Human Activity Recognition Research Articles

Changing attitudes through information exposure: Experimental evidence on reducing agricultural burning in rural Nigeria

AbstractThis study investigates the effectiveness of an education intervention that targets low‐income farmers regarding the consequences of agricultural burning. Agricultural burning is a major contributor to carbon emissions, second only to those from the energy sector. Using three treatment arms and a control group, I provided rural farmers in Nigeria with information on the social (health impacts, wildfire spread, biodiversity loss) and economic (income implications) consequences of agricultural burning. The third group received information that combined aspects from the first and second treatments, offering a comprehensive view of the effects of agricultural burning. The analysis suggests that exposure to this information prompts farmers to view agricultural burning as unacceptable, reducing their likelihood of engaging in this practice during the upcoming planting season. In addition, exposed farmers demonstrate a higher willingness to pay nominal fees for government services, such as weed disposal. This outcome demonstrates their support for sustainable farming practices, rather than resorting to burning. The influence of economic information is particularly noteworthy; farmers exposed to it demonstrate a higher willingness to pay such fees. Furthermore, the interventions led to a shift in farmers’ perceptions regarding the primary drivers of climate change, with increased recognition of human activity as the dominant factor. The study highlights how the gender of the farmer and their religious beliefs influence attitudes towards environmental protection. Specifically, male and less religious farmers tend to be more responsive to the interventions. These findings provide valuable insights into the effectiveness of “low‐cost” educational interventions in promoting sustainable agricultural practices among low‐income communities.

Unsupervised deep domain adaptation algorithm for video based human activity recognition via recurrent neural networks

The integration of cyber-physical systems and artificial intelligent human activity recognition (HAR) applications enables intelligent interactions within a physical environment. In real-world HAR applications, the domain shift between training (source) and testing (target) images captured in different scenarios leads to low classification accuracy. Existing unsupervised domain adaptation (UDA) methods often require some labeled target samples for model adaptation, which limits their practicality. This study proposes a novel unsupervised deep domain adaptation algorithm (UDDAA) for HAR using recurrent neural networks. UDDAA introduces a maximum mean class discrepancy (MMCD) metric, accounting for both inter-class and intra-class differences within each domain. MMCD extends the maximum mean discrepancy to measure the class-level distribution discrepancy across source and target domains, aligning these distributions to enhance domain adaptation performance. Without relying on labeled target data, UDDAA predicts pseudo-labels for the unlabeled target dataset, combining these with labeled source data to train the model on domain-invariant representations. This approach makes UDDAA highly practical for scenarios where labeled target data is difficult or expensive to obtain, enabling human-computer interaction (HCI) systems to function effectively across varied environments and user behaviors. Extensive experiments on benchmark datasets demonstrate UDDAA's superior classification accuracy over existing baselines. Notably, UDDAA achieved 92% and 99% accuracy for University of Central Florida database (UCF) to Human Motion Database (HMDB) and HMDB to UCF transfers, respectively. Additionally, on personal recorded videos with complex backgrounds, it achieved high classification accuracies of 95% for basketball and 90% for football activities, underscoring its generalization ability, robustness, and effectiveness.

Advancements in Real-Time Human Activity Recognition via Innovative Fusion of 3DCNN and ConvLSTM Models

Object detection (OD) is a computer vision procedure for locating objects in digital images. Our study examines the crucial need for robust OD algorithms in human activity recognition, a vital domain spanning human-computer interaction, sports analysis, and surveillance. Nowadays, three-dimensional convolutional neural networks (3DCNNs) are a standard method for recognizing human activity. Utilizing recent advances in Deep Learning (DL), we present a novel framework designed to create a fusion model that enhances conventional methods at integrates three-dimensional convolutional neural networks (3DCNNs) with Convolutional Long-Short-Term Memory (ConvLSTM) layers. Our proposed model focuses on utilizing the spatiotemporal features innately present in video streams. An important aspect often missed in existing OD methods. We assess the efficacy of our proposed architecture employing the UCF-50 dataset, which is well-known for its different range of human activities. In addition to designing a novel deep-learning architecture, we used data augmentation techniques that expand the dataset, improve model robustness, reduce overfitting, extend dataset size, and enhance performance on imbalanced data. The proposed model demonstrated outstanding performance through comprehensive experimentation, achieving an impressive accuracy of 98.11% in classifying human activity. Furthermore, when benchmarked against state-of-the-art methods, our system provides adequate accuracy and class average for 50 activity categories.

