Despite the ubiquity and rapid growth of mobile reading activities, researchers and practitioners today either rely on coarse-grained metrics such as click-through-rate (CTR) and dwell time, or expensive equipment such as gaze trackers to understand users’ reading behavior on mobile devices. We present Lepton, an intelligent mobile reading system and a set of dual-channel sensing algorithms to achieve scalable and fine-grained understanding of users’ reading behaviors, comprehension, and engagements on unmodified smartphones. Lepton tracks the periodic lateral patterns, i.e. saccade, of users’ eye gaze via the front camera, and infers their muscle stiffness during text scrolling via a Mass-Spring-Damper (MSD) based kinematic model from touch events. Through a 25-participant study, we found that both the periodic saccade patterns and muscle stiffness signals captured by Lepton can be used as expressive features to infer users’ comprehension and engagement in mobile reading. Overall, our new signals lead to significantly higher performances in predicting users’ comprehension (correlation: 0.36 vs. 0.29), concentration (0.36 vs. 0.16), confidence (0.5 vs. 0.47), and engagement (0.34 vs. 0.16) than using traditional dwell-time based features via a user-independent model.