Cancer survivors face unique mental health challenges, yet nearly half report unmet psychosocial needs. Smartphone interventions could help, but a major obstacle is knowing if, when, and how to intervene because inferring affective states with low-burden methods is hard. We test whether ultra-brief mobile diaries can infer contextual information approximating survivors’ affect, desire to regulate affect, and potential availability for brief digital behavioral interventions. Analyzing 24,183 entries from 407 survivors, administrative and health-related situations align with higher negative affect, whereas leisure/social situations align with higher positive affect. We introduce a Context-Aware LLM (CALLM) framework, which curates context via similarity-aligned peer cases and short personal trajectories, achieving balanced accuracy of 72.96% (positive affect), 73.29% (negative affect), 73.72% (regulation desire), and 60.09% (intervention availability), outperforming baselines. Post-hoc analyses show LLM confidence tracks accuracy, longer entries aid inference, and brief calibration improves personalization. Findings inform future just-in-time adaptive interventions for this underrepresented population.

Effective patient-provider communication is crucial in clinical care, directly impacting patient outcomes and quality of life. Traditional evaluation methods, such as human ratings, patient feedback, and provider self-assessments, are often limited by high costs and scalability issues. Although existing natural language processing (NLP) techniques show promise, they struggle with the nuances of clinical communication and require sensitive clinical data for training, reducing their effectiveness in real-world applications. Emerging large language models (LLMs) offer a new approach to assessing complex communication metrics, with the potential to advance the field through integration into passive sensing and just-in-time intervention systems. This study explores LLMs as evaluators of palliative care communication quality, leveraging their linguistic, in-context learning, and reasoning capabilities. Specifically, using simulated scripts crafted and labeled by healthcare professionals, we test proprietary models (e.g., GPT-4) and fine-tune open-source LLMs (e.g., LLaMA2) with a synthetic dataset generated by GPT-4 to evaluate clinical conversations, to identify key metrics such as “understanding” and “empathy.” Our findings demonstrated LLMs’ superior performance in evaluating clinical communication, providing actionable feedback with reasoning, and demonstrating the feasibility and practical viability of developing in-house LLMs. This research highlights LLMs’ potential to enhance patient-provider interactions and lays the groundwork for downstream steps in developing LLM-empowered clinical health systems.

Social anxiety is a common mental health condition linked to significant challenges in academic, social, and occupational functioning. A core feature is elevated momentary (state) anxiety in social situations, yet little prior work has measured or predicted fluctuations in this anxiety throughout the day. Capturing these intra-day dynamics is critical for designing realtime, personalized interventions such as Just-In-Time Adaptive Interventions (JITAIs). To address this gap, we conducted a study with socially anxious college students ($\mathrm{N}=91$; 72 after exclusions) using our custom smartwatch-based system over an average of 9.03 days (SD = 2.95). Participants received seven ecological momentary assessments (EMAs) per day to report state anxiety. We developed a base model on over 10,000 days of external heart rate data, transferred its representations to our dataset, and fine-tuned it to generate probabilistic predictions. These were combined with trait-level measures in a meta-learner. Our pipeline achieved 60.4% balanced accuracy in state anxiety detection in our dataset. To evaluate generalizability, we applied the training approach to a separate hold-out set from the TILES-18 dataset-the same dataset used for pretraining. On 10,095 once-daily EMAs, our method achieved 59.1% balanced accuracy, outperforming prior work by at least 7%.

