# Extraction of Behavioral Features from Smartphone and Wearable Data

arXiv: https://arxiv.org/abs/1812.10394 · HTML: https://mlsanity.com/paper/1812.10394/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [CovidRhythm: A Deep Learning Model for Passive Prediction of Covid-19 using Biobehavioral Rhythms Derived from Wearable Physiological Data](https://mlsanity.com/paper/2301.10168/) (2023)

## Abstract (arXiv)

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.