Segmenting Continuous Motions with Hidden Semi-markov Models and Gaussian Processes

Nagaoka, Tomoaki; Nagai, Takayuki; Mochihashi, Daichi; Kobayashi, Ichiro; Asoh, Hideki; Kaneko, Masahide

doi:10.3389/fnbot.2017.00067

Cited by 48 publications

(12 citation statements)

References 15 publications

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“…Structured VAEs (Johnson et al, 2016) introduce a discrete latent variable to control dynamical mode, and use a VAE observation model. GPHSMMs (Nakamura et al, 2017) uses a Gaussian Process observation model within a hidden semi-Markov model. Kalman VAEs integrate a Kalman Filter with a VAE observation model (Fraccaro et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Segmenting Hybrid Trajectories using Latent ODEs

Shi¹,

Morris²

2021

Preprint

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Smooth dynamics interrupted by discontinuities are known as hybrid systems and arise commonly in nature. Latent ODEs allow for powerful representation of irregularly sampled time series but are not designed to capture trajectories arising from hybrid systems. Here, we propose the Latent Segmented ODE (LatSegODE), which uses Latent ODEs to perform reconstruction and changepoint detection within hybrid trajectories featuring jump discontinuities and switching dynamical modes. Where it is possible to train a Latent ODE on the smooth dynamical flows between discontinuities, we apply the pruned exact linear time (PELT) algorithm to detect changepoints where latent dynamics restart, thereby maximizing the joint probability of a piece-wise continuous latent dynamical representation. We propose usage of the marginal likelihood as a score function for PELT, circumventing the need for modelcomplexity-based penalization. The LatSegODE outperforms baselines in reconstructive and segmentation tasks including synthetic data sets of sine waves, Lotka Volterra dynamics, and UCI Character Trajectories.

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Section: Related Workmentioning

confidence: 99%

Segmenting Hybrid Trajectories using Latent ODEs

Shi¹,

Morris²

2021

Preprint

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“…Note that several automatic segmentation methods of time series data for operation data have been proposed. [38][39][40] In this article, the driving operation data are segmented based on GP-HSMM 40 Competitive learning based on HMMs. Next, a set of Positive data is classified and modeled by HMM.…”

Section: Procedures Of Contributing Modelmentioning

confidence: 99%

Detection of contributing object to driving operations based on hidden Markov model

Hashimoto¹,

Yamada²,

Tsuchiya³

et al. 2019

International Journal of Advanced Robotic Systems

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With increase in the number of elderly people in the Japanese society, traffic accidents caused by elderly driver is considered problematic. The primary factor of the traffic accidents is a reduction in their driving cognitive performance. Therefore, a system that supports the cognitive performance of drivers can greatly contribute in preventing accidents. Recently, the development of devices for visually providing information, such as smart glasses or head up display, is in progress. These devices can provide more effective supporting information for cognitive performance. In this article, we focus on the selection problem of information to be presented for drivers to realize the cognitive support system. It has been reported that the presentation of excessive information to a driver reduces the judgment ability of the driver and makes the information less trustworthy. Thus, indiscriminate presentation of information in the vision of the driver is not an effective cognitive support. Therefore, a mechanism for determining the information to be presented to the driver based on the current driving situation is required. In this study, the object that contributes to execution of avoidance driving operation is regarded as the object that drivers must recognize and present for drivers. This object is called as contributing object. In this article, we propose a method that selects contributing objects among the appeared objects on the current driving scene. The proposed method expresses the relation between the time series change of an appeared object and avoidance operation of the driver by a mathematical model. This model can predict execution timing of avoidance driving operation and estimate contributing object based on the prediction result of driving operation. This model named as contributing model consisted of multi-hidden Markov models. Hidden Markov model is time series probabilistic model with high readability. This is because that model parameters express the probabilistic distribution and its statistics. Therefore, the characteristics of contributing model are that it enables the designer to understand the basis for the output decision. In this article, we evaluated detection accuracy of contributing object based on the proposed method, and readability of contributing model through several experiments. According to the results of these experiments, high detection accuracy of contributing object was confirmed. Moreover, it was confirmed that the basis of detected contributing object judgment can be understood from contributing model.

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“…An equivalent discrete-state process can be modelled using Gaussian process regression either via the introduction of change points [ 23 ] or by directly employing a hidden Markov model [ 24 ]. However, in many cases we expect that changes in movement would be more gradual, for example as individuals respond to their environment or internal condition as they move around [ 25 – 27 ].…”

Section: Introductionmentioning

confidence: 99%

A hierarchical machine learning framework for the analysis of large scale animal movement data

2021

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Background In recent years the field of movement ecology has been revolutionized by our ability to collect high-accuracy, fine scale telemetry data from individual animals and groups. This growth in our data collection capacity has led to the development of statistical techniques that integrate telemetry data with random walk models to infer key parameters of the movement dynamics. While much progress has been made in the use of these models, several challenges remain. Notably robust and scalable methods are required for quantifying parameter uncertainty, coping with intermittent location fixes, and analysing the very large volumes of data being generated. Methods In this work we implement a novel approach to movement modelling through the use of multilevel Gaussian processes. The hierarchical structure of the method enables the inference of continuous latent behavioural states underlying movement processes. For efficient inference on large data sets, we approximate the full likelihood using trajectory segmentation and sample from posterior distributions using gradient-based Markov chain Monte Carlo methods. Results While formally equivalent to many continuous-time movement models, our Gaussian process approach provides flexible, powerful models that can detect multiscale patterns and trends in movement trajectory data. We illustrate a further advantage to our approach in that inference can be performed using highly efficient, GPU-accelerated machine learning libraries. Conclusions Multilevel Gaussian process models offer efficient inference for large-volume movement data sets, along with the fitting of complex flexible models. Applications of this approach include inferring the mean location of a migration route and quantifying significant changes, detecting diurnal activity patterns, or identifying the onset of directed persistent movements.

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Segmenting Continuous Motions with Hidden Semi-markov Models and Gaussian Processes

Cited by 48 publications

References 15 publications

Segmenting Hybrid Trajectories using Latent ODEs

Segmenting Hybrid Trajectories using Latent ODEs

Detection of contributing object to driving operations based on hidden Markov model

A hierarchical machine learning framework for the analysis of large scale animal movement data

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