Mobile sensing is ubiquitous and offers opportunities to gain insight into state mental health functioning. Detecting state elevations in social anxiety would be especially useful given this phenomenon is highly prevalent and impairing, but often not disclosed. In the present work, we explore the feasibility of detecting fluctuations in state social anxiety among N = 46 undergraduate students with elevated symptoms of trait social anxiety. Participants engaged in two dyadic and two group social interactions via Zoom. We evaluated participants' state anxiety levels as they anticipated, immediately after experiencing, and upon reflecting on each social interaction, spanning a time frame of 2–6 minutes. We collected biobehavioral features (i.e., PPG, EDA, skin temperature, and accelerometer) via Empatica E4 devices as they participated in the varied social contexts (e.g., dyadic vs. group; anticipating vs. experiencing the interaction; experiencing varying levels of social evaluation). We additionally measured their trait mental health functioning. Mixed-effect logistic regression and leave-one-subject-out machine learning modeling indicated biobehavioral features significantly predict state fluctuations in anxiety, though balanced accuracy tended to be modest (59%). However, our capacity to identify instances of heightened versus low state anxiety significantly increased (with balanced accuracy ranging from 69 % to 84 % across different operationalizations of state anxiety) when we integrated contextual data alongside trait mental health functioning into our predictive models. We discuss these and other findings in the context of the broader anxiety detection literature.

Correctly identifying an individual's social context from passively worn sensors holds promise for delivering just-in-time adaptive interventions (JITAIs) to treat social anxiety. In this study, we present results using passively collected data from a within-subjects experiment that assessed physiological responses across different social contexts (i.e., alone vs. with others), social phases (i.e., pre- and post-interaction vs. during an interaction), social interaction sizes (i.e., dyadic vs. group interactions), and levels of social threat (i.e., implicit vs. explicit social evaluation). Participants in the study ($N=46$) reported moderate to severe social anxiety symptoms as assessed by the Social Interaction Anxiety Scale ($\geq$34 out of 80). Univariate paired difference tests, multivariate random forest models, and cluster analyses were used to explore physiological response patterns across different social and non-social contexts. Our results suggest that social context is more reliably distinguishable than social phase, group size, or level of social threat, and that there is considerable variability in physiological response patterns even among distinguishable contexts. Implications for real-world context detection and future deployment of JITAIs are discussed.

During social interactions, understanding the in-tricacies of the context can be vital, particularly for socially anxious individuals. While previous research has found that the presence of a social interaction can be detected from ambient audio, the nuances within social contexts, which influence how anxiety provoking interactions are, remain largely unexplored. As an alternative to traditional, burdensome methods like self-report, this study presents a novel approach that harnesses ambient audio segments to detect social threat contexts. We focus on two key dimensions: number of interaction partners (dyadic vs. group) and degree of evaluative threat (explicitly evaluative vs. not explicitly evaluative). Building on data from a Zoom-based social interaction study (N=52 college students, of whom the majority N =45 are socially anxious), we employ deep learning methods to achieve strong detection performance. Under sample-wide 5-fold Cross Validation (CV), our model distinguished dyadic from group interactions with 90 % accuracy and detected evaluative threat at 83 %. Using a leave-one-group-out CV, accuracies were 82 % and 77 %, respectively. While our data are based on virtual interactions due to pandemic constraints, our method has the potential to extend to diverse real-world settings. This research underscores the potential of passive sensing and AI to differentiate intricate social contexts, and may ultimately advance the ability of context-aware digital interventions to offer personalized mental health support.

While audio data shows promise in addressing various health challenges, there is a lack of research on on-device audio processing for smartwatches. Privacy concerns make storing raw audio and performing post-hoc analysis undesirable for many users. Additionally, current on-device audio processing systems for smartwatches are limited in their feature extraction capabilities, restricting their potential for understanding user behavior and health. We developed a real-time system for on-device audio processing on smartwatches, which takes an average of 1.78 minutes (SD = 0.07 min) to extract 22 spectral and rhythmic features from a 1-minute audio sample, using a small window size of 25 milliseconds. Using these extracted audio features on a public dataset, we developed and incorporated models into a watch to classify foreground and background speech in real-time. Our Random Forest-based model classifies speech with a balanced accuracy of 80.3%.