A Robust Deep Feature Extraction Method for Human Activity Recognition Using a Wavelet Based Spectral Visualisation Technique.

Human Activity Recognition (HAR), alongside Ambient Assisted Living (AAL), are integral components of smart homes, sports, surveillance, and investigation activities. To recognize daily activities, researchers are focusing on lightweight, cost-effective, wearable sensor-based technologies as traditional vision-based technologies lack elderly privacy, a fundamental right of every human. However, it is challenging to extract potential features from 1D multi-sensor data. Thus, this research focuses on extracting distinguishable patterns and deep features from spectral images by time-frequency-domain analysis of 1D multi-sensor data. Wearable sensor data, particularly accelerator and gyroscope data, act as input signals of different daily activities, and provide potential information using time-frequency analysis. This potential time series information is mapped into spectral images through a process called use of 'scalograms', derived from the continuous wavelet transform. The deep activity features are extracted from the activity image using deep learning models such as CNN, MobileNetV3, ResNet, and GoogleNet and subsequently classified using a conventional classifier. To validate the proposed model, SisFall and PAMAP2 benchmark datasets are used. Based on the experimental results, this proposed model shows the optimal performance for activity recognition obtaining an accuracy of 98.4% for SisFall and 98.1% for PAMAP2, using Morlet as the mother wavelet with ResNet-101 and a softmax classifier, and outperforms state-of-the-art algorithms.

A human activity recognition method based on Vision Transformer

Human activity recognition has a wide range of applications in various fields, such as video surveillance, virtual reality and human–computer intelligent interaction. It has emerged as a significant research area in computer vision. GCN (Graph Convolutional networks) have recently been widely used in these fields and have made great performance. However, there are still some challenges including over-smoothing problem caused by stack graph convolutions and deficient semantics correlation to capture the large movements between time sequences. Vision Transformer (ViT) is utilized in many 2D and 3D image fields and has surprised results. In our work, we propose a novel human activity recognition method based on ViT (HAR-ViT). We integrate enhanced AGCL (eAGCL) in 2s-AGCN to ViT to make it process spatio-temporal data (3D skeleton) and make full use of spatial features. The position encoder module orders the non-sequenced information while the transformer encoder efficiently compresses sequence data features to enhance calculation speed. Human activity recognition is accomplished through multi-layer perceptron (MLP) classifier. Experimental results demonstrate that the proposed method achieves SOTA performance on three extensively used datasets, NTU RGB+D 60, NTU RGB+D 120 and Kinetics-Skeleton 400.

Hybrid Approach Using Machine Learning and IOT for Soldier Rescue : A Review

Despite the well-established influence of various factors on a soldier's burden – including environment, physical exertion, equipment design, and mental stress – our current understanding is largely based on studies conducted in controlled lab settings, focusing solely on the weight of carried equipment. This limited scope hinders a comprehensive picture of how these combined burdens impact a soldier's ability to survive on the battlefield, encompassing factors like performance, health, and vulnerability to enemy attacks. To bridge this gap and gain a more holistic understanding, field-based methods for capturing soldier movement are crucial. In this vein, we've developed a novel human activity recognition system. Trained using data collected from a single sensor placed on a soldier's upper back, the system can identify eleven distinct tactical movement patterns commonly employed by soldiers in the field. This advancement paves the way for a more nuanced understanding of how various burden factors interact and influence a soldier's effectiveness and safety in real-world scenarios. Using K- Nearest Neighbour, SVM Classifier, Logistic Regression, Naïve Bayer algorithms real-world constraints are forced, and class labels are expanded. This project is based on health monitoring and tracking system for soldiers. The proposed system can be mounted on the soldier’s jacket to track their health status and current locating using GPS. This information will be transmitted to the control room through IOT and ML. The proposed system comprises of tiny wearable physiological equipment’s, sensors, transmission modules. Hence, with the use of the proposed equipment, it is possible to implement a low-cost mechanism to protect the valuable human life on the battlefield. It also includes about securing of data of soldiers in the cloud. This new method offers a powerful tool for military leaders and scientists. By collecting real-world data on soldier burden, it allows them to quantify the complex factors affecting soldier performance (the tradespace). This data acts as valuable pre-processing for other technologies, ultimately enabling data-driven decisions to optimize soldier well-being, minimize risk, and maximize mission success.

