuals afflicted with upper-body deficits or limited use of either or both arms. Moreover, the combined human-factors and open-source approaches distinguish the products of this research as a common and extensible platform by which purposeful automotive-industry improvements can be realized—contrasted with arbitrary improvements that might be brought about predominantly to showcase technological advancements

Study of Machine Learning Methods in Intelligent Transportation Systems

Machine learning and data mining are currently hot topics of research and are applied in database, artificial intelligence, statistics, and so on to discover valuable knowledge and the patterns in big data available to users. Data mining is predominantly about processing unstructured data and extracting meaningful information from them for end users to help take business decisions. Machine learning techniques use mathematical algorithms to find a pattern or extract meaning out from big data. The popularity of such techniques in analyzing business problems has been enhanced by the arrival of big data. The main objective of this thesis is to study the importance of big data and machine learning and their impact on transportation industry. This thesis is primarily a review of the important machine learning algorithms and their applications in the field of big data. The author has tried to showcase the need to extract meaningful information from the vast amount of big data in the form of traffic data available in today’s world and also listed different machine learning techniques that can be used to extract this knowledge required in order to facilitate better decision making for transportation applications. The analysis is done by using five different multivariate analysis and machine learning techniques in data mining namely cluster analysis, multivariate linear regression, hierarchical multiple regression, factor analysis and discriminant analysis in two different software packages namely SPSS and R. As part of the analysis, the author has tried to explain how knowledge extracted from random traffic data containing variables such as age of the driver, sex of the driver, the day of the week, atmospheric condition and blood alcohol content of the driver can play an important role in predicting the traffic crash. The data taken into account is accident data, which was obtained from Fatality Analysis Reporting System (FARS) ranging from the year 1999 to 2009. It is concluded that traffic accidents were mostly impacted by the atmospheric conditions, blood alcohol content followed by the day of the week