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Background: The ActiPASS software was developed from the open-source Acti4 activity classification algorithm for thigh-worn accelerometry. However, the original algorithm has not been validated in children or compared with a child-specific set of algorithm thresholds. This study aims to evaluate the accuracy of ActiPASS in classifying activity types in children using 2 published sets of Acti4 thresholds. Methods: Laboratory and free-living data from 2 previous studies were used. The laboratory condition included 41 school-aged children (11.0 [4.8] y; 46.5% male), and the free-living condition included 15 children (10.0 [2.6] y; 66.6% male). Participants wore a single accelerometer on the dominant thigh, and annotated video recordings were used as a reference. Postures and activity types were classified with ActiPASS using the original adult thresholds and a child-specific set of thresholds. Results: Using the original adult thresholds, the mean balanced accuracy (95% CI) for the laboratory condition ranged from 0.62 (0.56–0.67) for lying to 0.97 (0.94–0.99) for running. For the free-living condition, accuracy ranged from 0.61 (0.48–0.75) for lying to 0.96 (0.92–0.99) for cycling. Mean balanced accuracy for overall sedentary behavior (sitting and lying) was ≥0.97 (0.95–0.99) across all thresholds and conditions. No meaningful differences were found between the 2 sets of thresholds, except for superior balanced accuracy of the adult thresholds for walking under laboratory conditions. Conclusions: The results indicate that ActiPASS can accurately classify different basic types of physical activity and sedentary behavior in children using thigh-worn accelerometer data. [ABSTRACT FROM AUTHOR]

Traditional systems for indoor pressure sensing and human activity recognition (HAR) rely on costly, high-resolution mats and computationally intensive neural network-based (NN-based) models that are prone to noise. In contrast, we design a cost-effective and noise-resilient pressure mat system for HAR, leveraging Velostat for intelligent pressure sensing and a novel hyperdimensional computing (HDC) classifier that is lightweight and highly noise resilient. To measure the performance of our system, we collected two datasets, capturing the static and continuous nature of human movements. Our HDC-based classification algorithm shows an accuracy of 93.19%, improving the accuracy by 9.47% over state-of-the-art CNNs, along with an 85% reduction in energy consumption. We propose a new HDC noise-resilient algorithm and analyze the performance of our proposed method in the presence of three different kinds of noise, including memory and communication, input, and sensor noise. Our system is more resilient across all three noise types. Specifically, in the presence of Gaussian noise, we achieve an accuracy of 92.15% (97.51% for static data), representing a 13.19% (8.77%) improvement compared to state-of-the-art CNNs.

Accurately understanding the travel demand of urban rail transit (URT) systems is crucial for effective operational management. Despite the recognition that the diversity in human activity patterns results in different travel demands, few studies have thoroughly investigated the heterogeneity among passengers and its impact on URT ridership. This study utilizes smart card data collected from the Beijing Subway to categorize passengers into four groups: tourist passengers, flexible commuters, regular commuters, and life‐oriented passengers, based on their spatiotemporal travel patterns. Furthermore, a Multiscale Geographically Weighted Regression (MGWR) model is employed to examine the relationship between station‐level ridership of URT and its determinants, including the built environment and station properties, for each passenger group. The results indicate that the influence of these determinants on station‐level ridership varies across passenger groups and spatial scales. For instance, regular commuters exhibit lower sensitivity to accessibility on workdays, whereas those unfamiliar with the URT network are more concerned about the bus accessibility in pedestrian‐ or bicycle‐unfriendly areas. Notably, for tourist and life‐oriented passengers, the stations significantly affected by population density are concentrated in areas with a higher proportion of elderly individuals. Conversely, for flexible and regular commuters, these stations are predominantly situated in areas associated with internet technology and scientific research. These findings are valuable for policymakers in designing strategies tailored to different passenger groups to balance trip demand and capacity, thereby improving URT services and promoting a sustainable urban environment. [ABSTRACT FROM AUTHOR]

Abnormal behavior methods have attempted to reduce execution time, computational complexity, efficiency, robustness against pixel occlusion, and generalizability. This research proposed a novel method in human activity-based anomaly detection and recognition by surveillance video utilizing DL methods. Input is collected as video and processed for noise removal and smoothening. Then kernel local component analysis extracts these video features for human activity monitoring. Then the extracted features are classified using Bayesian network-based spatiotemporal neural networks. The classified output shows the anomaly activities of the selected input surveillance video dataset. The simulation results are obtained for various crowd datasets regarding the mean average error, mean square error, training accuracy, validation accuracy, specificity, and F_measure. The proposed technique attained an MAE of 58%, MSE of 63%, specificity of 89%, and F-measure of 68%. training and validation accuracy of 92% and 96% respectively. [ABSTRACT FROM AUTHOR]

