7. The apparatus of claim 6 , wherein the apparatus further comprises a clustering part configured: to obtain a first rotational angle coordinate sample for each of the plurality of fingerspelling motions using roll and pitch rotation values acquired from the at least one gyro sensor; to measure a distance from a first central coordinate for each of the predetermined gyro groups with respect to the first rotational angle coordinate sample and to assign the first rotational angle coordinate sample to a group in closest proximity to the first rotational angle coordinate sample; to calculate a second central coordinate according to an average value of the first rotational angle coordinate sample and the first central coordinate; and to repeat the processes of the measuring and the calculating.
6. An apparatus for recognizing sign language using at least one electromyogram sensor and at least one gyro sensor, the apparatus comprising: a processor configured to store a set of Gaussian candidate models for a plurality of fingerspelling motions and to group the set of Gaussian candidate models into predetermined gyro groups; a signal receiving part configured to receive a first gyro measurement signal from at least one gyro sensor and a first electromyogram measurement signal from the at least one electromyogram sensor, wherein the at least one gyro sensor and the at least one electromyogram sensor are attached to a body of a subject, and wherein the first gyro measurement signal includes a first pitch rotation and a first roll rotation; a group determining part configured to determine a first gyro group which is in closest proximity to the first gyro measurement signal among the predetermined gyro groups; a model acquiring part configured to acquire a first Gaussian model for the first electromyogram measurement signal; and a sign language recognizing part configured to compare the first Gaussian model with the set of Gaussian candidate models in the first gyro group, and to recognize a first fingerspelling motion which corresponds to the first Gaussian candidate model.