Investigation on Human Activity Recognition using Deep Learning

Velliangiri, S.; Joseph, Iwin Thankumar; M, Maravarman; Karthikeyan, P.

doi:10.1016/j.procs.2022.08.009

Cited by 8 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wearable devices can collect data on various parameters such as heart rate, speed, distance, and acceleration. This data can be used to monitor and analyze an athlete’s performance, track their progress, and identify areas for improvement [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

A wearable-based sports health monitoring system using CNN and LSTM with self-attentions

Wang,

Cui,

Fan

2023

PLoS ONE

View full text Add to dashboard Cite

Sports performance and health monitoring are essential for athletes to maintain peak performance and avoid potential injuries. In this paper, we propose a sports health monitoring system that utilizes wearable devices, cloud computing, and deep learning to monitor the health status of sports persons. The system consists of a wearable device that collects various physiological parameters and a cloud server that contains a deep learning model to predict the sportsperson’s health status. The proposed model combines a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and self-attention mechanisms. The model is trained on a large dataset of sports persons’ physiological data and achieves an accuracy of 93%, specificity of 94%, precision of 95%, and an F1 score of 92%. The sports person can access the cloud server using their mobile phone to receive a report of their health status, which can be used to monitor their performance and make any necessary adjustments to their training or competition schedule.

show abstract

Section: Introductionmentioning

confidence: 99%

A wearable-based sports health monitoring system using CNN and LSTM with self-attentions

Wang,

Cui,

Fan

2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…However, there might still be a few problems. To begin with, CNN is not equivalent to an affine transformation [12]. Additionally, spatial information in picture data can be removed by downsampling on the pooling layer [13] [14].…”

Section: Introductionmentioning

confidence: 99%

Distracted driver behavior recognition using modified capsule networks

Kadar,

Dewi,

Suryawati

et al. 2023

J. Mechatron. Electr. Power Veh. Technol.

View full text Add to dashboard Cite

Human activity recognition (HAR) is an increasingly active study field within the computer vision community. In HAR, driver behavior can be detected to ensure safe travel. Detect driver behaviors using a capsule network with leave-one-subject-out validation. The study was done using CapsNet with leave-one-subject-out validation to identify driving habits. The proposed method in this study consists of two parts, namely encoder and decoder. The encoder used in this study modifies Sabour’s capsule network architecture by adding a convolution layer before going to the primary capsule layer. The proposed method is evaluated using a primary dataset with 10 classes and 300 images for each class. The dataset is split based on hold-out validation and leave-one-subject-out validation. The resulting models were then compared to conventional CNN architecture. The objective of the research is to identify driving behavior. In this study, the proposed method results an accuracy rate of 97.83 % in the split dataset using hold-out validation. However, the accuracy decreased by 53.11 % when the proposed method was used on a split dataset using leave-one-subject-out validation. This is because the proposed method extracts all features including the attributes of each participant contained in the input image (user-independent). Thus, the resulting model in this study tends to overfit.

show abstract

Optimizing HAR Systems: Comparative Analysis of Enhanced SVM and k-NN Classifiers

Shdefat,

Mostafa,

Al-Arnaout

et al. 2024

Int J Comput Intell Syst

View full text Add to dashboard Cite

This research addresses the accuracy issues in IoT-based human activity recognition (HAR) applications, essential for health monitoring, elderly care, gait analysis, security, and Industry 5.0. This study uses 12 machine learning approaches, split equally between support vector machine (SVM) and k-nearest neighbor (k-NN) models. Data from 102 individuals, aged 18–43, were used to train and test these models. The researchers aimed to detect twelve daily activities, such as sitting, walking, and cycling. Results showed k-NN models achieved slightly higher accuracy (97.08%) compared to SVM models (95.88%), though SVM had faster processing times. The improved machine learning approaches proved effective in accurately classifying daily activities, with k-NN models outperforming SVM models marginally. The paper provides significant contributions to the field of HAR by enhancing the performance of SVM and k-NN classifiers, optimizing them for higher accuracy and faster processing. Through robust testing with samples of real-world data, the study provides a detailed comparative analysis that highlights strengths and weaknesses of each classifier model, specifically within IoT-based systems. This work not only advances the theoretical understanding and practical applications of HAR systems in areas, such as healthcare and industrial automation, but also sets the stage for future research that could explore hybrid models or further enhancements, consequently improving the efficiency and functionality of IoT devices based on activity recognition.

show abstract

Investigation on Human Activity Recognition using Deep Learning

Cited by 8 publications

References 17 publications

A wearable-based sports health monitoring system using CNN and LSTM with self-attentions

A wearable-based sports health monitoring system using CNN and LSTM with self-attentions

Distracted driver behavior recognition using modified capsule networks

Optimizing HAR Systems: Comparative Analysis of Enhanced SVM and k-NN Classifiers

Contact Info

Product

Resources

About