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Extraction of Behavioral Features from Smartphone and Wearable Data

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arxiv 1812.10394 v2 pith:P7I6RULA submitted 2018-12-18 cs.CY cs.HCcs.LGstat.ML

classification cs.CYcs.HCcs.LGstat.ML
keywords datastreamswearabledevicesfeaturesframeworksmartphonesused
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 2 Pith papers

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    A 14-person interview study identifies five situated sources of inequity and 15 lifecycle fairness risks in wellbeing sensing, going beyond identity-based audits.

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    cs.HC 2025-04 conditional novelty 5.0 of 10

    First-week passive sensing and self-report data can predict end-of-term low academic performance with accuracy comparable to prior month-long models, but only within the same term; cross-year generalization is limited...

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