Quality patient-provider communication is critical to improve clinical care and patient outcomes. While progress has been made with communication skills training for clinicians, significant gaps exist in how to best monitor, measure, and evaluate the implementation of communication skills in the actual clinical setting. Advancements in ubiquitous technology and natural language processing make it possible to realize more objective, real-time assessment of clinical interactions and in turn provide more timely feedback to clinicians about their communication effectiveness. In this paper, we propose CommSense, a computational sensing framework that combines smartwatch audio and transcripts with natural language processing methods to measure selected "best-practice'' communication metrics captured by wearable devices in the context of palliative care interactions, including understanding, empathy, presence, emotion, and clarity. We conducted a pilot study involving N=40 clinician participants, to test the technical feasibility and acceptability of CommSense in a simulated clinical setting. Our findings demonstrate that CommSense effectively captures most communication metrics and is well-received by both practicing clinicians and student trainees. Our study also highlights the potential for digital technology to enhance communication skills training for healthcare providers and students, ultimately resulting in more equitable delivery of healthcare and accessible, lower cost tools for training with the potential to improve patient outcomes.

Wearable devices with embedded sensors can provide personalized healthcare and wellness benefits in digital phenotyping and adaptive interventions. However, the collection, storage, and transmission of biometric data (including processed features rather than raw signals) from these devices pose significant privacy concerns. This quantitative, data-driven study examines the privacy risks associated with wearable-based digital phenotyping practices, with a focus on user reidentification (ReID), which is the process of identifying participants’ IDs from deidentified digital phenotyping datasets. We propose a machine-learning-based computational pipeline to evaluate and quantify model outcomes under various configurations, such as modality inclusion, window length, and feature type and format, to investigate the factors influencing ReID risks and their predictive trade-offs. This pipeline leverages features extracted from three wearable sensors, resulting in up to 68.43% accuracy in ReID risk for a sample size of N=45 socially anxious participants based on only descriptive features of 10-second observations. Additionally, we explore the trade-offs between privacy risks and predictive benefits by adjusting various settings (e.g., the ways to process extracted features). Our findings highlight the importance of privacy in digital phenotyping and suggest potential future directions.

Wearable smart devices are capable of capturing a variety of information from their users using a multitude of noninvasive sensing modalities. Using features from the raw measurements of wearable devices, sensor fusion enables us to obtain a holistic picture of the users’ context and monitor their activity state with increased accuracy. Human activity recognition using noninvasive sensors allows us to capture the natural behavior of users in their day-to-day lives. This in-the-wild activity recognition, however, poses several key challenges that must be addressed to create effective classification models. The main challenges are class imbalance, uncertainty in classifier decisions, and large feature spaces. To address them, this study further explores a probabilistic sensor fusion method called Naive Adaptive Probabilistic Sensor (NAPS) Fusion. In doing so, we establish the viability of NAPS Fusion for natural human activity recognition using noninvasive sensing modalities. NAPS Fusion handles dimensionality reduction by creating reduced feature sets and mitigates the class imbalance issue through the use of Synthetic Minority Oversampling Technique (SMOTE). Moreover, NAPS Fusion addresses uncertainty in the decisions of classifiers using a Dempster-Shafer theoretic late fusion framework. Our empirical evaluation demonstrates that NAPS Fusion has broad applications beyond its original design for cognitive state detection. It outperforms similar decision level sensor fusion methods (late fusion using averaging, LFA, and late fusion using learned weights, LFL) in the detection of exercise and sedentary activities such as walking, running, lying down, and sitting. We observe improvements of up to 56% in F1 score and up to 59% in precision with NAPS Fusion over the compared methods.

The Internet of Things (IoT) boom has revolutionized almost every corner of people’s daily lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technology, IoT artifacts, including smart wearables, cameras, smartwatches, and autonomous systems can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph neural networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source codes from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT.

