Fine-Grained Intoxicated Gait Classification Using a Bilinear CNN

Consuming excessive amounts of alcohol causes impaired mobility and judgment and driving accidents, resulting in more than 800 injuries and fatalities each day. Passive methods to detect intoxicated drivers beyond the safe driving limit can facilitate Just-In-Time alerts and reduce Driving Under the Influence (DUI) incidents. Popularly-owned smartphones are not only equipped with motion sensors (accelerometer and gyroscope) that can be employed for passively collecting gait (walk) data but also have the processing power to run computationally expensive machine learning models. In this paper, we advance the state-of-the-art by proposing a novel method that utilizes a Bi-linear Convolution Neural Network (BiCNN) for analyzing smartphone accelerometer and gyroscope data to determine whether a smartphone user is over the legal driving limit (0.08) from their gait. After segmenting the gait data into steps, we converted the smartphone motion sensor data to a Gramian Angular Field (GAF) image and then leveraged the BiCNN architecture for intoxication classification. Distinguishing GAF-encoded images of the gait of intoxicated vs. sober users is challenging as the differences between the classes (intoxicated vs. sober) are subtle, also known as a fine-grained image classification problem. The BiCNN neural network has previously produced state-of-the-art results on fine-grained image classification of natural images. To the best of our knowledge, our work is the first to innovatively utilize the BiCNN to classify GAF encoded images of smartphone gait data in order to detect intoxication. Prior work had explored using the BiCNN to classify natural images or explored other gait-related tasks but not intoxication Our complete intoxication classification pipeline consists of several important pre-processing steps carefully adapted to the BAC classification task, including step detection and segmentation, data normalization to account for inter-subject variability, data fusion, GAF image generation from time-series data, and a BiCNN classification model. In rigorous evaluation, our BiCNN model achieves an accuracy of 83.5%, outperforming the previous state-of-the-art and demonstrating the feasibility of our approach.

User-centered preferences for a gait-informed alcohol intoxication app

BACKGROUND

mHealth technology can be used as a potential intervention for alcohol-related consequences. Applications designed to monitor alcohol use and relay information to the user may help to reduce risky behavior. Acceptability of such applications needs to be assessed.

METHODS

Survey data from 139 participants (29.8 years on average, 58% female) completing a single-session study for developing an application to detect blood alcohol concentration (BAC) from gait was analyzed to examine user preferences. Participants reported on their interest in an application for monitoring BAC from gait. Participants also reported on their preference for controlling features of the application. Acceptability and feasibility data were collected. Data were examined for the entire sample as well as differences in preference by age and gender were examined.

RESULTS

The majority of the sample indicated that they were interested in using an mHealth application to infer BAC from their gait. Users were interested in being able to control features of the application, such as monitoring BAC and reporting information to other individuals. Adults, as compared to emerging adults, preferred the ability to turn off the BAC-monitoring feature of the app. Females reported a preference for an app that does not allow the user to turn off notifications for BAC as well as safety features of the app.

CONCLUSIONS

Results of the survey data indicate general interest in mHealth technology that monitors BAC from passive input. These results suggest that such an app may be accepted and used as an intervention for monitoring alcohol levels, which could mediate drinking and alcohol-related consequences.

Estimation of Blood Alcohol Concentration From Smartphone Gait Data Using Neural Networks

Driving is a dynamic activity, which requires quick reflexes and decision making in order to respond to sudden changes in traffic conditions. Alcohol consumption impairs motor and cognitive skills, and causes many driving-related accidents annually. Passive methods of proactively detecting drivers who are too drunk to drive in order to notify them and prevent accidents, have recently been proposed. The effects of alcohol on a drinker's gait (walk) is a reliable indicator of their intoxication level. In this paper, we investigate detecting drinkers' intoxication levels from their gait by using neural networks to analyze sensor data gathered from their smartphone. Using data gathered from a large controlled alcohol study, we perform regression analysis using a Bi-directional Long Short Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) architectures to predict a person's Blood Alcohol Concentration (BAC) from their smartphone's accelerometer and gyroscope data. We innovatively proposed a comprehensive suite of pre-processing techniques and model-specific extensions to vanilla CNN and bi-LSTM models, which are well thought out and adapted specifically for BAC estimation. Our Bi-LSTM architecture achieves an RMSE of 0.0167 and the CNN architecture achieves an RMSE of 0.0168, outperforming state-of-the-art intoxication detection models using Bayesian Regularized Multilayer Perceptrons (MLP) (RMSE of 0.017) and the Random Forest (RF), with hand-crafted features. Moreover, our models learn features from raw sensor data, obviating the need for hand-crafted features, which is time consuming. Moreover, they achieve lower variance across folds and are hence more generalizable.