The advancements in intelligent action recognition can be instrumental in developing autonomous robotic systems capable of analyzing complex human activities in real-time, contributing to the growing field of robotics that operates in dynamic environments. The precise recognition of basketball players' actions using artificial intelligence technology can provide valuable assistance and guidance to athletes, coaches, and analysts, and can help referees make fairer decisions during games. However, unlike action recognition in simpler scenarios, the background in basketball is similar and complex, the differences between various actions are subtle, and lighting conditions are inconsistent, making action recognition in basketball a challenging task. To address this problem, an Adaptive Context-Aware Network (ACA-Net) for basketball player action recognition is proposed in this paper. It contains a Long Short-term Adaptive (LSTA)module and a Triplet Spatial-Channel Interaction (TSCI)module to extract effective features at the temporal, spatial, and channel levels. The LSTA module adaptively learns global and local temporal features of the video. The TSCI module enhances the feature representation by learning the interaction features between space and channels. We conducted extensive experiments on the popular basketball action recognition datasets SpaceJam and Basketball-51. The results show that ACA-Net outperforms the current mainstream methods, achieving 89.26% and 92.05% in terms of classification accuracy on the two datasets, respectively. ACA-Net's adaptable architecture also holds potential for real-world applications in autonomous robotics, where accurate recognition of complex human actions in unstructured environments is crucial for tasks such as automated game analysis, player performance evaluation, and enhanced interactive broadcasting experiences. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method's potential benefits and implications for HRC. [ABSTRACT FROM AUTHOR]

To create an effective Ambient Assisted Living (AAL) system that supports the daily activities of patients or the elderly, it is crucial to accurately detect and differentiate user actions to determine the necessary assistance. Traditional intrusive methods, such as wearable or object-attached devices, can interfere with the natural behavior of patients and may lead to resistance. Furthermore, non-intrusive systems that rely on video or sound data processed by servers or the cloud can generate excessive data traffic and raise concerns about the security of personal information. In this study, we developed an edge-based real-time system for detecting Activities of Daily Living (ADL) using ambient noise. Additionally, we introduced an online post-processing method to enhance classification performance and extract activity events from noisy sound in resource-constrained environments. The system, tested with data collected in a living space, achieved high accuracy in classifying ADL-related behaviors in continuous events and successfully generated user activity logs from time-series sound data, enabling further analyses such as ADL assessments. Future work will focus on enhancing detection accuracy and expanding the range of detectable behaviors by integrating the activity logs generated in this study with additional data sources beyond sound. [ABSTRACT FROM AUTHOR]

This paper investigates the feasibility of cross-domain recognition for human activities captured using low-resolution 8 × 8 infrared sensors in indoor environments. To achieve this, a novel prototype recurrent convolutional network (PRCN) was evaluated using a few-shot learning strategy, classifying up to eleven activity classes in scenarios where one or two individuals engaged in daily tasks. The model was tested on two independent datasets, with real-world measurements. Initially, three different networks were compared as feature extractors within the prototype network. Following this, a cross-domain evaluation was conducted between the real datasets. The results demonstrated the model's effectiveness, showing that it performed well regardless of the diversity of samples in the training dataset. [ABSTRACT FROM AUTHOR]

Automating tasks through human activity recognition in video data has become increasingly vital. Deep learning has yielded versatile activity recognition systems applicable in surveillance, healthcare analysis, sports, and human–computer interaction. Despite various proposed video-based activity recognition techniques over the years, the reliance over RGB frames, accompanied by other modalities like joint locations and depth maps, often proves less effective compared to multimodal methods. In response to this challenge, our paper introduces a competitive approach for identifying human activity in video frames. Leveraging a Convolutional Long Short-Term Memory (Conv-LSTM) network and a novel pre-processing step involving a Human Segmentation network, our method accentuates human subjects in each frame using segmentation maps. These highlighted frames undergo further processing through Convolutional Neural Networks (CNNs) to learn feature vectors, without including other modalities with RGB frames directly. The learned features are then subjected to Long Short-Term Memory (LSTM) units for comprehending sequential video data and drawing meaningful inferences. The proposed methodology undergoes rigorous testing on three publicly available datasets—KARD, MSR Daily Activity, and SBU-Interactions. Remarkably, our approach outperforms similar state-of-the-art methods, achieving benchmark accuracy scores exceeding 98% on MSR Daily Activity and 99% on KARD and SBU-Interactions datasets. In essence, our method not only provides a competitive solution for human activity recognition in video frames but also contributes to advancing the field by integrating Conv-LSTM networks and innovative pre-processing techniques. The comprehensive evaluation on multiple datasets underlines the robustness and superior performance of our proposed approach. [ABSTRACT FROM AUTHOR]

