Gait phase detection is important in the field of motion analysis and exoskeleton-assisted walking so that the accurate control of exoskeleton robots can be achieved. Therefore, to obtain accurate motion gait information and ensure good detection accuracy of the gait phase, in this study, a new hidden Markov model (N-HMM) algorithm is proposed to improve the accuracy of gait phase detection. A multisensor gait data acquisition system was developed to determine the acceleration and plantar pressure of the human body. Data were collected from 10 healthy subjects and sensors were attached to the subjects’ legs and feet to detect the motion gait phase. A comparison of results with hidden Markov model (HMM) algorithms shows that the proposed algorithm improves the recall and precision rates by 3 and 3.5%, respectively. The N-HMM was used for gait phase detection and the detection accuracy of the N-HMM was compared with that of the HMM, support vector machine (SVM), decision tree, and back propagation (BP) network algorithms. The average accuracy of the N-HMM was 96.2%, outperforming all other algorithms. The results show that the N-HMM is capable of detecting the human gait phase with high accuracy. The results of this study provide a theoretical basis for the design and control of exoskeleton robots.