An unsupervised statistical representation learning method for human activity recognition

An unsupervised statistical representation learning method for human activity recognition

Multimodal Human Action Recognition for Rehabilitation Exercise of Upper Body for Individuals With Cerebral Palsy

Multimodal Human Action Recognition for Rehabilitation Exercise of Upper Body for Individuals With Cerebral Palsy

MXene-Based Dual Network Hydrogel as Flexible Strain Sensor for Human Actions Recognition

MXene-Based Dual Network Hydrogel as Flexible Strain Sensor for Human Actions Recognition

Human Action Recognition using Long Short-Term Memory and Convolutional Neural Network Model

Human Action Recognition (HAR) is the difficulty of quickly identifying strenuous exercise performed by people. It is feasible to sample some measures of a body's tangential acceleration and speed using inertial sensors and exercise them only to learn model skills of incorrectly categorizing behavior into the relevant categories. In detecting human activities, the use of detectors in personal and portable devices has increased to better understand and anticipate human behavior. Many specialists are working toward developing a classification that can distinguish between a user's behavior and uncooked data while utilizing as few reserves as possible. A Long-term Recurrent Convolutional Network (LRCN) is proposed as a comprehensive human action recognition system based on deep neural networks in this paper.

Human Activity Recognition for Elderly People Using Pseudonym-Based Conditional Privacy-Preservation Authentication

Human Activity Recognition for Elderly People Using Pseudonym-Based Conditional Privacy-Preservation Authentication

WiFi CSI-based Human Activity Recognition Using Multiple Segment Signals

WiFi CSI-based Human Activity Recognition Using Multiple Segment Signals

MediaPipe with GNN for Human Activity Recognition

Human interaction and computer vision converge in the realm of Human Activity Recognition (HAR), which is a research field dedicated to the creation of automated systems capable of observing and categorizing human activities. This domain closely aligns with machine learning, involving the development of algorithms and models adept at learning to recognize and classify patterns within data. HAR typically unfolds in two pivotal phases: data acquisition and processing, followed by activity classification. In the initial phase of data acquisition and processing, information is gathered from various sensors or video sources, such as accelerometers, smartphones, or smartwatches. Subsequently, the collected data undergo preprocessing to extract relevant features. The subsequent phase, activity classification, employs machine learning algorithms to categorize these extracted features into distinct activity types, ranging from walking and running to sitting. This paper introduces an innovative approach grounded on these two fundamental phases. For the first phase, we leverage the MediaPipe algorithm to discern human articulations. Once these poses are detected, we contribute by extracting the coordinates of each articulation. These coordinates are then transformed into graphs, where nodes signify the articulation coordinates and edges represent the connections between them. In the second phase, we enhance existing methodologies by incorporating a diverse set of machine learning models. Notably, the utilization of Graph Neural Networks (GNNs) which stands out as a significant advancement. This choice proves instrumental in effectively learning and representing complex spatial and temporal patterns, surpassing the limi-tations of conventional machine learning algorithms. The developed system undergoes evaluation on the KTH and UCF50 datasets, demonstrating state-of-the-art performance in HAR.

Editorial

Dear Readers, It gives me great pleasure to announce the sixth regular issue of 2024. In this issue, 6 papers cover various topical aspects of computer science by 19 authors from 7 countries: Brazil, Chile, France, India, Saudi Arabia, Tunisia, and Turkey. In an ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. As always, I would like to thank all authors for their sound research and the editorial board and our guest reviewers for their extremely valuable review effort and suggestions for improvement. These contributions, together with the generous support of the consortium members, help to maintain the quality of our journal.  In this regular issue, I am very pleased to introduce the following 6 accepted articles: George Marsicano, Edna Dias Canedo, Glauco V. Pedrosa, Cristiane S. Ramos, and Rejane M. da C. Figueiredo from Brazil look in their study into digital transformation of public services in a startup-based environment by 23 focus groups and 175 participants in total. Mauricio Solar and Pablo Aguirre from Chile discuss their research on 3D chest CT processes applying a ResNet-50 model to which a new dimension of information has been added, namely a simple autoencoder. In a collaborative work between researchers from Tunisia and France, Rakia Saidi, Fethi Jarray, and Didier Schwab propose in their article a cross-encoder neural network (Cross-BERT-GRU) to deal with the semantic similarity of Arabic sentences that benefits from both the strong contextual understanding of BERT and the sequential modeling capabilities of GRU. Also in a collaborative research between institutions from Tunisia and Saudi Arabia, Nozha Jlidi, Sameh Kouni, Olfa Jemai, and Tahani Bouchrika present their research on MediaPipe with GNN for human activity recognition. G.V.Vidya Lakshmi and S. Gopikrishnan from India look into missing values research for the IoT domain and in particular present IMD-MP technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations. Last but not least, F. Didem Alay, Nagehan İlhan, and M. Tahir Güllüoğlu address in their article a comparative study of data mining methods for solar radiation and temperature forecasting models. Enjoy Reading Cordially,  Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria

