Augmented Human State Estimation Using Interacting Multiple Model Particle Filters With Probabilistic Data Association

@article{Chalvatzaki2018AugmentedHS,
  title={Augmented Human State Estimation Using Interacting Multiple Model Particle Filters With Probabilistic Data Association},
  author={Georgia Chalvatzaki and Xanthi S. Papageorgiou and Costas S. Tzafestas and Petros Maragos},
  journal={IEEE Robotics and Automation Letters},
  year={2018},
  volume={3},
  pages={1872-1879}
}
The accurate human gait tracking is an important factor for various robotic applications, such as robotic walkers aiming to provide assistance to patients with different mobility impairment, social robot companions, etc. A context-aware robot control architecture needs constant knowledge of the user's kinematic state to assess the patient's gait status and adjust its movement properly to provide optimal assistance. In this letter, we present a novel human gait tracking approach that uses two… 

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References

SHOWING 1-10 OF 29 REFERENCES
Comparative experimental validation of human gait tracking algorithms for an intelligent robotic rollator
TLDR
This work experimentally validate the performance of two gait tracking algorithms using data from elderly patients, and demonstrates the superior performance of the PFs in difficult cases of occlusions and clutter, where KF tracking often fails.
Towards a user-adaptive context-aware robotic walker with a pathological gait assessment system: First experimental study
TLDR
The results demonstrate that a generic control scheme does not meet every patient's needs, and therefore, an Adaptive Context-Aware MAD (ACA MAD), that can understand the specific needs of the user, is important for enhancing the human-robot physical interaction.
Parallel Interacting Multiple Model-Based Human Motion Prediction for Motion Planning of Companion Robots
TLDR
An autonomous motion planning framework for companion robots to accompany humans in a socially desirable manner, which takes safety and comfort requirements into account is proposed, and a nonlinear model predictive control technique is utilized for the robot motion planning.
Experimental validation of human pathological gait analysis for an assisted living intelligent robotic walker
TLDR
Experimental validation of an in house developed gait analysis system using a Hidden Markov Model for gait cycle estimation, and extraction of the gait parameters demonstrates that the added complexity of the HMM system is necessary for improving the accuracy and efficacy of the system.
Estimating double support in pathological gaits using an HMM-based analyzer for an intelligent robotic walker
TLDR
The results obtained and presented demonstrate that the proposed human data analysis (modeling, learning and inference) framework has the potential to support efficient detection and classification of specific walking pathologies, as needed to empower a cognitive robotic mobility-assistance device with user-adaptive and context-aware functionalities.
Particle filter based feedback control of JAIST Active Robotic Walker
TLDR
A particle filtered interface function (PFIF) is proposed to estimate and predict the locations of the user's legs and body and the simple feedback motion control function adjusts the motions of JARoW corresponding to the estimation and prediction.
Tracking multiple moving targets with a mobile robot using particle filters and statistical data association
TLDR
A sample-based variant of joint probabilistic data association filters is introduced to track features originating from individual objects and to solve the correspondence problem between the detected features and the filters.
A Multiple Hypothesis Walking Person Tracker with Switched Dynamic Model
TLDR
This paper presents the first comprehensive model for a walking person in range data from a scanner mounted at leg height and extends the multiple hypothesis framework to allow for both association uncertainty and a switched dynamic model depending on the currently moving leg.
Context-aware assisted interactive robotic walker for Parkinson's disease patients
TLDR
A context-aware assisted active robotic walker for Parkinson's disease (PD) patients that locks the motors when sudden forward pushing by the user is detected, and the road conditions in front of the walker will be automatically analyzed, making user able to adjust his/her walking pace dynamically.
Efficient people tracking in laser range data using a multi-hypothesis leg-tracker with adaptive occlusion probabilities
TLDR
This work extends the data association so that it explicitly handles track occlusions in addition to detections and deletions, and adapt the corresponding probabilities in a situation-dependent fashion so as to reflect the fact that legs frequently occlude each other.
...
1
2
3
...