Human activity recognition(HAR) is an important research focus in ubiquitous computing. It has been widely applied in various domains, such as smart homes, healthcare assistance, and sports training. Accurate human activity recognition directly impacts the performance of downstream tasks. Existing methods for human activity recognition primarily rely on extracting temporal or spatial features from the data. These features are used for the task of human activity recognition. The existing methods mainly use CNN or RNN models to model the Euclidean correlations among spatially adjacent sensors or channels. However, non-Euclidean pairwise correlations among all sensors or channels are even critical for accurate classification, which has been ignored by the existing methods. In this paper, we incorporate fine-grained spatial structural data into the model to overcome these limitations. A novel deep learning model for human activity recognition is proposed, which is called the fine-grained data-oriented Spatial-Temporal Graph Transformer network (STGT). The introduction of the STGT model can eliminate the limitations of existing spatial feature extraction methods by leveraging a novel data organization approach proposed in this study. This model enhances the spatial features within the time feature extraction module for effective spatio-temporal feature extraction. We conducted experiments on four large-scale real-world HAR datasets to evaluate its performance. The experimental results demonstrate the superiority of our method over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Human Interaction Recognition (HIR) was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements. HIR requires more sophisticated analysis than Human Action Recognition (HAR) since HAR focuses solely on individual activities like walking or running, while HIR involves the interactions between people. This research aims to develop a robust system for recognizing five common human interactions, such as hugging, kicking, pushing, pointing, and no interaction, from video sequences using multiple cameras. In this study, a hybrid Deep Learning (DL) and Machine Learning (ML) model was employed to improve classification accuracy and generalizability. The dataset was collected in an indoor environment with four-channel cameras capturing the five types of interactions among 13 participants. The data was processed using a DL model with a fine-tuned ResNet (Residual Networks) architecture based on 2D Convolutional Neural Network (CNN) layers for feature extraction. Subsequently, machine learning models were trained and utilized for interaction classification using six commonly used ML algorithms, including SVM, KNN, RF, DT, NB, and XGBoost. The results demonstrate a high accuracy of 95.45% in classifying human interactions. The hybrid approach enabled effective learning, resulting in highly accurate performance across different interaction types. Future work will explore more complex scenarios involving multiple individuals based on the application of this architecture. [ABSTRACT FROM AUTHOR]

Analyzing physical activities through wearable devices is a promising research area for improving health assessment. This research focuses on the development of an affordable and real-time Human Activity Recognition (HAR) system designed to operate on low-performance microcontrollers. The system utilizes data from a body-worn accelerometer to recognize and classify human activities, providing a cost-effective, easy-to-use, and highly accurate solution. A key challenge addressed in this study is the execution of efficient motion recognition within a resource-constrained environment. The system employs a Random Forest (RF) classifier, which outperforms Gradient Boosting Decision Trees (GBDT), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) in terms of accuracy and computational efficiency. The proposed features Average absolute deviation (AAD), Standard deviation (STD), Interquartile range (IQR), Range, and Root mean square (RMS). The research has conducted numerous experiments and comparisons to establish optimal parameters for ensuring system effectiveness, including setting a sampling frequency of 50 Hz and selecting an 8-s window size with a 40% overlap between windows. Validation was conducted on both the WISDM public dataset and a self-collected dataset, focusing on five fundamental daily activities: Standing, Sitting, Jogging, Walking, and Walking the stairs. The results demonstrated high recognition accuracy, with the system achieving 96.7% on the WISDM dataset and 97.13% on the collected dataset. This research confirms the feasibility of deploying HAR systems on low-performance microcontrollers and highlights the system’s potential applications in patient support, rehabilitation, and elderly care. [ABSTRACT FROM AUTHOR]

As the proportion of elderly individuals in developed countries continues to rise globally, addressing their healthcare needs, particularly in preserving their autonomy, is of paramount concern. A growing body of research focuses on Ambient Assisted Living (AAL) systems, aimed at alleviating concerns related to the independent living of the elderly. This systematic review examines the literature pertaining to fall detection and Human Activity Recognition (HAR) for the elderly, two critical tasks for ensuring their safety when living alone. Specifically, this review emphasizes the utilization of Deep Learning (DL) approaches on computer vision data, reflecting current trends in the field. A comprehensive search yielded 2,616 works from five distinct sources, spanning the years 2019 to 2023 (inclusive). From this pool, 151 relevant works were selected for detailed analysis. The review scrutinizes the employed DL models, datasets, and hardware configurations, with particular emphasis on aspects such as privacy preservation and real-world deployment. The main contribution of this study lies in the synthesis of recent advancements in DL-based fall detection and HAR for the elderly, providing insights into the state-of-the-art techniques and identifying areas for further improvement. Given the increasing importance of AAL systems in enhancing the quality of life for the elderly, this review serves as a valuable resource for researchers, practitioners, and policymakers involved in developing and implementing such technologies. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is a technology that infers current user activities by using the available sensory data network. Research on activity recognition is considered extremely important, particularly when it comes to delivering sensitive services such as healthcare services and live tracking assistance and autonomy. For this purpose, many researchers have proposed a knowledge-driven approach or data-driven reasoning for identification techniques. However, there are multiple limitations associated with these approaches and the resulting models are typically not complete enough to capture all types of human activities. Thus, recent works have suggested combining these techniques through a hybrid model. This paper's goal is to give a brief overview of activity recognition implementation approaches by looking at various sensing technologies used to gather data from internet of things (IoT) gadgets, looking at preprocessing and feature extraction approaches, and then comparing methods used to identify human activities in smart homes, and highlighting their strengths and weaknesses across various fields. Numerous pertinent works were located, and their accomplishments were assessed. [ABSTRACT FROM AUTHOR]

