Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities

There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users' level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network. We evaluate our approach on the open-source StudentLife dataset. Moreover, we discuss the challenges and trade-offs involved in building machine learning models for digital mental health and highlight potential future work in this direction.Comment: 6 pages, 2 figures, In Proceedings of the NIPS Workshop on Machine Learning for Healthcare 2017 (ML4H 2017). Colocated with NIPS 201

What does touch tell us about emotions in touchscreen-based gameplay?

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACM. It is posted here by permission of ACM for your personal use. Not for redistribution.Nowadays, more and more people play games on touch-screen mobile phones. This phenomenon raises a very interesting question: does touch behaviour reflect the player’s emotional state? If possible, this would not only be a valuable evaluation indicator for game designers, but also for real-time personalization of the game experience. Psychology studies on acted touch behaviour show the existence of discriminative affective profiles. In this paper, finger-stroke features during gameplay on an iPod were extracted and their discriminative power analysed. Based on touch-behaviour, machine learning algorithms were used to build systems for automatically discriminating between four emotional states (Excited, Relaxed, Frustrated, Bored), two levels of arousal and two levels of valence. The results were very interesting reaching between 69% and 77% of correct discrimination between the four emotional states. Higher results (~89%) were obtained for discriminating between two levels of arousal and two levels of valence

Logging Stress and Anxiety Using a Gamified Mobile-based EMA Application, and Emotion Recognition Using a Personalized Machine Learning Approach

According to American Psychological Association (APA) more than 9 in 10 (94 percent) adults believe that stress can contribute to the development of major health problems, such as heart disease, depression, and obesity. Due to the subjective nature of stress, and anxiety, it has been demanding to measure these psychological issues accurately by only relying on objective means. In recent years, researchers have increasingly utilized computer vision techniques and machine learning algorithms to develop scalable and accessible solutions for remote mental health monitoring via web and mobile applications. To further enhance accuracy in the field of digital health and precision diagnostics, there is a need for personalized machine-learning approaches that focus on recognizing mental states based on individual characteristics, rather than relying solely on general-purpose solutions. This thesis focuses on conducting experiments aimed at recognizing and assessing levels of stress and anxiety in participants. In the initial phase of the study, a mobile application with broad applicability (compatible with both Android and iPhone platforms) is introduced (we called it STAND). This application serves the purpose of Ecological Momentary Assessment (EMA). Participants receive daily notifications through this smartphone-based app, which redirects them to a screen consisting of three components. These components include a question that prompts participants to indicate their current levels of stress and anxiety, a rating scale ranging from 1 to 10 for quantifying their response, and the ability to capture a selfie. The responses to the stress and anxiety questions, along with the corresponding selfie photographs, are then analyzed on an individual basis. This analysis focuses on exploring the relationships between self-reported stress and anxiety levels and potential facial expressions indicative of stress and anxiety, eye features such as pupil size variation and eye closure, and specific action units (AUs) observed in the frames over time. In addition to its primary functions, the mobile app also gathers sensor data, including accelerometer and gyroscope readings, on a daily basis. This data holds potential for further analysis related to stress and anxiety. Furthermore, apart from capturing selfie photographs, participants have the option to upload video recordings of themselves while engaging in two neuropsychological games. These recorded videos are then subjected to analysis in order to extract pertinent features that can be utilized for binary classification of stress and anxiety (i.e., stress and anxiety recognition). The participants that will be selected for this phase are students aged between 18 and 38, who have received recent clinical diagnoses indicating specific stress and anxiety levels. In order to enhance user engagement in the intervention, gamified elements - an emerging trend to influence user behavior and lifestyle - has been utilized. Incorporating gamified elements into non-game contexts (e.g., health-related) has gained overwhelming popularity during the last few years which has made the interventions more delightful, engaging, and motivating. In the subsequent phase of this research, we conducted an AI experiment employing a personalized machine learning approach to perform emotion recognition on an established dataset called Emognition. This experiment served as a simulation of the future analysis that will be conducted as part of a more comprehensive study focusing on stress and anxiety recognition. The outcomes of the emotion recognition experiment in this study highlight the effectiveness of personalized machine learning techniques and bear significance for the development of future diagnostic endeavors. For training purposes, we selected three models, namely KNN, Random Forest, and MLP. The preliminary performance accuracy results for the experiment were 93%, 95%, and 87% respectively for these models

Gender differences in liking and wanting sex: examining the role of motivational context and implicit versus explicit processing

The present study investigated the specificity of sexual appraisal processes by making a distinction between implicit and explicit appraisals and between the affective (liking) and motivational (wanting) valence of sexual stimuli. These appraisals are assumed to diverge between men and women, depending on the context in which the sexual stimulus is encountered. Using an Implicit Association Test, explicit ratings, and film clips to prime a sexual, romantic or neutral motivational context, we investigated whether liking and wanting of sexual stimuli differed at the implicit and explicit level, differed between men and women, and were differentially sensitive to context manipulations. Results showed that, at the implicit level, women wanted more sex after being primed with romantic mood whereas men showed the least wanting of sex in the romantic condition. At the explicit level, men reported greater liking and wanting of sex than women, independently of context. We also found that women's (self-reported) sexual behavior was best predicted by the incentive salience of sexual stimuli whereas men's sexual behavior was more closely related to the hedonic qualities of sexual stimuli. Results were discussed in relation to an emotion-motivational account of sexual functioning

