Human Gait Analysis and Prediction Using the Levenberg-Marquardt Method

Alharbi, Abdullah; Equbal, Kamran; Ahmad, Sultan; Rahman, Haseeb Ur; Alyami, Hashem

doi:10.1155/2021/5541255

Cited by 13 publications

(7 citation statements)

References 13 publications

(12 reference statements)

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“…ese real-time samples are extracted through the Levenberg-Marquardt method, and their artifacts are removed using the Butterworth filters in order to train the neural network effectively. e gait data are observed from 5-subjects at distinct speeds 2.4, 3.2, and 5.4 kmph and in total 45 instances are utilized for evaluation [27]. ough the proposed NN achieved better accuracy for tested data, it is not suitable for dynamic movements, and the tested data range are very low.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Optimized GRU-ECNN Models for Gait Recognition with Wearable IOT Devices

Monica

Parvathi

Gayathri

et al. 2022

Computational Intelligence and Neuroscience

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With the advent of the Internet of Things (IoT), human-assistive technologies in healthcare services have reached the peak of their application in terms of diagnosis and treatment process. These devices must be aware of human movements to provide better aid in clinical applications as well as the user’s daily activities. In this context, real-time gait analysis remains to be key catalyst for developing intelligent assistive devices. In addition to machine and deep learning algorithms, gait recognition systems have significantly improved in terms of high accuracy recognition. However, most of the existing models are focused on improving gait recognition while ignoring the computational overhead that affects the accuracy of detection and even remains unsuitable for real-time implementation. In this research paper, we proposed a hybrid gated recurrent unit (GRU) based on BAT-inspired extreme convolutional networks (BAT-ECN) for the effective recognition of human activities using gait data. The gait data are collected by implanting the wearable Internet of Things (WIoT) devices invasively. Then, a novel GRU and ECN networks are employed to extract the spatio-temporal features which are then used for classification to realize gait recognition. Extensive and comprehensive experimentations have been carried out to evaluate the proposed model using real-time datasets and also other benchmarks such as whuGait and OU-ISIR datasets. To prove the excellence of the proposed learning model, we have compared the model’s performance with the other existing hybrid models. Results demonstrate that the proposed model has outperformed the other learning models in terms of high gait classification and less computational overhead.

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Section: Related Workmentioning

confidence: 99%

Hybrid Optimized GRU-ECNN Models for Gait Recognition with Wearable IOT Devices

Monica

Parvathi

Gayathri

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…Levenberg-Marquardt backpropagation was chosen as the training algorithm due to its better performance than another training algorithm [12]. Some studies found that Levenberg-Marquardt backpropagation has classification results above 95% [27]- [30]. EMG signals were classified into 8 classes, i.e., treadmill walking with speed 1, labeled as T1, treadmill walking with speed 2, labeled as T2, treadmill walking with speed 3, labeled as T3, treadmill walking with speed 4, labeled as T4, treadmill walking with speed 5 labeled as T5, ground walking labeled as GW, walked upstairs Labelled as UW and walked down stair labeled as DW.…”

Section: Classification Methodsmentioning

confidence: 99%

Artificial Neural Network Accuracy Optimization Using Transfer Function Methods on Various Human Gait Walking Environments

Indrawati,

Putri,

Safriana

et al. 2024

JOIV : Int. J. Inform. Visualization

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A bionic leg with ergonomic functionality can increase the user’s independence. However, an ergonomic bionic leg can be challenged to be developed. One of its challenges is related to functionality, where the bionic leg motor can be rugged to adapt to the user. One of the solutions for the bionic leg challenge is the application of a motor driver controlled by the user’s muscle signal. EMG signal can be utilized as the user’s signal source. The EMG signal is then fed back into the motor device. EMG signals obtained during a natural walking environment can result in smooth and natural movement. This study classifies EMG signals into 8 classes: a controlled walking environment (treadmill walking with various speeds) and a natural walking environment (ground walking, upstairs and downstairs walking). This research aims to optimize the ANN method using transfer function variations. The best method will be used to train EMG-driven motors for future studies related to bionic legs. The best ANN parameter in this research using Levenberg-Marquardt backpropagation as a training algorithm with transfer function pairing of the exponential function: Hyperbolic tangent sigmoid transfer function and SoftMax transfer function with 98.8% accuracy and 0.036 MSE value. The best method from the experiment and ANN classification can be used as a training method for a bionic leg in future research.

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“…This framework achieved better performance in terms of performance. However, this architecture has a drawback in that it is inappropriate for dynamic walk patterns [13].…”

Section: Related Workmentioning

confidence: 99%

ECG-NETS – A novel integration of capsule networks and extreme gated recurrent neural network for IoT based human activity recognition

Arokiaraj¹,

Viswanathan²

2023

IFS

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Human Activity Recognition (HAR) has reached its new dimension with the support of Internet of Things (IoT) and Artificial Intelligence (AI). To observe human activities, motion sensors like accelerometer or gyroscope can be integrated with microcontrollers to collect all the inputs and send to the cloud with the help of IoT transceivers. These inputs give the characteristics such as, angular velocity of movements, acceleration and apply them for an effective HAR. But reaching high recognition rate with less complicated computational overhead still represents a problem in the research. To solve this aforementioned issue, this work proposes a novel ensembling of Capsule Networks (CN) and modified Gated Recurrent Units (MGRU) with Extreme Learning Machine (ELM) for an effective HAR classification based on data collected using IoT systems called Ensemble Capsule Gated (ECG)-Networks (NETS). The proposed system uses Capsule networks for spatio-feature extraction and modified (Gated Recurrent Unit) GRU for temporal feature extraction. The powerful feed forward training networks are then employed to train these features for human activity recognition. The proposed model is validated on real time IoT data and WISDM datasets. Experimental results demonstrates that proposed model achieves better results comparatively higher than existing (Deep Learning) DL models.

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Human Gait Analysis and Prediction Using the Levenberg-Marquardt Method

Cited by 13 publications

References 13 publications

Hybrid Optimized GRU-ECNN Models for Gait Recognition with Wearable IOT Devices

Hybrid Optimized GRU-ECNN Models for Gait Recognition with Wearable IOT Devices

Artificial Neural Network Accuracy Optimization Using Transfer Function Methods on Various Human Gait Walking Environments

ECG-NETS – A novel integration of capsule networks and extreme gated recurrent neural network for IoT based human activity recognition

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