The success of human activity recognition relies on the extraction of effective features, which can provide richer information for subsequent downstream tasks after embedding spatial fusion. However, due to the disruption of the original temporal relationships when integrating features from different variables, existing methods exert pressure on capturing temporal characteristics. In response to this issue, we propose a deep learning-based multi-channel architecture that combines two stages graph neural network and stacked attentional neural control differential equation method, named GANCDE. First, an adaptive graph architecture is employed to calculate similarity weights between different variables. Then, to obtain global information, multi-channel features are squeezed, and attention weights are calculated to overcome the drawbacks of graph convolution operations. This enables a more precise graph structure while eliminating redundant information. Next, the expression of the derivative of the temporal attention weight curve is obtained using tricubic interpolation and multi-scale convolution. Finally, the downstream task is completed using an N-layer convolutional architecture. Extensive experiments prove that GANCDE not only alleviates the issue of motion and non-motion features becoming similar caused by graph convolution, but also produces attention weight curves that better adhere to the continuity of actions. As a result, GANCDE achieves state-of-the-art performance in human activity recognition tasks. Specifically, on datasets such as WISDM, UCIHAR, and PAMAP2, the accuracy can reach 94.75, 93.50, and 96.37%, respectively. [ABSTRACT FROM AUTHOR]

Perception is difficult in video surveillance applications because of the presence of dynamic objects and constant environmental changes. This problem worsens when bad weather, including snow, rain, fog, dark nights, and bright daylight, interferes with the quality of perception. The proposed work aims to enhance the accuracy of camera-based perception for human activity detection in video surveillance during adverse weather conditions. To identify primary human activities, including walking on the road during severe weather, transfer learning from many adverse conditions using realtime images or videos has been proposed as an improvement for you look only once v8 (YOLOv8)-based human activity recognition in poor weather conditions. We collected and sorted training rates into frames from videos depicting human walking activity, their combined forms, and other subgroups, such as running and standing, based on their characteristics. The assessment of the detection efficiency of the previously described images and subgroups led to a comparison of the training weights. The use of realtime activity images for training greatly enhanced the detection performance when comparing the proposed test results to the existing YOLO base weights. Furthermore, a notable improvement in human activity efficiency was obtained by utilizing extra images and feature-related combinations of data techniques. [ABSTRACT FROM AUTHOR]

Human behavior recognition is the process of automatically identifying and analyzing multiple human behaviors using modern technology. From previous studies, we find that redundant features not only slow down the model training process and increase the structural complexity but also degrade the overall performance of the model. To overcome this problem, this paper investigates a temporal convolutional neural network (TCN) model based on improved sparrow search algorithm random forest (SSARF) feature selection to accurately identify human behavioral traits based on wearable devices. The model is based on the TCN classification model and incorporates a random forest with the sparrow optimization algorithm to perform dimensionality reduction on the original features, which is used to remove poorly correlated and unimportant features and retain effective features with a certain contribution rate to generate the optimal feature subset. In order to verify the reliability of the method, the performance of the model was evaluated on two public datasets, UCI Human Activity Recognition and WISDM, respectively, and 98.54% and 97.83% recognition accuracies were obtained, which were improved by 0.47% and 1.04% compared to the prefeature selection, but the number of features was reduced by 84.31% and 32.50% compared to the original feature set. In addition, we compared the TCN classification model with other deep learning models in terms of evaluation metrics such as F1 score, recall, precision, and accuracy, and the results showed that the TCN model outperformed the other control models in all four metrics. Meanwhile, it also outperforms the existing recognition methods in terms of accuracy and other aspects, which have some practical application value. [ABSTRACT FROM AUTHOR]