During pandemics, effective interventions require monitoring the problem at different scales and understanding the various tradeoffs between efficacy, privacy, and economic burden. To address these challenges, we propose a framework where we perform Bayesian change-point analysis on aggregate behavior markers extracted from mobile sensing data collected during the COVID-19 pandemic. Results generated by 598 participants for up to four months reveal rich insights: We observe an increase in smartphone usage around February 10th, followed by an increase in email usage around February 27th and, finally, a large reduction in participant’s mobility around March 13th. These behavior changes overlapped with important news events and government directives such as the naming of COVID-19, a spike in the number of reported cases in Europe, and the declaration of national emergency by President Trump. We also show that our detected change points align with changes in large scale external sources, including number of COVID-19 tweets, COVID-19 search traffic, and a large-scale foot traffic data collected by SafeGraph, providing further validation of our method. Our results show promise towards the feasibility of using mobile sensing to understand communities’ responses to public health interventions.

Mobile sensing systems have been widely used as a practical approach to collect behavioral and health-related information from individuals and to provide timely intervention to promote health and well being, such as mental health and chronic care. As the objectives of mobile sensing could be eitherpersonalized medicine for individualsorpublic health for populations, in this work, we review the design of these mobile sensing systems, and propose to categorize the design of these systems in two paradigms—1)personal sensingand 2)crowdsensingparadigms. While both sensing paradigms might incorporate common ubiquitous sensing technologies, such aswearable sensors,mobility monitoring,mobile data offloading, andcloud-based data analyticsto collect and process sensing data from individuals, we present two novel taxonomy systems based on the: 1)sensing objectives(e.g., goals of mobile health (mHealth) sensing systems and how technologies achieve the goals) and 2)the sensing systems design and implementation (D&I)(e.g., designs of mHealth sensing systems and how technologies are implemented). With respect to the two paradigms and two taxonomy systems, this work systematically reviews this field. Specifically, we first present technical reviews on the mHealth sensing systems in eight common/popular healthcare issues, ranging from depression and anxiety to COVID-19. By summarizing the mHealth sensing systems, we comprehensively survey the research works using the two taxonomy systems, where we systematically review thesensing objectivesandsensing systems D&Iwhile mapping the related research works onto the life-cycles of mHealth Sensing, i.e.: 1)sensing task creation and participation; 2)(health surveillance and data collection; and 3)data analysis and knowledge discovery. In addition to summarization, the proposed taxonomy systems also help the potential directions of mobile sensing for health from both personalized medicine and population health perspectives. Finally, we attempt to test and discuss the validity of our scientific approaches to the survey.

Inspired by human visual attention, we propose a novel inverse reinforcement learning formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for predicting the visual attention of drivers in accident-prone situations. MEDIRL predicts fixation locations that lead to maximal rewards by learning a task-sensitive reward function from eye fixation patterns recorded from attentive drivers. Additionally, we introduce EyeCar, a new driver attention dataset in accident-prone situations. We conduct comprehensive experiments to evaluate our proposed model on three common benchmarks: (DR(eye)VE, BDD-A, DADA-2000), and our EyeCar dataset. Results indicate that MEDIRL outperforms existing models for predicting attention and achieves state-of-the-art performance. We present extensive ablation studies to provide more insights into different features of our proposed model.1

Mental health disorders, such as generalized anxiety disorder and depression, are prevalent in modern society. Early detection of mental illness is essential to minimize the negative consequences of long-term psychological discomfort and behavioral dysfunction. As a diverse set of embedded sensors in smart mobile devices becomes commonplace, passively and continuously collected mobile sensing data are increasingly being used to develop machine learning based tools for early-stage disease diagnosis. In the training process of machine learning models, self-reported results from ecological momentary assessments (EMAs) are usually employed to provide supervisions. However, complete responses of these high-frequency surveys in the wild are impractical due to heavy user burden and low user engagement. Without the availability of EMA responses in low level of label granularity, the annotations in the high level can only provide weak supervisions. To leverage the vast majority of unannotated data in different levels of granularity, in this paper, we propose an end-to-end graph neural network algorithm called semi-supervised Graph Instance Transformer (SS-GIT) based on multiple instance learning and contrastive self-supervised learning to predict early signs of generalized anxiety disorder and depression under the weak supervisions. Using a mobile sensing dataset that we collected from around 1,300 participants in the wild, our empirical results demonstrate improved performance when compared to the existing state-of-the-art baseline graph neural networks for mental health inference. On average, our proposed model outperforms the best baseline model by 8.8% on Fl-score, 6.7% on ROC-AUC, and 7.2% on PR-AUC.

