1. A method for recognizing an input human motion as most similar to a model human motion, which is a member of a stored collection of model human motions, comprising the steps of: a. Creating a collection of model human motions by measuring and recording the model trajectories of body parts that pertain to human performances of such collection of model human motions; b. sampling at a model-rate of sampling, one recorded model trajectories of body parts; c. repeating step b each time with another recorded model trajectories of body parts, until all the recorded model trajectories of body parts included in the collection of model human motions, have been sampled; d. representing each sample of the recorded model trajectories of body parts by a model vector m rj ; wherein each component of such model vector is derived from a sample of a recorded model trajectory of one body part; e. representing each member of the collection of model human motions by a model vectors sequence M j &equals;(m 1j... m rj... m qj ); wherein the subscript q denotes the total number of model vectors in the model vectors sequence M j ; wherein the subscript r denotes the location of the model vector m rj within the model vectors sequence M j ; wherein the subscript j denotes the serial number of the model vectors sequence M j within the collection of model vectors sequences &lcub;M j &rcub; that represents the corresponding collection of model human motions; f. storing in a hash table, the entire collection of model vectors sequences &lcub;M j &rcub; by storing each of the model vectors m rj that belongs to the collection of model vectors sequences &lcub;M j &rcub;, in a hash table bin whose address is the nearest to the model vector m rj ; g. Acquiring an input human motion by measuring and recording the input trajectories of body parts that pertain to a human performance of such input human motion; h. sampling at an input-rate of sampling, the recorded input trajectories of body parts; wherein the input-rate of sampling is set to be substantially different from the model-rate of sampling; i. representing each sample of the recorded input trajectories of body parts by an input vector t nk ; wherein each component of such an input vector is derived from a sample of a recorded input trajectory of the same body part that pertains to the corresponding component of the model vector m rj ; j. representing the sampled input human motion by an input vectors sequence T k &equals;(t 1k... t nk... t pk ); wherein the subscript p denotes the total number of input vectors in the input vectors sequence; wherein the subscript n denotes the location of the input vector t nk within the input vectors sequence T k ; wherein the subscript k denotes the serial number of the input vectors sequence; k. employing an optimal matching algorithm to find the optimal matching score between the input vectors sequence T k and one model vectors sequence M j, which is one of the members of the collection of model vectors sequences &lcub;M j &rcub;; l. repeating step k until all the optimal matching scores that pertain to all the model vectors sequences M j, which are members of the collection of model vectors sequences &lcub;M j &rcub; are found; m. comparing all the optimal matching scores that pertain to all the model vectors sequences M j, which are members of the collection of model vectors sequences &lcub;M j &rcub; and finding one model vectors sequence M o that has the highest optimal matching score; recognizing the input human motion as most similar to the model human motion that pertains to the model vectors sequence M o with the highest optimal matching score;