Transfer Learning for Activity Recognition in Mobile Health

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

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Transfer Learning for Activity Recognition in Mobile Health

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arxiv 2007.06062 v1 pith:D4AHWEX5 submitted 2020-07-12 cs.LG cs.HCstat.ML

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

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Hassan Ghasemzadeh

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classification cs.LGcs.HCstat.ML

keywords activityrecognitiontransfallhealthlayerlearningmobiletransfer

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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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