REAL-TIME DETECTION OF CRAVINGS IN INDIVIDUALS WITH SUBSTANCE ABUSE USING WEARABLE BIOSENSORS AND MACHINE LEARNING

Deaths in the US have drastically increased over the past decade due to addictive behaviors and drugs. According to the World Health Organization (WHO), 1 in 20 adults between the age of 15 and 64 years are addicted to at least one illicit drug; globally, 29 million people are suffering from drug use disorder. The addiction of narcotics alters a person’s primary function as well as critical areas of the brain due to multiple reasons like genetics, hereditary, stress or pressure, and mental health conditions. It not only affects an individual but also their families. Intensive research has been launched all over the world to spread awareness about how to prevent addiction. The current problem for efficiently managing and treating these addicted individuals is the lack of biomarker for detecting cravings. If clinicians could identify cravings in individuals, they might able to design appropriate intervention strategies, including mobile based mindfulness techniques, dialectical behavioral therapy (DBT) based exercises, or direct contact with support persons to mitigate risky situations (cravings) that could otherwise result in relapse. In our work, we explored the possibility of employing wearable biosensors along with machine learning approaches to define a reliable biomarker of craving. In this work, participants wore wrist-mounted biosensors on their non-dominant arm for all waking hours for a four-day period. An event marker was used to denote any time they perceived drug craving. For analysis, raw accelerometer data in three axes (x, y, and z) evaluated 20 minutes before and 20 minutes after each marked event. A sliding window technique with signal processing Hilbert transformation approach was applied to extract relevant features mean, variance, shape, scale, and (a distance measure derived using six parameters in a hypothetical six-dimensional space). These features employed in machine learning approach with two different quadratic (non-linear) models to detect cravings. The collaborative work of two machine learning models provided us an accuracy of 72% in the detection of cravings

Understanding Social Context from Smartphone Sensing: Generalization Across Countries and Daily Life Moments

Understanding and longitudinally tracking the social context of people help in understanding their behavior and mental well-being better. Hence, instead of burdensome questionnaires, some studies used passive smartphone sensors to infer social context with machine learning models. However, the few studies that have been done up to date have focused on unique, situated contexts (i.e., when eating or drinking) in one or two countries, hence limiting the understanding of the inference in terms of generalization to (i) everyday life occasions and (ii) different countries. In this paper, we used a novel, large-scale, and multimodal smartphone sensing dataset with over 216K self-reports collected from over 580 participants in five countries (Mongolia, Italy, Denmark, UK, Paraguay), first to understand whether social context inference (i.e., alone or not) is feasible with sensor data, and then, to know how behavioral and country-level diversity affects the inference. We found that (i) sensor features from modalities such as activity, location, app usage, Bluetooth, and WiFi could be informative of social context; (ii) partially personalized multi-country models (trained and tested with data from all countries) and country-specific models (trained and tested within countries) achieved similar accuracies in the range of 80%-90%; and (iii) models do not generalize well to unseen countries regardless of geographic similarity

AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health

In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework’s design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice

Booze Clues: The Integration of Technology into the College Social Scene to Promote Responsible Drinking

Alcohol is prevalent in the social lives of many college students; however, my research suggests the endeavors to promote responsible drinking through the current research and education efforts are failing. Binge drinking remains synonymous to the college social scene, people continue to practice irresponsible drinking behaviors, and students remain uninformed about alcohol and its effects. The purpose of this research was to understand how wearable technology could be integrated into the college social scene to encourage people to drink more responsibly. The study included interviews and a survey. My research suggests alcohol consumption is commonplace in many college students&rsquo; lives, but the majority of people remain uninformed about the consequences of drinking. These findings were used in combination with principles of User-Centered Design to inform a prototype of Booze Clues. Booze Clues is an integration of a wearable device and mobile application. The integration of this technology into people&rsquo;s social lives can educate the users on the consequences of their alcohol consumption in real-time. Booze Clues will not only present the user&rsquo;s current blood alcohol concentration reading as detected by a wearable device; it will also be an educational tool to inform people about blood alcohol concentration and alcohol&rsquo;s impact on the body and mind. It is time for college administrators and researchers who are responsible for educating students on safe drinking practices to reevaluate their efforts. Designed for the college social scene, Booze Clues is the new way to encourage students to practice responsible drinking behaviors.</p