Improving School Improvement

PREFACEIn opening this volume, you might be thinking:Is another book on school improvement really needed?Clearly our answer is yes. Our analyses of prevailing school improvement legislation, planning, and literature indicates fundamental deficiencies, especially with respect to enhancing equity of opportunity and closing the achievement gap.Here is what our work uniquely brings to policy and planning tables:(1) An expanded framework for school improvement – We highlight that moving from a two- to a three-component policy and practice framework is essential for closing the opportunity and achievement gaps. (That is, expanding from focusing primarily on instruction and management/government concerns by establishing a third primary component to improve how schools address barriers to learning and teaching.)(2) An emphasis on integrating a deep understanding of motivation – We underscore that concerns about engagement, management of behavior, school climate, equity of opportunity, and student outcomes require an up-to-date grasp of motivation and especially intrinsic motivation.(3) Clarification of the nature and scope of personalized teaching – We define personalization as the process of matching learner motivation and capabilities and stress that it is the learner's perception that determines whether the match is a good one.(4) A reframing of remediation and special education – We formulate these processes as personalized special assistance that is applied in and out of classrooms and practiced in a sequential and hierarchical manner.(5) A prototype for transforming student and learning supports – We provide a framework for a unified, comprehensive, and equitable system designed to address barriers to learning and teaching and re-engage disconnected students and families.(6) A reworking of the leadership structure for whole school improvement --We outline how the operational infrastructure can and must be realigned in keeping with a three component school improvement framework.(7) A systemic approach to enhancing school-community collaboration – We delineate a leadership role for schools in outreaching to communities in order to work on shared concerns through a formal collaborative operational infrastructure that enables weaving together resources to advance the work.(8) An expanded framework for school accountability – We reframe school accountability to ensure a balanced approach that accounts for a shift to a three component school improvement policy.(9) Guidance for substantive, scalable, and sustainable systemic changes –We frame mechanisms and discuss lessons learned related to facilitating fundamental systemic changes and replicating and sustaining them across a district.The frameworks and practices presented are based on our many years of work in schools and from efforts to enhance school-community collaboration. We incorporate insights from various theories and the large body of relevant research and from lessons learned and shared by many school leaders and staff who strive everyday to do their best for children.Our emphasis on new directions in no way is meant to demean current efforts. We know that the demands placed on those working in schools go well beyond what anyone should be asked to do. Given the current working conditions in many schools, our intent is to help make the hard work generate better results. To this end, we highlight new directions and systemic pathways for improving school outcomes.Some of what we propose is difficult to accomplish. Hopefully, the fact that there are schools, districts, and state agencies already trailblazing the way will engender a sense of hope and encouragement to those committed to innovation.It will be obvious that our work owes much to many. We are especially grateful to those who are pioneering major systemic changes across the country. These leaders and so many in the field have generously offered their insights and wisdom. And, of course, we are indebted to hundreds of scholars whose research and writing is a shared treasure. As always, we take this opportunity to thank Perry Nelson and the host of graduate and undergraduate students at UCLA who contribute so much to our work each day, and to the many young people and their families who continue to teach us all.Respectfully submitted for your consideration,Howard Adelman & Linda Taylo

Unsupervised routine discovery in egocentric photo-streams

The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person's health. In this work, we address the recognition of routine related days. To do so, we rely on egocentric images, which are recorded by a wearable camera and allow to monitor the life of the user from a first-person view perspective. We propose an unsupervised model that identifies routine related days, following an outlier detection approach. We test the proposed framework over a total of 72 days in the form of photo-streams covering around 2 weeks of the life of 5 different camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted F-Score for all the users. Thus, we show that our framework is able to recognise routine related days and opens the door to the understanding of the behaviour of people

Inferring Mood-While-Eating with Smartphone Sensing and Community-Based Model Personalization

The interplay between mood and eating has been the subject of extensive research within the fields of nutrition and behavioral science, indicating a strong connection between the two. Further, phone sensor data have been used to characterize both eating behavior and mood, independently, in the context of mobile food diaries and mobile health applications. However, limitations within the current body of literature include: i) the lack of investigation around the generalization of mood inference models trained with passive sensor data from a range of everyday life situations, to specific contexts such as eating, ii) no prior studies that use sensor data to study the intersection of mood and eating, and iii) the inadequate examination of model personalization techniques within limited label settings, as we commonly experience in mood inference. In this study, we sought to examine everyday eating behavior and mood using two datasets of college students in Mexico (N_mex = 84, 1843 mood-while-eating reports) and eight countries (N_mul = 678, 329K mood reports incl. 24K mood-while-eating reports), containing both passive smartphone sensing and self-report data. Our results indicate that generic mood inference models decline in performance in certain contexts, such as when eating. Additionally, we found that population-level (non-personalized) and hybrid (partially personalized) modeling techniques were inadequate for the commonly used three-class mood inference task (positive, neutral, negative). Furthermore, we found that user-level modeling was challenging for the majority of participants due to a lack of sufficient labels and data from the negative class. To address these limitations, we employed a novel community-based approach for personalization by building models with data from a set of similar users to a target user