In order to solve the problem of low accuracy and efficiency in video human behavior recognition algorithm, a deep learning video human behavior recognition algorithm is proposed, which is based on an improved time division network. This method innovates on the classical two-stream convolutional neural network framework, and the core is to enhance the performance of the time division network by implementing the sliding window sampling technique with multiple time scales. This sampling strategy not only effectively integrates the full time-series information of the video, but also accurately captures the long-term dependencies hidden in human behavior, which further improves the accuracy and efficiency of behavior recognition. Experimental results show that the method proposed in this paper has achieved good advantages in multiple data sets. On HMDB51, our method achieves 84% recognition accuracy, while on the more complex Kinetics and UCF101 datasets, it also achieves 94% and significant recognition results, respectively. In the face of complex scenes and changeable human body structure, the proposed algorithm shows excellent robustness and stability. In terms of real-time, it can meet the high requirements of real-time video processing. Through the validation of experimental data, our method has made significant progress in extracting spatiotemporal features, capturing long-term dependencies, and focusing on key information. [ABSTRACT FROM AUTHOR]

Human Activity Recognition models have potential to contribute to valuable and detailed knowledge of habitual physical activity for typically developing children and children with Cerebral Palsy. The main objective of the present study was to develop and validate two Human Activity Recognition models. One trained on data from typically developing children (n = 63), the second also including data from children with Cerebral Palsy (n = 16), engaging in standardised activities and free play. Our data was collected using accelerometers and ground truth was established with video annotations. Additionally, we aimed to investigate the influence of window settings on model performance. Utilizing the Extreme gradient boost (XGBoost) classifier, twelve sub-models were created, with 1-,3- and 5-seconds windows, with and without overlap. Both Human Activity Recognition models demonstrated excellent predictive capabilities (>92%) for standardised activities for both typically developing and Cerebral Palsy. From all window sizes, the 1-second window performed best for all test groups. Accuracy was slightly lower (>75%) for the Cerebral Palsy test group performing free play activities. The impact of window size and overlap varied depending on activity. In summary both Human Activity Recognition models effectively predict standardised activities, surpassing prior models for typically developing and children with Cerebral Palsy. Notably, the model trained on combined typically developing children and Cerebral Palsy data performed exemplary across all test groups. Researchers should select window settings aligned with their specific research objectives. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) deals with the automatic recognition of physical activities and plays a crucial role in healthcare and sports where wearable sensors and intelligent computational techniques are used. We propose a HAR algorithm that uses the smartphones accelerometer data for human activity recognition. In particular, we present a recurrent convolutional neural network-based HAR algorithm that combines a Convolutional Neural Network (CNN) to extract temporal features from the sensor data, a Fuzzy C-Means (FCM) clustering algorithm to cluster the features extracted by the CNN, and a Long Short-Term Memory (LSTM) network to learn the temporal dependencies between the features. We evaluate the proposed methodology on two distinct datasets: the MotionSense dataset and the WISDM dataset. We evaluate the proposed CNN-FCM-LSTM model on the publicly available MotionSense dataset to classify ten activity types: 1) walking upstairs, 2) walking downstairs, 3) jogging, 4) sitting, 5) standing, 6) level ground walking, 7) jumping jacks, 8) brushing teeth, 9) writing, and 10) eating. Next, we evaluate the model's performance on the WISDM dataset to assess its ability to generalize to unseen data. On the MotionSense test dataset, CNN-FCM-LSTM achieves a classification accuracy of 99.69%, a sensitivity of 99.62%, a specificity of 99.63%, and a false positive rate per hour (FPR/h) of 0.37%. Meanwhile, it achieves a classification accuracy of 97.27% on the WISDM dataset. The CNN-FCM-LSTM model's capability to classify a diverse range of activities within a single architecture is noteworthy. The results suggest that the proposed CNN-FCM-LSTM model using smartphone inputs is more accurate, reliable, and robust in detecting and classifying activities than the state-of-the-art models. It should be noted that activity recognition technology has the potential to aid in studying the underpinnings of physical activity, designing more effective training regimens, and simulating the rigors of competition in sports. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) has grown more important in the domains of pervasive computing, human behavior analysis, assistive health care, and Human–Computer Interaction (HCI). While Deep Learning (DL)-based methods have demonstrated remarkable efficiency in the identification of activities, the management of time series data remains a challenge. Nevertheless, a large number of models currently in use direct the effective extraction of temporal and geographical information from data on human behavior. In this paper, a fusion of a 1-dimensional convolutional neural network (1-DCNN) and Stacked long short-term memory (SLSTM) is proposed for HAR. This model automatically extracts geographical information from the raw body sensor data. 1-D CNN model can extract local features and the SLSTM model can extract long-term relationships in sequence data. In addition, a batch normalization layer was added after the convolution layer to reduce internal covariate shift, accelerate training, and reduce the need for dropout. Moreover, a Max Pooling Layer was applied next to the pooling layer to reduce model parameters. The final step involves applying the learned features to the Softmax layer to fully recognize human activities. The model performance is evaluated on two standard datasets MHEALTH and WISDM and achieves classification accuracies of 99.67% and 99.55% correspondingly. The results demonstrate that the recommended model outperformed the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Public space is usually conceived as where people live, perceive, and interact with other people. The environment affects people in several different ways as well. The impact of environmental problems on humans is significant, affecting all human activities, including health and socio-economic development. Thus, there is a need to rethink how space is used. Dealing with the important needs raised by climate emergency, pandemic and digitization, the contributions of this paper consist in the creation of opportunities for developing generative approaches to space design and utilization. It is proposed GREEN PATH, an intelligent expert system for space planning. GREEN PATH uses human trajectories and deep learning methods to analyse and understand human behaviour for offering insights to layout designers. In particular, a Generative Adversarial Imitation Learning (GAIL) framework hybridised with classical reinforcement learning methods is proposed. An example of the classical reinforcement learning method used is continuous penalties, which allow us to model the shape of the trajectories and insert a bias, which is necessary for the generation, into the training. The structure of the framework and the formalisation of the problem to be solved allow for the evaluation of the results in terms of generation and prediction. The use case is a chosen retail domain that will serve as a demonstrator for optimising the layout environment and improving the shopping experience. Experiments were assessed on shoppers' trajectories obtained from four different stores, considering two years. [ABSTRACT FROM AUTHOR]