Chronic kidney disease (CKD) is a gradual loss of renal function over time, and it increases the risk of mortality, decreased quality of life, as well as serious complications. The prevalence of CKD has been increasing in the last couple of decades, which is partly due to the increased prevalence of diabetes and hypertension. To accurately detect CKD in diabetic patients, we propose a novel framework to learn sparse longitudinal representations of patients' medical records. The proposed method is also compared with widely used baselines such as Aggregated Frequency Vector and Bag-of-Pattern in Sequences on real EHR data, and the experimental results indicate that the proposed model achieves higher predictive performance. Additionally, the learned representations are interpreted and visualized to bring clinical insights.

Delivering treatment recommendations via pervasive electronic devices such as mobile phones has the potential to be a viable and scalable treatment medium for long-term health behavior management. But active experimentation of treatment options can be time-consuming, expensive and altogether unethical in some cases. There is a growing interest in methodological approaches that allow an experimenter to learn and evaluate the usefulness of a new treatment strategy before deployment. We present the first development of a treatment recommender system for emotion regulation using real-world historical mobile digital data from n = 114 high socially anxious participants to test the usefulness of new emotion regulation strategies. We explore a number of offline contextual bandits estimators for learning and propose a general framework for learning algorithms. Our experimentation shows that the proposed doubly robust offline learning algorithms performed significantly better than baseline approaches, suggesting that this type of recommender algorithm could improve emotion regulation. Given that emotion regulation is impaired across many mental illnesses and such a recommender algorithm could be scaled up easily, this approach holds potential to increase access to treatment for many people. We also share some insights that allow us to translate contextual bandit models to this complex real-world data, including which contextual features appear to be most important for predicting emotion regulation strategy effectiveness.

Over 35% of the world's population uses social media. Platforms like Facebook, Twitter, and Instagram have radically influenced the way individuals interact and communicate. These platforms facilitate both public and private communication with strangers and friends alike, providing rich insight into an individual's personality, health, and wellbeing. To date, many researchers have employed a variety of methods for extracting mental health-centric features from digital text communication (DTC) data, including natural language processing, social network analysis, and extraction of temporal discourse patterns. However, none have explored a hierarchical framework for extracting features from private messages with the goal of unifying approaches across methodological domains. Furthermore, while analyses of large, public corpora abound in existing literature, limited work has been done to explore the relationship between of private textual communications, personality traits, and symptoms of mental illness. We present a framework for constructing rich feature spaces from digital text communications. We then demonstrate the efficacy of our framework by applying it to a dataset of private Facebook messages in a college student population (N=103). Our results reveal key individual differences in temporal and relational behaviors, as well as language usage in relation to validated measures of trait-level anxiety, loneliness, and personality. This work represents a critical step forward in linking features of private social media messages to validated measures of mental health, wellbeing, and personality.

Continuous passive sensing using smartphone embedded sensors can drain the battery quickly, interrupting other usages of the device. In order to improve the energy efficiency in continuous mobile sensing applications, we propose a new adaptive sensing framework using reinforcement learning to optimize the sensing timing. We model our adaptive sensing problem as a Markov Decision Process and dynamically change the sensing timing of targeted sensor(s) so that they are only operating in desired contexts (e.g. collect accelerometer data only when the phone is moving). Using accelerometer data continuously collected from 220 participants for over two weeks, we show that our approach is able to save the energy while attaining high accuracy and data coverage. Specifically, our strategy attains energy saving of 62.4 % at an accuracy of 80.9% and data coverage of 67.4%, which outperforms two baseline strategies, including a random strategy and a strategy using learning automata technique.

