Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

Human-gait-phase-recognition is an important technology in the field of exoskeleton robot control and medical rehabilitation. Inertial sensors with accelerometers and gyroscopes are easy to wear, inexpensive and have great potential for analyzing gait dynamics. However, current deep-learning methods extract spatial and temporal features in isolation—while ignoring the inherent correlation in high-dimensional spaces—which limits the accuracy of a single model. This paper proposes an effective hybrid deep-learning framework based on the fusion of multiple spatiotemporal networks (FMS-Net), which is used to detect asynchronous phases from IMU signals. More specifically, it first uses a gait-information acquisition system to collect IMU sensor data fixed on the lower leg. Through data preprocessing, the framework constructs a spatial feature extractor with CNN module and a temporal feature extractor, combined with LSTM module. Finally, a skip-connection structure and the two-layer fully connected layer fusion module are used to achieve the final gait recognition. Experimental results show that this method has better identification accuracy than other comparative methods with the macro-F1 reaching 96.7%.

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Systematic Review on Human Gait Analysis using Deep Learning Models

10.54216/ajbor.060101 ◽

2021 ◽

pp. 09-22

Author(s):

Piyush Kumar Shukla ◽

◽

Prashant Kumar Shukla ◽

Keyword(s):

Deep Learning ◽

Gait Analysis ◽

Gait Recognition ◽

The Body ◽

Human Gait ◽

Learning Models ◽

Gait Patterns ◽

Learning Techniques ◽

The Individual ◽

Human Gait Analysis

Human Gait is known as a behavioral characteristic of humans, compared with the other biometrics gait is found to be a difficult process to conceal. Human gait analysis is usually done by extracting the features from the body. Analysis of gait involves evaluating the individual by means of kinematic analysis while walking along a surface. The main objective and the purpose of gait recognition is to give the best method where risks are recognized in places where there is a need for high security in any public place and to detect diseases like Parkinson’s. In order to acquire a normal person’s identification and validation performance, various Deep Learning techniques are totally studied and modeled the biometrics of gait which is based on walking data. It is reviewed that among various essential metrics that are used, deep learning convolution neural networks are typically better Machine Learning models. The main objective of the present study was to examine in detail individual gait patterns. Finally, this paper recommends deep learning methods and suggests the directions for future gait analysis and also for its applications.

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Hybrid Deep Learning for Week-Ahead Evapotranspiration Forecasting

10.21203/rs.3.rs-424493/v1 ◽

2021 ◽

Author(s):

A. A Masrur Ahmed ◽

Ravinesh C Deo ◽

Qi Feng ◽

Afshin Ghahramani ◽

Nawin Raj ◽

...

Keyword(s):

Deep Learning ◽

Ant Colony Optimization ◽

High Efficiency ◽

Scientific Information ◽

Forecast Model ◽

Absolute Error ◽

Ant Colony ◽

Climate Indices ◽

Linear Relationships ◽

Moderate Resolution Imaging Spectroradiometer

Abstract Reference evapotranspiration (ET) is an integral hydrological factor in soil-plant-atmospheric water balance studies and the management of drought events. This paper proposes a new hybrid-deep learning (DL) approach, combining Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) along with Ant Colony Optimization (ACO), for a multi-step (week 1 to 4) daily-ET forecast. The method also assimilates a comprehensive dataset with 52 diverse predictors, i.e., satellite-derived Moderate Resolution Imaging Spectroradiometer (MODIS), ground-based datasets from Scientific Information for Landowners (SILO) and synoptic-scale climate indices (CI). To develop a vigorous CNN-GRU model, a feature selection stage entails the Ant Colony Optimization (ACO) method implemented to improve the ET forecast model for the three selected sites in Australian Murray Darling Basin. The results demonstrate excellent forecasting capability of the hybrid CNN-GRU model against the counterpart benchmark models, evidenced by a relatively small mean absolute error and high efficiency. Overall, this study shows that the proposed hybrid CNN-GRU model successfully apprehends the complex and non-linear relationships between predictor variables and the daily ET.

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Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion

Sensors ◽

10.3390/s21227584 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7584

Author(s):

Faizan Saleem ◽

Muhammad Attique Khan ◽

Majed Alhaisoni ◽

Usman Tariq ◽

Ammar Armghan ◽

...

Keyword(s):

Feature Extraction ◽

Feature Selection ◽

Deep Learning ◽

Data Augmentation ◽

Feature Fusion ◽

Gait Recognition ◽

Human Gait ◽

Computational Time ◽

Absolute Deviation ◽

Whale Optimization

Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking surfaces. Furthermore, identification from differing views is a significant difficulty in HGR. Many techniques have been introduced in the literature for HGR using conventional and deep learning techniques. However, the traditional methods are not suitable for large datasets. Therefore, a new framework is proposed for human gait recognition using deep learning and best feature selection. The proposed framework includes data augmentation, feature extraction, feature selection, feature fusion, and classification. In the augmentation step, three flip operations were used. In the feature extraction step, two pre-trained models were employed, Inception-ResNet-V2 and NASNet Mobile. Both models were fine-tuned and trained using transfer learning on the CASIA B gait dataset. The features of the selected deep models were optimized using a modified three-step whale optimization algorithm and the best features were chosen. The selected best features were fused using the modified mean absolute deviation extended serial fusion (MDeSF) approach. Then, the final classification was performed using several classification algorithms. The experimental process was conducted on the entire CASIA B dataset and achieved an average accuracy of 89.0. Comparison with existing techniques showed an improvement in accuracy, recall rate, and computational time.

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