Abstract
Smartphone and wearable devices are widely used in behavioral and clinical research to collect longitudinal data that, along with ground truth data, are used to create models of human behavior. Mobile sensing researchers often program data processing and analysis code from scratch even though many research teams collect data from similar mobile sensors, platforms, and devices. This leads to significant inefficiency in not being able to replicate and build on others' work, inconsistency in quality of code and results, and lack of transparency when code is not shared alongside publications. We provide 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. RAPIDS is formed by a group of R and Python scripts that are executed on top of reproducible virtual environments, orchestrated by a workflow management system, and organized following a consistent file structure for data science projects. We share open source, documented, extensible and tested code to preprocess, extract, and visualize behavioral features from data collected with any Android or iOS smartphone sensing app as well as Fitbit and Empatica wearable devices. RAPIDS allows researchers to process mobile sensor data in a rigorous and reproducible way. This saves time and effort during the data analysis phase of a project and facilitates sharing analysis workflows alongside publications.
Highlights
Researchers in computer science, behavioral science, medicine, and other fields are increasingly harnessing data collected from smartphone sensors and wearable devices like smartwatches and activity bands to passively monitor people’s activities and environment as they go about their daily lives.Raw or preprocessed mobile sensor data collected over time are usually further manipulated to extract more meaningful behavioral features, such as number of incoming calls, minutes spent at home, or number of screen unlocks that are used to create models of risk prediction or detection [1]
As of August 2021, Reproducible Analysis Pipeline for Data Streams (RAPIDS) can process smartphone data logged with the AWARE Framework and stored in CSV files, MySQL, and InfluxDB databases but researchers can bring support for any other storage medium and Android or iOS mobile sensing applications
Flexible Time Segments In mobile sensing research, behavioral features are usually extracted within specific time windows that aim to summarize human activities at a specific time granularity, for example every hour or day
Summary
Raw or preprocessed mobile sensor data (e.g., smartphone accelerometer logs or Fitbit step counts) collected over time are usually further manipulated to extract more meaningful behavioral features, such as number of incoming calls, minutes spent at home, or number of screen unlocks that are used to create models of risk prediction or detection [1]. If validated, these features have the potential to become behavioral phenotypes [2] or digital biomarkers [3,4,5].
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