Research Article
RunningCoach – Cadence Training System for Long-Distance Runners
@INPROCEEDINGS{10.1145/3154862.3154935, author={Daniel Aranki and Uma Balakrishnan and Hannah Sarver and Lucas Serven and Carlos Asuncion and Kaidi Du and Caitlin Gruis and Gao Xian Peh and Yu Xiao and Ruzena Bajcsy}, title={RunningCoach -- Cadence Training System for Long-Distance Runners}, proceedings={11th EAI International Conference on Pervasive Computing Technologies for Healthcare}, publisher={ACM}, proceedings_a={PERVASIVEHEALTH}, year={2018}, month={1}, keywords={mhealth telemonitoring marathon cadence berkeley telemonitoring project}, doi={10.1145/3154862.3154935} }- Daniel Aranki
Uma Balakrishnan
Hannah Sarver
Lucas Serven
Carlos Asuncion
Kaidi Du
Caitlin Gruis
Gao Xian Peh
Yu Xiao
Ruzena Bajcsy
Year: 2018
RunningCoach – Cadence Training System for Long-Distance Runners
PERVASIVEHEALTH
ACM
DOI: 10.1145/3154862.3154935
Abstract
Long-distance running is a category of sports that is injury-prone. Half of the injuries sustained in long-distance running are at the knee and are attributed to the inability of the lower extremity joints to sufficiently handle the load applied during initial stance. Furthermore, cadence (steps per minute) has been identified as a factor that is strongly associated with running-related injuries. Increasing cadence results in reduced energy absorption at the hip and the knee, thus reducing the risk of some common running injuries. Therefore, it is vital for runners to run at an appropriate running cadence in order to minimize risk of injury. In this paper, we present an mHealth system that remotely monitors running cadence levels of runners in a continuous fashion, among other variables, and provides immediate feedback to runners in an effort to help them optimize their running cadence. We also present some initial findings based on a feasibility study we are currently conducting using this system.
