Karg Michelle, Kulić Dana
Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada, N2L 361.
Continental, Lindau, Germany.
Methods Mol Biol. 2017;1552:199-213. doi: 10.1007/978-1-4939-6753-7_15.
Movement primitives are elementary motion units and can be combined sequentially or simultaneously to compose more complex movement sequences. A movement primitive timeseries consist of a sequence of motion phases. This progression through a set of motion phases can be modeled by Hidden Markov Models (HMMs). HMMs are stochastic processes that model time series data as the evolution of a hidden state variable through a discrete set of possible values, where each state value is associated with an observation (emission) probability. Each motion phase is represented by one of the hidden states and the sequential order by their transition probabilities. The observations of the MP-HMM are the sensor measurements of the human movement, for example, motion capture or inertial measurements. The emission probabilities are modeled as Gaussians. In this chapter, the MP-HMM modeling framework is described and applications to motion recognition and motion performance assessment are discussed. The selected applications include parametric MP-HMMs for explicitly modeling variability in movement performance and the comparison of MP-HMMs based on the loglikelihood, the Kullback-Leibler divergence, the extended HMM-based F-statistic, and gait-specific reference-based measures.
运动基元是基本的运动单元，可以顺序或同时组合以构成更复杂的运动序列。运动基元时间序列由一系列运动阶段组成。通过一组运动阶段的这种进展可以用隐马尔可夫模型（HMM）来建模。HMM是一种随机过程，它将时间序列数据建模为隐藏状态变量通过一组离散的可能值的演变，其中每个状态值都与一个观测（发射）概率相关联。每个运动阶段由其中一个隐藏状态表示，顺序由它们的转移概率表示。MP-HMM的观测值是人体运动的传感器测量值，例如运动捕捉或惯性测量。发射概率被建模为高斯分布。在本章中，描述了MP-HMM建模框架，并讨论了其在运动识别和运动性能评估中的应用。所选应用包括用于明确建模运动性能变异性的参数化MP-HMM，以及基于对数似然、库尔贝克-莱布勒散度、基于扩展HMM的F统计量和步态特定参考测量的MP-HMM比较。
