Unsupervised Race Walking Recognition Using Smartphone Accelerometers

In today's race walking competition, the determination of whether an athlete fouls is mainly affected by a referee's subjective judgment, leading to a high possibility of misjudgment. The purpose of this work is to determine whether race walking can be automatically recognized by accelerometers embedded in smartphones. In this work, acceleration data are collected by a smartphone app developed by ourselves. Nineteen features are extracted from the raw sensor data, and are used by an unsupervised classification method for activity recognition, named MCODE. We evaluate various data sampling rates and window lengths during feature extraction in the experiments. We also compare our method with other well-known methods on the metrics such as sensitivity, specificity and adjusted rank index. The results show that our method is viable to recognize race walking using smartphone accelerometers.