Suicide is the second leading cause of death among young adults but the challenges of preventing suicide are significant because the signs often seem invisible. Research has shown that clinicians are not able to reliably predict when someone is at greatest risk. In this paper, we describe the design, collection, and analysis of text messages from individuals with a history of suicidal thoughts and behaviors to build a model to identify periods of suicidality (i.e., suicidal ideation and non-fatal suicide attempts). By reconstructing the timeline of recent suicidal behaviors through a retrospective clinical interview, this study utilizes a prospective research design to understand if text communications can predict periods of suicidality versus depression. Identifying subtle clues in communication indicating when someone is at heightened risk of a suicide attempt may allow for more effective prevention of suicide.

Automatic understanding of domain specific texts in order to extract useful relationships for later use is a non-trivial task. One such relationship would be between railroad accidents' causes and their correspondent descriptions in reports. From 2001 to 2016 rail accidents in the U.S. cost more than $4.6B. Railroads involved in accidents are required to submit an accident report to the Federal Railroad Administration (FRA). These reports contain a variety of fixed field entries including primary cause of the accidents (a coded variable with 389 values) as well as a narrative field which is a short text description of the accident. Although these narratives provide more information than a fixed field entry, the terminologies used in these reports are not easy to understand by a non-expert reader. Therefore, providing an assisting method to fill in the primary cause from such domain specific texts (narratives) would help to label the accidents with more accuracy. Another important question for transportation safety is whether the reported accident cause is consistent with narrative description. To address these questions, we applied deep learning methods together with powerful word embeddings such as Word2Vec and GloVe to classify accident cause values for the primary cause field using the text in the narratives. The results show that such approaches can both accurately classify accident causes based on report narratives and find important inconsistencies in accident reporting.

Increasingly large document collections require improved information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which has become an important application for supervised learning. Recently the performance of traditional supervised classifiers has degraded as the number of documents has increased. This is because along with growth in the number of documents has come an increase in the number of categories. This paper approaches this problem differently from current document classification methods that view the problem as multi-class classification. Instead we perform hierarchical classification using an approach we call Hierarchical Deep Learning for Text classification (HDLTex). HDLTex employs stacks of deep learning architectures to provide specialized understanding at each level of the document hierarchy.

Electronic health records (EHR) provide a rich source of temporal data that present a unique opportunity to characterize disease patterns and risk of imminent disease. While many data-mining tools have been adopted for EHR-based disease early detection, linear discriminant analysis (LDA) is one of the most commonly used statistical methods. However, it is difficult to train an accurate LDA model for early disease diagnosis when too few patients are known to have the target disease. Furthermore, EHR data are heterogeneous with significant noise. In such cases, the covariance matrices used in LDA are usually singular and estimated with a large variance. This article presents Daehr , an extension of the LDA framework using electronic health record data to address these issues. Beyond existing LDA analyzers, we propose Daehr to (1) eliminate the data noise caused by the manual encoding of EHR data and (2) lower the variance of parameter (covariance matrices) estimation for LDA models when only a few patients’ EHR are available for training. To achieve these two goals, we designed an iterative algorithm to improve the covariance matrix estimation with embedded data-noise/parameter-variance reduction for LDA. We evaluated Daehr extensively using the College Health Surveillance Network, a large, real-world EHR dataset. Specifically, our experiments compared the performance of LDA to three baselines (i.e., LDA and its derivatives) in identifying college students at high risk for mental health disorders from 23 U.S. universities. Experimental results demonstrate Daehr significantly outperforms the three baselines by achieving 1.4%--19.4% higher accuracy and a 7.5%--43.5% higher F1-score.

