• Corpus ID: 56895290

Extraction of Behavioral Features from Smartphone and Wearable Data

  title={Extraction of Behavioral Features from Smartphone and Wearable Data},
  author={Afsaneh Doryab and Prerna Chikarsel and Xinwen Liu and Anind K. Dey},
The rich set of sensors in smartphones and wearable devices provides the possibility to passively collect streams of data in the wild. The raw data streams, however, can rarely be directly used in the modeling pipeline. We provide a generic framework that can process raw data streams and extract useful features related to non-verbal human behavior. This framework can be used by researchers in the field who are interested in processing data from smartphones and Wearable devices. 

Outliers in Smartphone Sensor Data Reveal Outliers in Daily Happiness

This paper proposes a new approach that advances emotional state prediction by extracting outlier-based features and by mitigating the subjectivity in capturing ground-truth information and demonstrates prediction performance improvements of up to 13% in AUC and 27% in F-score compared to the traditional modelling approaches.

A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams

This evaluation demonstrates the framework’s ability to generate new knowledge and findings through rigorous micro- and macro-level modeling of human rhythms from mobile and wearable data streams collected in the wild and using them to assess and predict different life and health outcomes.

Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

Passive sensing has the potential for detecting loneliness in college students and identifying the associated behavioral patterns and these findings highlight intervention opportunities through mobile technology to reduce the impact of loneliness on individuals’ health and well-being.

Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices

An overview of Reproducible Analysis Pipeline for Data Streams (RAPIDS), a reproducible pipeline to standardize the preprocessing, feature extraction, analysis, visualization, and reporting of data streams coming from mobile sensors is provided.

Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: 3-Fold Analysis

The mining of associations between classifier-selected behavioral features and loneliness indicated that more activity and less sedentary behavior, especially in the evening, was associated with a decrease in levels of loneliness from the beginning of the semester to the end of it.

CoRhythMo: A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams

Evaluation of CoRhythMo uses a dataset of smartphone and Fitbit data collected from 138 college students over a semester to evaluate the framework’s ability to model biobehavioral rhythms of students, measure the stability of their rhythms over the course of the semester, and predict the mental health status in students using the model of theirBiobeh behavioral rhythms.

A Decade of Ubiquitous Computing Research in Mental Health

A decade of research into technologies for mental health, focusing on the use of mobile and wearable technology is reviewed, finding 46 systems that are analyzed in a historical context and discussed according to which mental disorder they target, the type of technology, and the type and size of clinical studies they have been used in.

Prediction of Hospital Readmission from Longitudinal Mobile Data Streams

This research addresses the challenge of daily readmission risk prediction after the hospital discharge via leveraging the abilities of mobile data streams collected from patients devices in a probabilistic deep learning framework.

Digital health tools for the passive monitoring of depression: a systematic review of methods

Improvements in reporting standards are recommended including consideration of generalisability and reproducibility, such as wider diversity of samples, thorough reporting methodology and the reporting of potential bias in studies with numerous features.

Continuous Digital Assessment for Weight Loss Surgery Patients

It is demonstrated that consumer-grade activity trackers can capture behavioral and physiological changes resulting from weight loss surgery and these devices have the potential to be used to develop measures of patients’ postoperative recovery that are convenient, sensitive, scalable, individualized, and continuous.



Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis

A smartphone application is developed that periodically collects the locations of the users and the answers to daily questionnaires that quantify their depressive mood and the design of models that are able to successfully predict changes in the depressive mood of individuals by analyzing their movements are presented.

Toss 'n' turn: smartphone as sleep and sleep quality detector

The rapid adoption of smartphones along with a growing habit for using these devices as alarm clocks presents an opportunity to use this device as a sleep detector, and individual models performed better than generally trained models on detecting sleep and sleep quality.

The relationship between mobile phone location sensor data and depressive symptom severity

The finding that GPS features predict depressive symptom severity up to 10 weeks prior to assessment suggests that GPS Features may have the potential as early warning signals of depression.

Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study

The detection of daily-life behavioral markers using mobile phone global positioning systems and usage sensors and their use in identifying depressive symptom severity suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach.

SmartGPA: how smartphones can assess and predict academic performance of college students

It is shown that there are a number of important behavioral factors automatically inferred from smartphones that significantly correlate with term and cumulative GPA, including time series analysis of activity, conversational interaction, mobility, class attendance, studying, and partying.

AWARE: Mobile Context Instrumentation Framework

It is demonstrated how AWARE can mitigate researchers’ effort when building mobile data-logging tools and context-aware applications, with minimal battery impact.

Detection of Behavior Change in People with Depression

The design of Big Black Dog, a smartphone-based system for gathering data about social and sleep behaviors and the results of a pilot evaluation to understand the feasibility of gathering and using data from smartphones for inferring the onset of depression are reported on.

Using passively collected sedentary behavior to predict hospital readmission

This work evaluates the ability of behavioral risk factors, specifically Fitbit-assessed behavior, to predict readmission for 25 postsurgical cancer inpatients, and shows that sum of steps, maximum sedentary bouts, frequency, and low breaks in sedentary times during waking hours are strong predictors of readmission.

A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.

Fast algorithm for spectral analysis of unevenly sampled data

On montre que les transformations de Fourier rapides peuvent servir pour faire des calculs sur ordinateur de l'ordre de 10 2 N Log N