Implementation of machine learning algorithms for gait recognition
TLDR
This article explores machine learning techniques for user authentication on HugaDB database which is a human gait data collection for analysis and activity recognition (Chereshnev and Kertesz-Farkas, 2017).About:
This article is published in Engineering Science and Technology, an International Journal.The article was published on 2020-08-01 and is currently open access. It has received 36 citations till now. The article focuses on the topics: Gait (human) & Activity recognition.read more
Citations
More filters
Journal ArticleDOI
Multi-modal gait: A wearable, algorithm and data fusion approach for clinical and free-living assessment
TL;DR: The utilisation of the fusion approach presented here warrants further investigation in those with neurological conditions, which could significantly contribute to the current understanding of impaired gait.
Journal ArticleDOI
Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges
TL;DR: A holistic understanding of an individual’s health and well-being can be obtained without interrupting day-to-day activities by developing a system that is capable of measuring parameters such as PPD, gait characteristics, foot temperature and heart rate.
Journal ArticleDOI
KinectGaitNet: Kinect-Based Gait Recognition Using Deep Convolutional Neural Network
TL;DR: The proposed KinectGaitNet is the first, to the best of the authors' knowledge, deep learning-based architecture that is based on a unique 3D input representation of joint coordinates that achieves performance higher than previous traditional and deep learning methods, with fewer parameters and shorter inference time.
Journal ArticleDOI
Privacy protected user identification using deep learning for smartphone-based participatory sensing applications
TL;DR: A deep convolution neural network model is proposed for the user identification with accelerometer data generated from users smartphone sensors, and it is observed that the proposed model achieves better results as compared to the baseline methods.
Journal ArticleDOI
Handover management over dual connectivity in 5G technology with future ultra-dense mobile heterogeneous networks: A review
TL;DR: In this paper , a comprehensive review of handover management in future mobile ultra-dense HetNets is presented to highlight their contribution in providing seamless connection during user mobility.
References
More filters
Journal ArticleDOI
A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs
TL;DR: Experimental results show that this first work based on deep CNNs for gait recognition in the literature outperforms the previous state-of-the-art methods by a significant margin, and shows great potential for practical applications.
Journal ArticleDOI
IDNet: Smartphone-based gait recognition with convolutional neural networks
Matteo Gadaleta,Michele Rossi +1 more
TL;DR: IDNet is the first system that exploits a deep learning approach as universal feature extractors for gait recognition, and that combines classification results from subsequent walking cycles into a multi-stage decision making framework.
Proceedings ArticleDOI
Siamese neural network based gait recognition for human identification
TL;DR: A Siamese neural network based gait recognition framework to automatically extract robust and discriminative gait features for human identification that impressively outperforms state-of-the-arts models.
Journal ArticleDOI
IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion.
TL;DR: This paper designs a deep convolutional neural network (DCNN) learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discrim inative fashion, and presents two methods for early and late multi-sensor fusion to improve the gait identification generalization performance.
Proceedings ArticleDOI
Performance analysis of machine learning and pattern recognition algorithms for Malware classification
TL;DR: This paper visualize viruses in an image as they capture minor changes while retaining a global structure and implements Principal Component Analysis (PCA) method for feature extraction and studies the performance of various Artificial Neural Network algorithms along with K-Nearest Neighbors and Support Vector Machine classification techniques for identification of malware data into their respective classes.
Related Papers (5)
Deep Learning-Based Gait Recognition Using Smartphones in the Wild
An Analysis of Different Approaches to Gait Recognition Using Cell Phone Based Accelerometers
Muhammad Muaaz,Rene Mayrhofer +1 more