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arXiv · 2007.06062

Transfer Learning for Activity Recognition in Mobile Health

Yuchao Ma·Andrew T. Campbell·Diane J. Cook·John Lach·Shwetak N. Patel·Thomas Ploetz·Majid Sarrafzadeh·Donna Spruijt-Metz·Hassan Ghasemzadeh
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Abstract

While activity recognition from inertial sensors holds potential for mobile health, differences in sensing platforms and user movement patterns cause performance degradation. Aiming to address these challenges, we propose a transfer learning framework, TransFall, for sensor-based activity recognition. TransFall's design contains a two-tier data transformation, a label estimation layer, and a model generation layer to recognize activities for the new scenario. We validate TransFall analytically and empirically.

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Yuchao Ma, Andrew T. Campbell, Diane J. Cook, John Lach, Shwetak N. Patel, Thomas Ploetz, Majid Sarrafzadeh, Donna Spruijt-Metz, Hassan Ghasemzadeh. 2020-07-12. Transfer Learning for Activity Recognition in Mobile Health. https://arxiv.org/abs/2007.06062

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