1. A system implemented by a portable computing device for creating a custom entry in a database that represents a custom movement performed by a user wearing one or more accelerometers, the system comprising: wearable devices incorporating accelerometers; a portable computing device for receiving wireless input from the wearable devices, said input being formatted into a request that a custom entry be created in a database of the portable computing device that stores a plurality of entries, each custom entry representing one or more feature sets of accelerometer data that are generated when a particular movement is performed; the portable computing device receiving accelerometer data from one or more accelerometers worn by a user while performing a custom movement; using a processor of the portable computing device to generate one or more feature sets by: extracting a chunk of the accelerometer data corresponding to a plurality of axes; splitting the chunk into a plurality of time series, each time series including the accelerometer data from a particular axis; for each time series, extracting a magnitude of various frequencies in the data of each time series; for each time series, summing groups of magnitudes into bins; and for each time series, aggregating bins to form feature sets; and storing the custom entry in the database, the custom entry including the feature sets and an identifier of the custom movement; receiving additional accelerometer data at the portable computing device from the one or more accelerometers worn by the user while performing the custom movement; accessing the database with the processor to determine that the additional accelerometer data received from the one or more accelerometers includes the one or more feature sets of the custom entry; displaying on the portable computer device an indication that the custom movement has been performed by the user; for each of a plurality of feature sets in the database including the feature set of the custom entry, using the processor to determine the inverse Euclidean metric of the feature set of the unknown movement and the feature set; and using the processor to determine which feature set of the plurality of feature sets in the database yields the greatest inverse Euclidean metric.