Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model

Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients’ gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment.

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Gait Phase Detection Based on a Foot-Mounted Inertial Sensor Using Long Short-Term Memory Enhanced by Hidden Markov Model

10.23919/icac50006.2021.9594161 ◽

2021 ◽

Author(s):

Zhipeng Yu ◽

Jianghai Zhao ◽

Xiong Zhou ◽

Kun Liu ◽

Yu Yan

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Short Term Memory ◽

Hidden Markov ◽

Inertial Sensor ◽

Phase Detection ◽

Short Term ◽

Term Memory ◽

Gait Phase ◽

Long Short Term Memory

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Human Gait Phase Recognition using a Hidden Markov Model Framework*

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros45743.2020.9341380 ◽

2020 ◽

Author(s):

Ferhat Attal ◽

Yacine Amirat ◽

Abdelghani Chibani ◽

Samer Mohammed

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Human Gait ◽

Model Framework ◽

Gait Phase ◽

Phase Recognition

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Hidden Markov Model based Stride Segmentation on Unsupervised Free-living Gait Data in Parkinson’s Disease Patients

10.21203/rs.3.rs-269965/v1 ◽

2021 ◽

Author(s):

Nils Roth ◽

Arne Küderle ◽

Martin Ullrich ◽

Till Gladow ◽

Franz Marxreiter ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Markov Model ◽

Hidden Markov Model ◽

Gait Analysis ◽

Large Scale ◽

Hidden Markov ◽

Inertial Sensor ◽

Training Data ◽

Free Living

Abstract Background: To objectively assess a patient's gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing. Method : To address this issue, we present a comprehensive free-living evaluation dataset, including 146.574 semi-automatic labeled strides of 28 Parkinson's Disease patients. This dataset was used to evaluate the segmentation performance of a new Hidden Markov Model (HMM) based stride segmentation approach compared to an available dynamic time warping (DTW) based method. Results: The proposed HMM achieved a mean F1-score of 92.1% and outperformed the DTW approach significantly. Further analysis revealed a dependency of segmentation performance to the number of strides within respective walking bouts. Shorter bouts (<30 strides) resulted in worse performance, which could be related to more heterogeneous gait and an increased diversity of different stride types in short free-living walking bouts. In contrast, the HMM reached F1-scores of more than 96.2% for longer bouts (>50 strides). Furthermore, we showed that an HMM, which was trained on at-lab data only, could be transferred to a free-living context with a negligible decrease in performance. Conclusion: The generalizability of the proposed HMM is a promising feature, as fully labeled free-living training data might not be available for many applications. To the best of our knowledge, this is the first evaluation of stride segmentation performance on a large scale free-living dataset. Our proposed HMM-based approach was able to address the increased complexity of free-living gait data, and thus will help to enable a robust assessment of stride parameters in future free-living gait analysis applications

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Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson’s disease patients

Journal of NeuroEngineering and Rehabilitation ◽

10.1186/s12984-021-00883-7 ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Nils Roth ◽

Arne Küderle ◽

Martin Ullrich ◽

Till Gladow ◽

Franz Marxreiter ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Markov Model ◽

Hidden Markov Model ◽

Gait Analysis ◽

Large Scale ◽

Hidden Markov ◽

Inertial Sensor ◽

Training Data ◽

Free Living

Abstract Background To objectively assess a patient’s gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing. Method To address this issue, we present a comprehensive free-living evaluation dataset, including 146.574 semi-automatic labeled strides of 28 Parkinson’s Disease patients. This dataset was used to evaluate the segmentation performance of a new Hidden Markov Model (HMM) based stride segmentation approach compared to an available dynamic time warping (DTW) based method. Results The proposed HMM achieved a mean F1-score of 92.1% and outperformed the DTW approach significantly. Further analysis revealed a dependency of segmentation performance to the number of strides within respective walking bouts. Shorter bouts ($$< 30$$ < 30 strides) resulted in worse performance, which could be related to more heterogeneous gait and an increased diversity of different stride types in short free-living walking bouts. In contrast, the HMM reached F1-scores of more than 96.2% for longer bouts ($$> 50$$ > 50 strides). Furthermore, we showed that an HMM, which was trained on at-lab data only, could be transferred to a free-living context with a negligible decrease in performance. Conclusion The generalizability of the proposed HMM is a promising feature, as fully labeled free-living training data might not be available for many applications. To the best of our knowledge, this is the first evaluation of stride segmentation performance on a large scale free-living dataset. Our proposed HMM-based approach was able to address the increased complexity of free-living gait data, and thus will help to enable a robust assessment of stride parameters in future free-living gait analysis applications.

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