Mental health problems are highly prevalent and appear to be increasing in frequency and severity among the college student population. The upsurge in mobile and wearable wireless technologies capable of intense, longitudinal tracking of individuals, provide valuable opportunities to examine temporal patterns and dynamic interactions of key variables in mental health research. In this paper, we present a feasibility study leveraging non-invasive mobile sensing technology to passively assess college students' social anxiety, one of the most common disorders in the college student population. We have first developed a smartphone application to continuously track GPS locations of college students, then we built an analytic infrastructure to collect the GPS trajectories and finally we analyzed student behaviors (e.g. studying or staying at home) using Point-Of-Interest (POI). The whole framework supports intense, longitudinal, dynamic tracking of college students to evaluate how their anxiety and behaviors change in the college campus environment. The collected data provides critical information about how students' social anxiety levels and their mobility patterns are correlated. Our primary analysis based on 18 college students demonstrated that social anxiety level is significantly correlated with places students' visited and location transitions.

The burden of entry into mobile crowdsensing (MCS) is prohibitively high for human-subject researchers who lack a technical orientation. As a result, the benefits of MCS remain beyond the reach of research communities (e.g., psychologists) whose expertise in the study of human behavior might advance applications and understanding of MCS systems. This paper presents Sensus, a new MCS system for human-subject studies that bridges the gap between human-subject researchers and MCS methods. Sensus alleviates technical burdens with on-device, GUI-based design of sensing plans, simple and efficient distribution of sensing plans to study participants, and uniform participant experience across iOS and Android devices. Sensing plans support many hardware and software sensors, automatic deployment of sensor-triggered surveys, and double-blind assignment of participants within randomized controlled trials. Sensus offers these features to study designers without requiring knowledge of markup and programming languages. We demonstrate the feasibility of using Sensus within two human-subject studies, one in psychology and one in engineering. Feedback from non-technical users indicates that Sensus is an effective and low-burden system for MCS-based data collection and analysis.

This paper first defines a novel spatial-temporal coverage metric, k-depth coverage, for mobile crowdsensing (MCS) problems. This metric considers both the fraction of subareas covered by sensor readings and the number of sensor readings collected in each covered subarea. Then iCrowd, a generic MCS task allocation framework operating with the energy-efficient Piggyback Crowdsensing task model, is proposed to optimize the MCS task allocation with different incentives and k-depth coverage objectives/ constraints. iCrowd first predicts the call and mobility of mobile users based on their historical records, then it selects a set of users in each sensing cycle for sensing task participation, so that the resulting solution achieves two dual optimal MCS data collection goals-i.e., Goal. 1 near-maximal k-depth coverage without exceeding a given incentive budget or Goal. 2 near-minimal incentive payment while meeting a predefined k-depth coverage goal. We evaluated iCrowd extensively using a large-scale real-world dataset for these two data collection goals. The results show that: for Goal.1, iCrowd significantly outperformed three baseline approaches by achieving 3-60 percent higher k-depth coverage; for Goal.2, iCrowd required 10.0-73.5 percent less incentives compared to three baselines under the same k-depth coverage constraint.

Currently, a large amount of data is amassed in electronic health records (EHRs). However, EHR systems are largely information silos, that is, uses of these systems are often confined to management of patient information and analytics specific to a clinician's practice. A growing trend in healthcare is combining multiple databases to support epidemiological research. The College Health Surveillance Network is the first national data warehouse containing EHR data from 31 different student health centers. Each member university contributes to the data warehouse by uploading select EHR data including patient demographics, diagnoses, and procedures to a common server on a monthly basis. In this paper, we focus on the data quality dimensions from a subsample of the data comprised of over 5.7 million patient visits for approximately 980,000 patients with 4,465 unique diagnoses from 23 of those universities. We examine the data for measures of completeness, consistency, and availability for secondary use for epidemiological research. Additionally, clinical documentation practices and EHR vendor were evaluated as potential contributors to data quality. We found that overall about 70% of the data in the data warehouse is available for secondary use, and identified clinical documentation practices that are correlated to a reduction in data quality. This suggests that automated quality control and proactive clinical documentation support could reduce ad-hoc data cleaning needs resulting in greater data availability for secondary use.

