… In this section, we illustrate the performance of the proposed recognition and segmentation
method using timeseries human motion data. We evaluated two aspects of the algorithm. …

… networks, position updates of moving objects in location-… the segmented time series, we
learn a hidden Markov model. … -based hidden Markov model (pHMM) for time series data. It …

… the time-series data to extract the motions of interest [55]. … ] apply the ZVC concept to motion
segmentation to study the … for gesture recognition, with an HMM for motion identification [77]. …

… basic concepts and formulate the time series segmentation … , we start with the classic
Hidden Markov Model (HMM) [40], … Kulic, “Movement primitive segmentation for human motion …

… [36] combines HMMs and AdaBoost to learn discriminative … a multi-dimensional time series
recording of a motion trial. First… can be characterized by the concept of dynamic time warping. …

… into a univariate or multivariate time series. As in many areas … techniques such as hidden
Markov model, multi-layer … encoding segments into a multidimensional format using the concept …

… We can see that sij has no relationship with the body position, … have been manually segmented,
we simplify online extracting … time series based methods [6, 17, 19, 20], which treat the …

… type finds repeated segments from a continuous time series. … Figure 1 shows the basic concept
of double articulation in … This corresponds to the relationship between a spoken word and …

… technique that uses time-series information extracted from … a combination of the Hidden
Markov Model (HMM) and the multi-… [26] modified the concept of vanilla DTW using a weighting …

… Additionally, we also present a new motion representation … In this paper, we use the concepts
of detectors and connectors … 7, each plot (b) contains three curves, ie, time series of the …