  Automatic classification of running styles can enable runners to obtain
feedback with the aim of optimizing performance in terms of minimizing energy
expenditure, fatigue, and risk of injury. To develop a system capable of
classifying running styles using wearables, we collect a dataset from 10
healthy runners performing 8 different pre-defined running styles. Five
wearable devices are used to record accelerometer data from different parts of
the lower body, namely left and right foot, left and right medial tibia, and
lower back. Using the collected dataset, we develop a deep learning solution
which consists of a Convolutional Neural Network and Long Short-Term Memory
network to first automatically extract effective features, followed by learning
temporal relationships. Score-level fusion is used to aggregate the
classification results from the different sensors. Experiments show that the
proposed model is capable of automatically classifying different running styles
in a subject-dependant manner, outperforming several classical machine learning
methods (following manual feature extraction) and a convolutional neural
network baseline. Moreover, our study finds that subject-independent
classification of running styles is considerably more challenging than a
subject-dependant scheme, indicating a high level of personalization in such
running styles. Finally, we demonstrate that by fine-tuning the model with as
few as 5% subject-specific samples, considerable performance boost is obtained.
