Rapid and accurate recognition of human action and road condition is a foundation and precondition of implementing self-control of intelligent prosthesis. In this paper, a Gaussian mixture model and hidden Markov model are used to recognize the road condition and human motion modes based on the inertial sensor in artificial limb (lower limb). Firstly, the inertial sensor is used to collect the acceleration, angle and angular velocity signals in the direction of x, y and z axes of lower limbs. Then we intercept the signal segment with the time window and eliminate the noise by wavelet packet transform, and the fast Fourier transform is used to extract the features of motion. Then the principal component analysis (PCA) is carried out to remove redundant information of the features. Finally, Gaussian mixture model and hidden Markov model are used to identify the human motion modes and road condition. The experimental results show that the recognition rate of routine movement (walking, running, riding, uphill, downhill, up stairs and down stairs) is 96.25%, 92.5%, 96.25%, 91.25%, 93.75%, 88.75% and 90% respectively. Compared with the support vector machine (SVM) method, the results show that the recognition rate of our proposed method is obviously higher, and it can provide a new way for the monitoring and control of the intelligent prosthesis in the future.
混合模型框架下的模型，如潜变量增长混合模型（latent growth mixture modeling，LGMM）或潜类别增长分析（latent class growth analysis，LCGA），因估算过程中涉及多个决策过程，导致潜变量轨迹分析结果的报告呈现多样性。为解决这一问题，指南制订小组按照系统化的制订流程，通过 4 轮德尔菲法调查，遵循专家小组意见，提出了各领域报告潜变量轨迹分析结果时需采用统一的标准，最终确定了报告轨迹研究结果必要的关键条目，发布了潜变量轨迹研究报告规范（guidelines for reporting on latent trajectory studies，GRoLTS），并利用 GRoLTS 评价了 38 篇使用 LGMM 或 LCGA 研究创伤后应激轨迹的论文的报告情况。
In clinic, intima and media thickness are the main indicators for evaluating the development of atherosclerosis. At present, these indicators are measured by professional doctors manually marking the boundaries of the inner and media on B-mode images, which is complicated, time-consuming and affected by many artificial factors. A grayscale threshold method based on Gaussian Mixture Model (GMM) clustering is therefore proposed to detect the intima and media thickness in carotid arteries from B-mode images in this paper. Firstly, the B-mode images are clustered based on the GMM, and the boundary between the intima and media of the vessel wall is then detected by the gray threshold method, and finally the thickness of the two is measured. Compared with the measurement technique using the gray threshold method directly, the clustering of B-mode images of carotid artery solves the problem of gray boundary blurring of inner and middle membrane, thereby improving the stability and detection accuracy of the gray threshold method. In the clinical trials of 120 healthy carotid arteries, means of 4 manual measurements obtained by two experts are used as reference values. Experimental results show that the normalized root mean square errors (NRMSEs) of the estimated intima and media thickness after GMM clustering were 0.104 7 ± 0.076 2 and 0.097 4 ± 0.068 3, respectively. Compared with the results of the direct gray threshold estimation, means of NRMSEs are reduced by 19.6% and 22.4%, respectively, which indicates that the proposed method has higher measurement accuracy. The standard deviations are reduced by 17.0% and 21.7%, respectively, which indicates that the proposed method has better stability. In summary, this method is helpful for early diagnosis and monitoring of vascular diseases, such as atherosclerosis.
In medical research, latent subgroups often emerge with characteristics or trends distinct from the general population, yet identifying them directly remain challenging. The latent variable mixture modeling, grounded in the idea that a population consists of a limited mixture of subgroups, assigns latent categories to individuals based on posterior probabilities. This model is suitable for both cross-sectional and longitudinal datasets. Approaching from a statistical perspective, this paper thoroughly explicates the foundational principles of four prevalent methods within the latent variable mixture modeling realm, outlining the essential modeling workflow. By integrating insights from previous cases and real-world data, we review the rational applications of these methods. The latent variable mixture modeling stands as a flexible classification tool for identifying and analyzing latent categories within research populations, further facilitating the in-depth exploration of predictors influencing these latent categories and their consequent effects on outcome variables.