According to a 2014 Spring American College Health Association Survey, almost 50% of college students reported feeling things were hopeless and that it was difficult to function within the last 12 months. More than 80% reported feeling overwhelmed and exhausted by their responsibilities. This critical subpopulation of Americans is facing significant levels of mental health disorders, challenging colleges to provide accessible and high quality behavioral health care. However, psychiatric disorders are frequently unrecognized in primary care settings, posing physical, emotional, economic, and social burdens to patients and others. Towards the goal of earlier identification and treatment of mental health disorders, this paper proposes M-SEQ, an early detection framework for anxiety/depression using electronic health data from primary care visit sequences. Specifically, compared to existing methods that predict a future disease state using frequency of diagnoses in a patient's medical history, we hypothesize that future disease might also be correlated with the temporal orders of diagnoses. Thus, M-SEQ first discovers a set of diagnosis codes that are discriminative of anxiety/depression, and then extracts each diagnosis pair from each patient's health record to represent the temporal orders of diagnoses. Further, it incorporates the extracted temporal order information with the existing representation to predict whether a patient is at risk of anxiety/depression. We evaluate M-SEQ using the electronic health record (EHR) data of 213,112 college students from 10 schools participating in the College Health Surveillance Network (CHSN) from January 1, 2011 through December 31, 2014. The experimental results shows that our framework can detect a future diagnosis of anxiety and depression based on the primary care visit data up to 3 months in advance, with approximately 1%-4.5% higher accuracy, compared to baseline methods using frequency of diagnoses.

In this paper, we present algorithms and display concepts that allow Soldiers to efficiently interact with a robotic swarm that is participating in a representative convoy mission. A critical aspect of swarm control, especially in disrupted or degraded conditions, is Soldier-swarm interaction-the Soldier must be kept cognizant of swarm operations through an interface that allows him or her to monitor status and/or institute corrective actions. We provide a control method for the swarm that adapts easily to changing battlefield conditions, metrics and supervisory algorithms that enable swarm members to economically monitor changes in swarm status as they execute the mission, and display concepts that can efficiently and effectively communicate swarm status to Soldiers in challenging battlefield environments.

In this paper, we present a strategy for organizing swarms of unmanned vehicles into a formation by utilizing artificial potential fields that were generated from normal and sigmoid functions. These functions construct the surface on which swarm members travel, controlling the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables that force the swarm to behave according to set constraints, formation, and member spacing. The artificial potential functions and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation and user-defined constraints. This approach is computationally efficient and scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models. Simulation results are presented for a swarm of 10 and 40 robots that follow circle, ellipse, and wedge formations. Experimental results are included to demonstrate the applicability of the approach on a swarm of four custom-built unmanned ground vehicles (UGVs).

This work addresses the problem of coordinating a swarm of unmanned ground vehicles with an unmanned aerial vehicle (UAV). The UAV is utilized as a leader robot that is intelligently followed by a coordinated group of unmanned ground vehicles (UGVs). The UAV is a completely autonomous agent that is controlled using Sugueno fuzzy logic. The UGVs are organized into formation utilizing artificial potential fields generated from normal and sigmoid functions. These functions are built around the location of the UAV and ultimately construct the surface that swarm members travel on, which inherently controls the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables forcing the swarm to behave according to set constraints, formation and member spacing. The swarm function and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation as well as user defined constraints. Simulations demonstrate the precision of the approach with up to forty UGVs. Experimental results are presented using a fully autonomous swarm of three UGVs and a single UAV helicopter for coordination.

This study investigates data collected from operating an Inuktun robot in an urban search and rescue (USAR) confined space training exercise task at Virginia Beach Training Center. Data was collected from coding approximately one hour of video. The video had no sound so all analysis is based on the video feed. Indicators of communication, gestures, physical interactions with the robot, and robot movements were analyzed. The findings indicate that the robot emerges as a virtual presence for the support of the team outside of the confined space. The team members spontaneously responded socially to the robot despite the robot not being engineered to have a social intelligence. This confirms numerous studies in the cognitive science, psychology, and affective computing literature that robots need a social interface regards of domain.