Insights into aerial intelligence: assessing CNN-based algorithms for human action recognition and object detection in diverse environments

Insights into aerial intelligence: assessing CNN-based algorithms for human action recognition and object detection in diverse environments

Wearable sensors based on artificial intelligence models for human activity recognition.

Human motion detection technology holds significant potential in medicine, health care, and physical exercise. This study introduces a novel approach to human activity recognition (HAR) using convolutional neural networks (CNNs) designed for individual sensor types to enhance the accuracy and address the challenge of diverse data shapes from accelerometers, gyroscopes, and barometers. Specific CNN models are constructed for each sensor type, enabling them to capture the characteristics of their respective sensors. These adapted CNNs are designed to effectively process varying data shapes and sensor-specific characteristics to accurately classify a wide range of human activities. The late-fusion technique is employed to combine predictions from various models to obtain comprehensive estimates of human activity. The proposed CNN-based approach is compared to a standard support vector machine (SVM) classifier using the one-vs-rest methodology. The late-fusion CNN model showed significantly improved performance, with validation and final test accuracies of 99.35 and 94.83% compared to the conventional SVM classifier at 87.07 and 83.10%, respectively. These findings provide strong evidence that combining multiple sensors and a barometer and utilizing an additional filter algorithm greatly improves the accuracy of identifying different human movement patterns.

Deep Learning for Skeleton-Based Human Activity Segmentation: An Autoencoder Approach

Automatic segmentation is essential for enhancing human activity recognition, especially given the limitations of publicly available datasets that often lack diversity in daily activities. This study introduces a novel segmentation method that utilizes skeleton data for a more accurate and efficient analysis of human actions. By employing an autoencoder, this method extracts representative features and reconstructs the dataset, using the discrepancies between the original and reconstructed data to establish a segmentation threshold. This innovative approach allows for the automatic segmentation of activity datasets into distinct segments. Rigorous evaluations against ground truth across three publicly available datasets demonstrate the method’s effectiveness, achieving impressive average annotation error, precision, recall, and F1-score values of 3.6, 90%, 87%, and 88%, respectively. This illustrates the robustness of the proposed method in accurately identifying change points and segmenting continuous skeleton-based activities as compared to two other state-of-the-art techniques: one based on deep learning and another using the classical time-series segmentation algorithm. Additionally, the dynamic thresholding mechanism enhances the adaptability of the segmentation process to different activity dynamics improving overall segmentation accuracy. This performance highlights the potential of the proposed method to significantly advance the field of human activity recognition by improving the accuracy and efficiency of identifying and categorizing human movements.

A Comparative Analysis of XGBoost and LightGBM Approaches for Human Activity Recognition: Speed and Accuracy Evaluation

A Comparative Analysis of XGBoost and LightGBM Approaches for Human Activity Recognition: Speed and Accuracy Evaluation

Enhanced Depth Motion Maps for Improved Human Action Recognition from Depth Action Sequences

Enhanced Depth Motion Maps for Improved Human Action Recognition from Depth Action Sequences

Prediction of Human Physiological States by Using an Enhanced Recursive Feature Elimination Method

Human Action Recognition (HAR) is a vital part of the healthcare sector. Medical practitioners are experiencing difficulty in recognizing human physiological conditions due to the enormous quantity of sensory stimulation. Machine learning techniques help to predict human physiological conditions, providing medical practitioners to work more efficiently. Feature selection is an essential part of discovering new knowledge in the majority of real-world problems when there are a lot of features. Feature selection is extremely useful because it speeds up decisions and enhances classification performance. The importance of feature selection in machine learning is dimension reduction in a massive multivariate data collection. This paper presents an effective feature selection method known as the Enhanced Recursive Feature Elimination (EFRE) for selecting key features from the data set for HAR prediction. The experimental results reveal that the ERFE technique selects the most suitable features for HAR prediction. The performance of the proposed ERFE approach is tested using different performance evaluation metrics. The performance analysis shows that the ERFE method outperforms existing feature selection methods with 88% accuracy.