Hidden Markov modeling of human normal gait using laser range finder for a mobility assistance robot

@article{Papageorgiou2014HiddenMM,
  title={Hidden Markov modeling of human normal gait using laser range finder for a mobility assistance robot},
  author={Xanthi S. Papageorgiou and Georgia Chalvatzaki and Costas S. Tzafestas and Petros Maragos},
  journal={2014 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2014},
  pages={482-487}
}
For an effective intelligent active mobility assistance robot, the walking pattern of a patient or an elderly person has to be analyzed precisely. A well-known fact is that the walking patterns are gaits, that is, cyclic patterns with several consecutive phases. These cyclic motions can be modeled using the consecutive gait phases. In this paper, we present a completely non-invasive framework for analyzing a normal human walking gait pattern. Our framework utilizes a laser range finder sensor… 

Figures from this paper

Hidden markov modeling of human pathological gait using laser range finder for an assisted living intelligent robotic walker
TLDR
This paper presents a completely non-invasive framework for analyzing and recognizing a pathological human walking gait pattern, using a laser range finder sensor to detect and track the human legs, and an appropriately synthesized Hidden Markov Model for state estimation, and recognition of the gait patterns.
Experimental comparison of human gait tracking algorithms: Towards a context-aware mobility assistance robotic walker
TLDR
This work presents the experimental comparison of two user leg tracking systems of a robotic assistance walker, using data collected by a laser range sensor to demonstrate the applicability of the tracking systems in real test cases.
Human Gait Phase Recognition using a Hidden Markov Model Framework*
TLDR
A novel discrete/continuous unsupervised Hidden Markov Model method is proposed that is able to recognize six gait phases of a typical human walking cycle through the use of two wearable Inertial Measurement Units (IMUs) mounted at both feet of the subject.
User-Adaptive Human-Robot Formation Control for an Intelligent Robotic Walker Using Augmented Human State Estimation and Pathological Gait Characterization
TLDR
The experimental evaluation comprises gait data from real patients, and demonstrates the stability of the human-robot formation control, indicating the importance of incorporating an on-line gait characterization of the user, using non-wearable and non-invasive methods, in the context of a robotic MAD.
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.
Gait modelling for a context-aware user-adaptive robotic assistant platform
TLDR
This work presents the basic concept for the robot control architecture and analyse three essential parts of the Adaptive Context-Aware Robot Control scheme; the detection and tracking of the subject’s legs, the gait modelling and classification and the computation of gait parameters for the impairment level assessment.
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.
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.
Comparing the Impact of Robotic Rollator Control Schemes on Elderly Gait using on-line LRF-based Gait Analysis
TLDR
A thorough experimental analysis is presented, using an on-line gait tracking and analysis system, to examine the impact of different control designs on the gait performance of elderly subjects who use an intelligent robotic rollator.
Automatic Recognition of Gait Phases Using a Multiple-Regression Hidden Markov Model
TLDR
The proposed approach outperforms standard unsupervised classification methods (Gaussian mixture model, k-means, and hidden Markov model), while remaining competitive with respect to standard supervised classification methods(support vector machine, random forest, and k-nearest neighbor).
...
1
2
3
...

References

SHOWING 1-10 OF 37 REFERENCES
Gait-based recognition of humans using continuous HMMs
TLDR
This paper proposes a view-based approach to recognize humans through gait using a continuous hidden Markov model that serves to compactly capture structural and transitional features that are unique to an individual.
Gait phase analysis based on a Hidden Markov Model
A reliable gait phase detection system
TLDR
The experiments showed that the gait phase detection system, unlike other similar devices, was insensitive to perturbations caused by nonwalking activities such as weight shifting between legs during standing, feet sliding, sitting down, and standing up.
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.
Detection and tracking of human legs for a mobile service robot
TLDR
A method for a mobile robot to detect a human leg and to follow the human for interaction with him/her and applies the suggested walking model for the case where mobile robots lost track of the target person's legs and failed to detect those legs.
HMM machine learning and inference for Activities of Daily Living recognition
TLDR
A powerful inference engine based on the Hidden Markov Model, called the Adaptive Learning Hidden MarkOV Model (ALHMM), which combines the Viterbi and Baum–Welch algorithms to enhance the accuracy and learning capability is proposed.
Gait Recognition Using Hidden Markov Model
TLDR
A new gait recognition algorithm using Hidden Markov Model (HMM) is proposed that reduces image feature from the two-dimensional space to a one-dimensional vector in order to best fit the characteristics of one- dimensional HMM.
Accelerometry-Based Classification of Human Activities Using Markov Modeling
TLDR
The HMM classifiers incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do, and the benefits of the obtained statistical leverage are illustrated and discussed.
EMG signals based gait phases recognition using hidden Markov models
TLDR
The application of hidden Markov model (HMM) to recognize gait phase using electromyographic (EMG) signals is described and the feature set and data segmentation manner yielded high rate of accuracy are ascertained.
An Architecture for Understanding Intent Using a Novel Hidden Markov Formulation
TLDR
This paper proposes an approach that allows a physical robot to detect the intent of others based on experience acquired through its own sensory–motor capabilities, then use this experience while taking the perspective of the agent whose intent should be recognized.
...
1
2
3
4
...