Since digital media has become increasingly popular, video processing has expanded in recent years. Video processing systems require high levels of processing, which is one of the challenges in this field. Various approaches, such as hardware upgrades, algorithmic optimizations, and removing unnecessary information, have been suggested to solve this problem. This study proposes a video saliency map based method that identifies the critical parts of the video and improves the system's overall performance. Using an image registration algorithm, the proposed method first removes the camera's motion. Subsequently, each video frame's color, edge, and gradient information are used to obtain a spatial saliency map. Combining spatial saliency with motion information derived from optical flow and color-based segmentation can produce a saliency map containing both motion and spatial data. A nonlinear function is suggested to properly combine the temporal and spatial saliency maps, which was optimized using a multi-objective genetic algorithm. The proposed saliency map method was added as a preprocessing step in several Human Action Recognition (HAR) systems based on deep learning, and its performance was evaluated. Furthermore, the proposed method was compared with similar methods based on saliency maps, and the superiority of the proposed method was confirmed. The results show that the proposed method can improve HAR efficiency by up to 6.5% relative to HAR methods with no preprocessing step and 3.9% compared to the HAR method containing a temporal saliency map. [ABSTRACT FROM AUTHOR]

Wearable sensor-based human activity recognition (HAR) methods hold considerable promise for upper-level control in exoskeleton systems. However, such methods tend to overlook the critical role of data quality and still encounter challenges in cross-subject adaptation. To address this, we propose an active learning framework that integrates the relation network architecture with data sampling techniques. Initially, target data are used to fine tune two auxiliary classifiers of the pre-trained model, thereby establishing subject-specific classification boundaries. Subsequently, we assess the significance of the target data based on classifier discrepancy and partition the data into sample and template sets. Finally, the sampled data and a category clustering algorithm are employed to tune model parameters and optimize template data distribution, respectively. This approach facilitates the adaptation of the model to the target subject, enhancing both accuracy and generalizability. To evaluate the effectiveness of the proposed adaptation framework, we conducted evaluation experiments on a public dataset and a self-constructed electromyography (EMG) dataset. Experimental results demonstrate that our method outperforms the compared methods across all three statistical metrics. Furthermore, ablation experiments highlight the necessity of data screening. Our work underscores the practical feasibility of implementing user-independent HAR methods in exoskeleton control systems. [ABSTRACT FROM AUTHOR]

Current approaches to activity-assisted living (AAL) are complex, expensive, and intrusive, which reduces their practicality and end user acceptance. However, emerging technologies such as artificial intelligence and wireless communications offer new opportunities to enhance AAL systems. These improvements could potentially lower healthcare costs and reduce hospitalisations by enabling more effective identification, monitoring, and localisation of hazardous activities, ensuring rapid response to emergencies. In response to these challenges, this paper introduces the TransparentRFIDTag Wall (TRT-Wall), a novel system taht utilises a passive ultra-high frequency (UHF) radio-frequency identification (RFID) tag array combined with deep learning for contactless human activity monitoring. The TRT-Wall is tested on five distinct activities: sitting, standing, walking (in both directions), and no-activity. Experimental results demonstrate that the TRT-Wall distinguishes these activities with an impressive average accuracy of 95.6 % under four distinct distances (2, 2.5, 3.5 and 4.5 m) by capturing the RSSI and phase information. This suggests that our proposed contactless AAL system possesses significant potential to enhance elderly patient-assisted living. [ABSTRACT FROM AUTHOR]

