Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms

Gao, Farong; Tian, Tao; Yao, Ting; Zhang, Qizhong

doi:10.1155/2021/6693206

Cited by 22 publications

(16 citation statements)

References 51 publications

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“…Accuracy is a key index of human gait classification (Gao et al, 2021 ). In this article, two evaluation indexes are used, accuracy and F1-score, which can be expressed as:…”

Section: Resultsmentioning

confidence: 99%

“…The human leg sEMG signal can offer valuable motion information, such as symmetric and periodic motion in human gait (Deng et al, 2020 ; Gao et al, 2021 ; Yao et al, 2021 ), and it is characterized by simple signal acquisition, intuitive data, and the non-invasive acquisition method (Kim et al, 2018 ; Lin et al, 2020 ; Ma et al, 2020 ). Artificial neural networks have made great progress, are widely used in the field of classification, and have shown great performance (Adewuyi et al, 2016 ; Atzori et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton

et al. 2021

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Gait phase classification is important for rehabilitation training in patients with lower extremity motor dysfunction. Classification accuracy of the gait phase also directly affects the effect and rehabilitation training cycle. In this article, a multiple information (multi-information) fusion method for gait phase classification in lower limb rehabilitation exoskeleton is proposed to improve the classification accuracy. The advantage of this method is that a multi-information acquisition system is constructed, and a variety of information directly related to gait movement is synchronously collected. Multi-information includes the surface electromyography (sEMG) signals of the human lower limb during the gait movement, the angle information of the knee joints, and the plantar pressure information. The acquired multi-information is processed and input into a modified convolutional neural network (CNN) model to classify the gait phase. The experiment of gait phase classification with multi-information is carried out under different speed conditions, and the experiment is analyzed to obtain higher accuracy. At the same time, the gait phase classification results of multi-information and single information are compared. The experimental results verify the effectiveness of the multi-information fusion method. In addition, the delay time of each sensor and model classification time is measured, which shows that the system has tremendous real-time performance.

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“…Accuracy is a key index of human gait classification (Gao et al, 2021 ). In this article, two evaluation indexes are used, accuracy and F1-score, which can be expressed as:…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton

et al. 2021

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“…These methods use handcrafted features, which is a crucial challenge because they cannot accurately distinguish different activities (simple and complex). Feature extraction methods such as symbolic representation, raw data statistics, and conversion coding are widely used in the HAR [ 103 ]. Still, they are exploratory methods and need expert knowledge to design features.…”

Section: Evaluation and Testingmentioning

confidence: 99%

Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Serpush

Menhaj

Masoumi

et al. 2022

Computational Intelligence and Neuroscience

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Human activity recognition (HAR) has been of interest in recent years due to the growing demands in many areas. Applications of HAR include healthcare systems to monitor activities of daily living (ADL) (primarily due to the rapidly growing population of the elderly), security environments for automatic recognition of abnormal activities to notify the relevant authorities, and improve human interaction with the computer. HAR research can be classified according to the data acquisition tools (sensors or cameras), methods (handcrafted methods or deep learning methods), and the complexity of the activity. In the healthcare system, HAR based on wearable sensors is a new technology that consists of three essential parts worth examining: the location of the wearable sensor, data preprocessing (feature calculation, extraction, and selection), and the recognition methods. This survey aims to examine all aspects of HAR based on wearable sensors, thus analyzing the applications, challenges, datasets, approaches, and components. It also provides coherent categorizations, purposeful comparisons, and systematic architecture. Then, this paper performs qualitative evaluations by criteria considered in this system on the approaches and makes available comprehensive reviews of the HAR system. Therefore, this survey is more extensive and coherent than recent surveys in this field.

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“…With the development of deep learning, the existing person re-ID methods use convolutional neural networks for feature extraction and person retrieval [27][28][29][30][31]. In recent years, supervised person re-ID has been extensively studied and has achieved great performance [2,32].…”

Section: Unsupervised Person Re-idmentioning

confidence: 99%

Stable Median Centre Clustering for Unsupervised Domain Adaptation Person Re‐Identification

Guo

Sun

Pang

et al. 2021

Computational Intelligence and Neuroscience

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The current unsupervised domain adaptation person re-identification (re-ID) method aims to solve the domain shift problem and applies prior knowledge learned from labelled data in the source domain to unlabelled data in the target domain for person re-ID. At present, the unsupervised domain adaptation person re-ID method based on pseudolabels has obtained state-of-the-art performance. This method obtains pseudolabels via a clustering algorithm and uses these pseudolabels to optimize a CNN model. Although it achieves optimal performance, the model cannot be further optimized due to the existence of noisy labels in the clustering process. In this paper, we propose a stable median centre clustering (SMCC) for the unsupervised domain adaptation person re-ID method. SMCC adaptively mines credible samples for optimization purposes and reduces the impact of label noise and outliers on training to improve the performance of the resulting model. In particular, we use the intracluster distance confidence measure of the sample and its K-reciprocal nearest neighbour cluster proportion in the clustering process to select credible samples and assign different weights according to the intracluster sample distance confidence of samples to measure the distances between different clusters, thereby making the clustering results more robust. The experiments show that our SMCC method can select credible and stable samples for training and improve performance of the unsupervised domain adaptation model. Our code is available at https://github.com/sunburst792/SMCC-method/tree/master.

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Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms

Cited by 22 publications

References 51 publications

A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton

A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton

Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Stable Median Centre Clustering for Unsupervised Domain Adaptation Person Re‐Identification

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