EarGo: Is Earbud a Necessary Complement to Smartwatch for Estimation of the Running Dynamics Parameters?

Human activity recognition has been an established active research area within the past few decades. While many researchers have tried to estimate some of the gait and running parameters, none was successful to provide a full suite of running dynamics parameters using commodity devices. Earbuds with their unique placement (in line with center of mass) provide an opportunity for activity recognition that never existed before with other commodity devices. Taking advantage of this opportunity, this work proposes a multi-modal approach to measure running dynamics using fusion of earbuds and smartwatch. Collecting a large dataset of 53 subjects, we developed various regression models to identify running parameters such as speed, cadence, stride length, vertical oscillation and ground contact time. These parameters were estimated in both jog and walk conditions and were evaluated in different device and context settings. Our MAPE ranges from 6.04% to 11.54% for various parameters.