Nowadays, video surveillance systems are gaining increasing attention in the computer vision fields due to user demands for security purposes. Observing human movement and predicting such movement perception are promising due to video surveillance systems. In this work, Hierarchical Auto-Associative Polynomial Convolutional Neural Networks (HA-PCNN) with Garran Rufa Fish Optimization (GRFO) is proposed for Human Activity Recognition (HAR) using a video surveillance system. Initially, video surveillance systems-based human activity images are obtained, which are collected by video camera by recording daily human activities. After that, the input images are fed to a pre-processing approach named as Switched Mode Fuzzy Median Filter (SMFM) method. In this, the noise presented in the input images is reduced by applying the SMFM model to normalize the dataset and improve the image quality. After that, the pre-processed images are given to the developed Fast Discrete Curvelet Transform with Wrapping (FDCT-WRP)-based feature extraction method to extract the relevant features. Moreover, the extracted features are given to the HA-PCNN model for HA classification. Generally, the HA-PCNN method does not express any adoption of optimization methods for scaling the ideal parameters and classification. Here, Garra Rufa Fish Optimization (GRFO) is proposed to enhance the parameters of HA-PCNN. Thus, the proposed HA-PCNN-GRFO methodology can classify human activities like standing, sitting, running, walking and sleeping. By then the performance of the proposed HA-PCNN-GRFO methodology is evaluated using Python Platform, and metrics like recall, accuracy, F-measure, precision, and specificity are analyzed. Thus, the proposed HA-PCNN-GRFO approach achieved 97.3% average accuracy rate, 96.5% average recall, 97.2% average precision, 95.9% average specificity, and 96.8% average F1-score for classifying human activities using UCI HAR dataset. Also, the proposed HA-PCNN-GRFO approach is outperformed in HAR than the conventional approaches. [ABSTRACT FROM AUTHOR]

This study examines the impact of sensor placement and multimodal sensor fusion on the performance of a Long Short-Term Memory (LSTM)-based model for human activity classification taking place in an agricultural harvesting scenario involving human-robot collaboration. Data were collected from twenty participants performing six distinct activities using five wearable inertial measurement units placed at various anatomical locations. The signals collected from the sensors were first processed to eliminate noise and then input into an LSTM neural network for recognizing features in sequential time-dependent data. Results indicated that the chest-mounted sensor provided the highest F1-score of 0.939, representing superior performance over other placements and combinations of them. Moreover, the magnetometer surpassed the accelerometer and gyroscope, highlighting its superior ability to capture crucial orientation and motion data related to the investigated activities. However, multimodal fusion of accelerometer, gyroscope, and magnetometer data showed the benefit of integrating data from different sensor types to improve classification accuracy. The study emphasizes the effectiveness of strategic sensor placement and fusion in optimizing human activity recognition, thus minimizing data requirements and computational expenses, and resulting in a cost-optimal system configuration. Overall, this research contributes to the development of more intelligent, safe, cost-effective adaptive synergistic systems that can be integrated into a variety of applications. [ABSTRACT FROM AUTHOR]

Obtaining discriminative joint features is crucial for skeleton-based human action recognition. Current models mainly focus on the research of skeleton topology encoding. However, their predefined topology is the same and fixed for all action samples, making it challenging to obtain discriminative joint features. Although some studies have considered the complex non-natural connection relationships between joints, the existing methods cannot fully capture this complexity by using high-order adjacency matrices or adding trainable parameters and instead increase the computation parameters. Therefore, this study constructs a novel adaptive channel-enhanced graph convolution (ACE-GCN) model for human action recognition. The model generates similar and affinity attention maps by encoding channel attention in the input features. These maps are complementarily applied to the input feature map and graph topology, which can realize the refinement of joint features and construct an adaptive and non-shared channel-based adjacency matrix. This method of constructing the adjacency matrix improves the model's capacity to capture intricate non-natural connections between joints, prevents the accumulation of unnecessary information, and minimizes the number of computational parameters. In addition, integrating the Edgeconv module into a multi-branch aggregation improves the model's ability to aggregate different scale and temporal features. Ultimately, comprehensive experiments were carried out on NTU-RGB+D 60 and NTU-RGB+D 120, which are two substantial datasets. On the NTU RGB+D 60 dataset, the accuracy of human action recognition was 92% (X-Sub) and 96.3% (X-View). The model achieved an accuracy of 96.6% on the NW-UCLA dataset. The experimental results confirm that the ACE-GCN exhibits superior recognition accuracy and lower computing complexity compared to current methodologies. [ABSTRACT FROM AUTHOR]

With the evolution of smart devices like smartphones, smartwatches, and other wearable devices, motion sensors have been integrated into these devices to collect data and analyze human activities. Consequently, sensor-based Human Activity Recognition (HAR) has emerged as a significant research area in the fields of ubiquitous computing and wearable computing. This paper presents a novel approach that employs Latent Dirichlet Allocation (LDA) to extract meaningful representations from activity signals. The method involves transforming the activity signal, which is a sequence of samples, into a sequence of discrete symbols using vector quantization. Subsequently, LDA is utilized to embed the symbol sequence into a fixed-length representation vector. Finally, a classifier is employed to classify the obtained representation vector. The effectiveness of the proposed method is evaluated using the UNIMIB-SHAR dataset. Experimental results demonstrate its competitive performance in terms of accuracy and F1-score metrics when compared to existing methods. Moreover, our method boasts a more lightweight architecture and incurs lower computational costs compared to deep learning-based approaches. The findings of this study contribute to the advancement of HAR and hold practical implications for HAR systems. [ABSTRACT FROM AUTHOR]