Detecting drinking episodes in young adults using smartphone-based sensors

Abstract Alcohol use in young adults is common, with high rates of morbidity and mortality largely due to periodic, heavy drinking episodes (HDEs). Behavioral interventions delivered through electronic communication modalities (e.g., text messaging) can reduce the frequency of HDEs in young adults, but effects are small. One way to amplify these effects is to deliver support materials proximal to drinking occasions, but this requires knowledge of when they will occur. Mobile phones have built-in sensors that can potentially be useful in monitoring behavioral patterns associated with the initiation of drinking occasions. The objective of our work is to explore the detection of daily-life behavioral markers using mobile phone sensors and their utility in identifying drinking occasions. We utilized data from 30 young adults aged 21–28 with past hazardous drinking and collected mobile phone sensor data and daily Experience Sampling Method (ESM) of drinking for 28 consecutive days. We built a machine learning-based model that is 96.6% accurate at identifying non-drinking, drinking and heavy drinking episodes. We highlight the most important features for detecting drinking episodes and identify the amount of historical data needed for accurate detection. Our results suggest that mobile phone sensors can be used for automated, continuous monitoring of at-risk populations to detect drinking episodes and support the delivery of timely interventions

Continuous Estimation of Smoking Lapse Risk from Noisy Wrist Sensor Data Using Sparse and Positive-Only Labels

Estimating the imminent risk of adverse health behaviors provides opportunities for developing effective behavioral intervention mechanisms to prevent the occurrence of the target behavior. One of the key goals is to find opportune moments for intervention by passively detecting the rising risk of an imminent adverse behavior. Significant progress in mobile health research and the ability to continuously sense internal and external states of individual health and behavior has paved the way for detecting diverse risk factors from mobile sensor data. The next frontier in this research is to account for the combined effects of these risk factors to produce a composite risk score of adverse behaviors using wearable sensors convenient for daily use. Developing a machine learning-based model for assessing the risk of smoking lapse in the natural environment faces significant outstanding challenges requiring the development of novel and unique methodologies for each of them. The first challenge is coming up with an accurate representation of noisy and incomplete sensor data to encode the present and historical influence of behavioral cues, mental states, and the interactions of individuals with their ever-changing environment. The next noteworthy challenge is the absence of confirmed negative labels of low-risk states and adequate precise annotations of high-risk states. Finally, the model should work on convenient wearable devices to facilitate widespread adoption in research and practice. In this dissertation, we develop methods that account for the multi-faceted nature of smoking lapse behavior to train and evaluate a machine learning model capable of estimating composite risk scores in the natural environment. We first develop mRisk, which combines the effects of various mHealth biomarkers such as stress, physical activity, and location history in producing the risk of smoking lapse using sequential deep neural networks. We propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of smoking lapse. To circumvent the lack of confirmed negative labels (i.e., annotated low-risk moments) and only a few positive labels (i.e., sensor-based detection of smoking lapse corroborated by self-reports), we propose a new loss function to accurately optimize the models. We build the mRisk models using biomarker (stress, physical activity) streams derived from chest-worn sensors. Adapting the models to work with less invasive and more convenient wrist-based sensors requires adapting the biomarker detection models to work with wrist-worn sensor data. To that end, we develop robust stress and activity inference methodologies from noisy wrist-sensor data. We first propose CQP, which quantifies wrist-sensor collected PPG data quality. Next, we show that integrating CQP within the inference pipeline improves accuracy-yield trade-offs associated with stress detection from wrist-worn PPG sensors in the natural environment. mRisk also requires sensor-based precise detection of smoking events and confirmation through self-reports to extract positive labels. Hence, we develop rSmoke, an orientation-invariant smoking detection model that is robust to the variations in sensor data resulting from orientation switches in the field. We train the proposed mRisk risk estimation models using the wrist-based inferences of lapse risk factors. To evaluate the utility of the risk models, we simulate the delivery of intelligent smoking interventions to at-risk participants as informed by the composite risk scores. Our results demonstrate the envisaged impact of machine learning-based models operating on wrist-worn wearable sensor data to output continuous smoking lapse risk scores. The novel methodologies we propose throughout this dissertation help instigate a new frontier in smoking research that can potentially improve the smoking abstinence rate in participants willing to quit

Latent variable models for understanding user behavior in software applications

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 147-157).Understanding user behavior in software applications is of significant interest to software developers and companies. By having a better understanding of the user needs and usage patterns, the developers can design a more efficient workflow, add new features, or even automate the user's workflow. In this thesis, I propose novel latent variable models to understand, predict and eventually automate the user interaction with a software application. I start by analyzing users' clicks using time series models; I introduce models and inference algorithms for time series segmentation which are scalable to large-scale user datasets. Next, using a conditional variational autoencoder and some related models, I introduce a framework for automating the user interaction with a software application. I focus on photo enhancement applications, but this framework can be applied to any domain where segmentation, prediction and personalization is valuable. Finally, by combining sequential Monte Carlo and variational inference, I propose a new inference scheme which has better convergence properties than other reasonable baselines.by Ardavan Saeedi.Ph. D