On Smartphone Sensability of Bi-Phasic User Intoxication Levels from Diverse Walk Types in Standardized Field Sobriety Tests

Intoxicated driving causes 10,000 deaths annually. Smartphone sensing of user gait (walk) to identify intoxicated users in order to prevent drunk driving, have recently emerged. Such systems gather motion sensor (accelerometer and gyroscope) data from the users' smartphone as they walk and classify them using machine or deep learning. Standard Field Sobriety Tests (SFSTs) involve various types of walks designed to cause an intoxicated person to lose their balance. However, SFSTs were designed to make intoxication apparent to a trained law enforcement officer who manually proctors them. No prior work has explored which types of walk yields the most accurate results when assessed autonomously by a smartphone intoxicated gait assessment system. In this paper, we compare how accurately Long Short Term Memory (LSTM), Convolution Neural Network (CNN), Random Forest, Gradient Boosted Machines (GBM) and neural network classifiers are able to detect intoxication levels of drunk subjects who performed normal, walk-and-turn and standing on one foot SFST walks. We also compared the accuracy of intoxication detection on the ascending (increasing intoxication) vs descending (decreasing intoxication) limbs of drinking sessions (bi-phasic). We found smartphone intoxication sensing more accurate on the descending limb of the drinking episode and that intoxication detection on the normal walks of subjects were just as accurate as the SFSTs.

An Open Trial of Electronic Cigarettes for Smoking Cessation Among Methadone-Maintained Smokers

INTRODUCTION

Smoking cessation pharmacotherapies tested in persons with opioid use disorder have produced low quit rates. Electronic cigarettes (e-cigarettes) have been used by many methadone-maintained (MMT) smokers, but controlled trials evaluating cessation and reduction outcomes have not been performed in this population with deleterious tobacco-related health consequences.

METHODS

In this open trial of NJOY e-cigarettes, MMT smokers received 6 weeks of treatment and were instructed to use only e-cigarettes. Outcomes included carbon monoxide confirmed 7-day point smoking cessation prevalence at week 7 (end of treatment) and self-reported change in mean cigarettes per day (CPD) at each 2-week assessment. The final assessment was 2 weeks after treatment ended (week 9).

RESULTS

The 12 participants averaged 46 years old and 50% were male. On average, participants reported smoking 17.8 (±5.3) CPD. One person had a biochemically confirmed quit at week 7. Participants tended to report marked reductions in mean CPD between quit day (week 1) and the week 3 assessment. Relative to baseline, statistically significant reductions in mean CPD were observed at all follow-up assessments. Mean reductions in CPD were -12.4 (95% confidence interval [CI]: -15.0, -9.9; P < .001), -14.8 (95% CI: -17.4, -12.2; P < .001), -13.9 (95% CI: -16.6, -11.2), and -10.8 (95% CI: -13.4, -8.2; P < .01) at the 3-, 5-, 7-, and 9-week assessments, respectively. Adherence to e-cigarettes was 89.1% during the 6 treatment weeks.

CONCLUSIONS

E-cigarettes were associated with reductions in cigarette use. Smoking cessation rates in MMT smokers are low and whether long-term smoking reductions can persist and produce health benefits should be studied.

IMPLICATIONS

E-cigarettes were associated with reduced tobacco use in MMT smokers. Adherence to e-cigarettes is high among methadone smokers. Week-7 smoking quit rates are similar to pharmacotherapies tested in this population.

E-cigarette knowledge, attitudes, and use in opioid dependent smokers

Individuals in treatment for opioid dependence have smoking rates 3-5 times greater than the U.S. prevalence rate. Traditional smoking cessation strategies have been ineffective in this population. Novel approaches are needed as well as harm reduction avenues. E-cigarettes (e-cigs) may provide such a novel harm reduction and cessation opportunity, but little is known about the knowledge of, attitudes about, and usage of e-cigs in opioid dependent smokers. The current study enrolled 315 opioid dependent smokers (164 methadone, 151 buprenorphine), treated in the same health system in Fall River, Massachusetts. The sample was 49.7% male and 85.1% non-Latino White. Overall 98.7% had heard of e-cigs, 73.0% had ever tried e-cigs, and 33.8% had used e-cigs in the past 30 days. The most common reasons for use were curiosity (41.4%) and to quit all nicotine (26.0%). The proportion of opioid dependent smokers that had ever tried e-cigs and used them in the past month was substantially greater than that found in recent general population surveys. While e-cigs have been used to quit smoking, how to optimize their utility as a cessation tool remains undefined. E-cigs should be a part of smoking cessation discussions with this vulnerable, difficult-to-treat population.