Recognition System for Home-Service-Related Sign Language Using Entropy-Based <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-Means Algorithm and ABC-Based HMM
针对家务相关手语，提出用熵图确定隐马尔可夫模型状态数的K均值算法，并结合人工蜂群优化模型参数，在11个台湾手语词上达到91.3%的平均识别率。
This paper presents a recognition system for understanding the words of home-service-related sign language. Because the data received from a sensor are sequential, the hidden Markov model (HMM) that has been successfully applied to speech signals is chosen as a classifier. However, the number of states in the HMM model should be decided upon first before constructing the HMM classifier. To solve this problem, an entropy-based K -means algorithm is proposed to evaluate the number of states in the HMM model with an entropy diagram. Four real datasets are utilized to verify the developed entropy-based K -means algorithm. Moreover, a data-driven method is given to combine the artificial bee colony algorithm with the Baum-Welch algorithm to determine the structure of HMM. The database contains 11 home-service-related Taiwan sign language words and each word is performed ten times, five males and five females are invited to perform such words. Finally, the recognition system is established by 11 HMM models, and the cross-validation demonstrates an average recognition rate of 91.3%.