Implementation of machine learning algorithms for gait recognition

Keçeci, Aybüke; Yildirak, Armağan; Ozyazici, Kaan; Ayluctarhan, Gulsen; Agbulut, Onur; Zincir, Ibrahim

doi:10.1016/j.jestch.2020.01.005

Cited by 57 publications

(21 citation statements)

References 4 publications

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“…Machine learning techniques are now widely used in recognition and classification processes, as well as for identifying various physical and movement disorders during daily activities [140][141][142][143][144][145][146][147][148][149][150][151][152][153][154][155]. Machine learning is a method of automation that includes different mathematical functions and algorithms, and it has the capability to learn and generate estimations based on the provided data [156]. A machine learning technique usually works by building a data-driven mathematical model to make estimations and produce decisions based on sample inputs rather than strictly following static program instructions.…”

Section: Data Analysis: Sensor Fusion Prediction and Decision Makingmentioning

confidence: 99%

Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges

Subramaniam

Majumder

Faisal

et al. 2022

Sensors

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Wearable health monitoring devices allow for measuring physiological parameters without restricting individuals’ daily activities, providing information that is reflective of an individual’s health and well-being. However, these systems need to be accurate, power-efficient, unobtrusive and simple to use to enable a reliable, convenient, automatic and ubiquitous means of long-term health monitoring. One such system can be embedded in an insole to obtain physiological data from the plantar aspect of the foot that can be analyzed to gain insight into an individual’s health. This manuscript provides a comprehensive review of insole-based sensor systems that measure a variety of parameters useful for overall health monitoring, with a focus on insole-based PPD measurement systems developed in recent years. Existing solutions are reviewed, and several open issues are presented and discussed. The concept of a fully integrated insole-based health monitoring system and considerations for future work are described. By developing a system that is capable of measuring parameters such as PPD, gait characteristics, foot temperature and heart rate, a holistic understanding of an individual’s health and well-being can be obtained without interrupting day-to-day activities. The proposed device can have a multitude of applications, such as for pathology detection, tracking medical conditions and analyzing gait characteristics.

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Section: Data Analysis: Sensor Fusion Prediction and Decision Makingmentioning

confidence: 99%

Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges

Subramaniam

Majumder

Faisal

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In order to determine the effectiveness of the proposed CNN-based model, it is compared with five baseline methods namely Support Vector Machine (SVM) [34], Decision Tree (DT) [35], Random Forest (RF) [36], Logistic Regression (LR) [37], and K-Nearest Neighbours (KNN) [38]. The models are mainly evaluated based on four performance metrics namely accuracy, precision, recall, and F1 score.…”

Section: Baselines and Evaluation Metricsmentioning

confidence: 99%

Privacy protected user identification using deep learning for smartphone-based participatory sensing applications

Middya

Roy

Mandal

et al. 2021

Neural Comput & Applic

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In smartphone-based crowd/participatory sensing systems, it is necessary to identify the actual sensor data provider. In this context, this paper attempts to recognize the users' identity based on their gait patterns (i.e. unique walking patterns). More specifically, a deep convolution neural network (CNN) model is proposed for the user identification with accelerometer data generated from users smartphone sensors. The proposed model is evaluated based on the real-world benchmark dataset (accelerometer biometric competition data) having a total of 387 users accelerometer sensor readings (60 million data samples). The performance of the proposed CNN-based approach is also compared with five baseline methods namely Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbours (KNN). It is observed that the proposed model achieves better results (accuracy = 98.8%, precision = 0.94, recall = 0.97, and F1-score = 0.95) as compared to the baseline methods.

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“…The prove the performance of the proposed model 4 different classification algorithms are chosen namely Naïve Bayes classifier [21], Decision Forest-Decision jungle [26], Random forest [27] and SVM [28]. The different performance measures include Accuracy, Precision, Recall, F-Measure, False Accept Rate and False Reject Rate.…”

Section: B Performance Measuresmentioning

confidence: 99%

GAIT based Behavioral Authentication using Hybrid Swarm based Feed Forward Neural Network

Chaitanya¹,

Sekhar²

2020

IJACSA

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Authentication of appropriate users for accessing the liable gadgets exists as one among the prime theme in security models. Illegal access of gadgets such as smart phones, laptops comes with an uninvited consequences, such as data theft, privacy breakage and a lot more. Straight forward approaches like pattern based security, password and pin based security are quite expensive in terms of memory where the user has to keep remembering the passwords and in case of any security issue risen then the password has to be changed and once again keep remembering the recent one. To avoid these issues, in this paper an effective GAIT based model is proposed with the hybridization of Artificial Neural Network model namely Feedforward Neural Network Model with Swarm based algorithm namely Krill Herd optimization algorithm (KH). The task of KH is to optimize the weight factor of FNN which leads to the convergence of optimal solution at the end of the run. The proposed model is examined with 6 different performance measures and compared with four different existing classification model. The performance analysis shows the significance of proposed model when compared with the existing algorithms.

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Implementation of machine learning algorithms for gait recognition

Cited by 57 publications

References 4 publications

Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges

Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges

Privacy protected user identification using deep learning for smartphone-based participatory sensing applications

GAIT based Behavioral Authentication using Hybrid Swarm based Feed Forward Neural Network

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