Meta Products towards a “gait/running style app” - Daumer, Martin; Schneider, Andreas N.; Mrowca, Damian; Ding, Rui; Gao, Han; Christian, Lederer (PeerJ PrePrints, 2014)
Background: The individual running style has an impact on the running performance as well as the running injury risk. In order to increase the performance and lower the injury risk, runners should be educated towards a healthy running style. But before advices can be made it is crucial to distinguish running styles from each other.Aim: The stretch goal is to build a running style app, which is able to track and display the user’s current running style by using accelerometry data, based on which advice can be given for a healthy and efficient running style with the help of gaming tools. To validate the approach, a gold standard with outdoor running acceleration data has to be created.Methods: The accelerometry data used by the smartphone app is gathered from the “actibelt”, an accelerometer included in a belt buckle. This sensor collects data close to the body COM in all three dimensions which is transferred to a smartphone via Bluetooth in real-time. The focus of this work is the validation of an acceleration based detection of different running styles, namely heel strikes, midfoot strikes and forefoot strikes. Features, which are able to clearly distinguish different running styles, have to be extracted out of the accelerometry data with machine learning techniques (SVM). Laboratory experiments have been conducted to analyze the actibelt data of three test persons performing heel, midfoot and forefoot strikes on a pressure sensitive treadmill with video control. As running apps are mainly used outdoors, the results had to be reproduced with outdoor running data. In an extreme ends approach four test persons with different running experience ranging from professional to occasional runners were asked to successively run on their heels, midfoot and forefoot, while accelerometry data was recorded and synchronized with mobile high speed video. The different running styles were performed on different substrates, with different shoes and speeds. Discussion/Conclusion: While significant differences in the accelerometry data of the running styles have been observed in the laboratory, those differences couldn’t be reproduced in outdoor environments. Characteristic peak patterns (Lieberman, nature 463, 531-535) could be reproduced in the laboratory but disappeared in outdoor running. The most distorting aspects are the harder and less comfortable surface and an irregular speed compared to treadmill running. Hence, for a reliable detection of the running style, the actibelt data may be complemented by further sensors, e.g. placed in the socks. A promising idea is to influence the stride frequency of runners at given speeds to improve the individual running style.
Keywords: running style; accelerometry; running injury risk; actibelt