  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.