Mobile and wearable devices have revolutionized the field of continuous user activity monitoring. However, analyzing the vast and intricate data captured by the sensors of these devices poses significant challenges. Deep neural networks have shown remarkable accuracy in Human Activity Recognition (HAR), but their application on mobile and wearable devices is constrained by limited computational resources. To address this limitation, we propose a novel method called Knowledge Distillation for Human Activity Recognition (KD-HAR) that leverages the knowledge distillation technique to compress deep neural network models for HAR using inertial sensor data. Our approach transfers the acquired knowledge from high-complexity teacher models (state-of-the-art models) to student models with reduced complexity. This compression strategy allows us to maintain performance while keeping computational costs low. To assess the compression capabilities of our approach, we evaluate it using two popular databases (UCI-HAR and WISDM) comprising inertial sensor data from smartphones. Our results demonstrate that our method achieves competitive accuracy, even at compression rates ranging from 18 to 42 times the number of parameters compared to the original teacher model. [ABSTRACT FROM AUTHOR]

Background: Accurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling. Methods: We first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expenditure model was then developed and validated using additional data based on a sample of 69 healthy adult participants (49% female; age = 25.2 ± 5.8 years) who completed a standardised activity protocol with indirect calorimetry as the reference measure. Results: The activity classification model showed an overall accuracy of 99.7% across all five activity types during validation. The composite model for estimating energy expenditure achieved a mean absolute percentage error of 10.9%. For running, walking, and cycling, the composite model achieved a mean absolute percentage error of 6.6%, 7.9% and 16.1%, respectively. Conclusions: The integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings. [ABSTRACT FROM AUTHOR]

Recent generation Microsoft Kinect Camera captures a series of multimodal signals that provide RGB video, depth sequences, and skeleton information, thus it becomes an option to achieve enhanced human action recognition performance by fusing different data modalities. However, most existing fusion methods simply fuse different features, which ignores the underlying semantics between different models, leading to a lack of accuracy. In addition, there exists a large amount of background noise. In this work, we propose a Vision Transformer-based Bilinear Pooling and Attention Network (VT-BPAN) fusion mechanism for human action recognition. This work improves the recognition accuracy in the following ways: 1) An effective two-stream feature pooling and fusion mechanism is proposed. The RGB frames and skeleton are fused to enhance the spatio-temporal feature representation. 2) A spatial lightweight multiscale vision Transformer is proposed, which can reduce the cost of computing. The framework is evaluated based on three widely used video action datasets, and the proposed approach performs a more comparable performance with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) has gained much attention since sensor technology has become more advanced and cost-effective. HAR is a process of identifying the daily living activities of an individual with the help of an efficient learning algorithm and prospective user-generated datasets. This paper addresses the technical advancement and classification of HAR systems in detail. Design issues, future opportunities, recent state-of-the-art related works, and a generic framework for activity recognition are discussed in a comprehensive manner with analytical discussion. Different publicly available datasets with their features and incorporated sensors are also descr-processing techniques with various performance metrics like - Accuracy, F1-score, Precision, Recall, Computational times and evaluation schemes are discussed for the comprehensive understanding of the Activity Recognition Chain (ARC). Different learning algorithms are exploited and compared for learning-based performance comparison. For each specific module of this paper, a compendious number of references is also cited for easy referencing. The main aim of this study is to give the readers an easy hands-on implementation in the field of HAR with verifiable evidence of different design issues. [ABSTRACT FROM AUTHOR]

In recent years, human activity recognition (HAR) has been widely used in medical rehabilitation, smart home and other fields. Currently, the recognition performance highly depends on feature extraction and effective algorithm. On the one hand, traditional manual feature extraction and classification algorithms hinder the improvement of HAR. On the other hand, the latest deep learning technology can automatically process data and extract features, but it faces the problems of poor feature quality and information loss. In order to solve this problem, this paper proposes a new recognition method using only wearable sensor data. In the feature extraction stage, the axis information of each sensor is extracted separately into one-dimensional data, and information of all axes is integrated into a two-dimensional graph. Then, two deep convolutional neural network models are designed to train the features based on one-dimensional data and two-dimensional graph respectively. Finally, weighted voting method is used to get the classification results. Experiments have shown that the average recognition accuracy of the method in this paper is about 3% higher than that of other HAR deep neural network methods, which shown the advantage of the method in this paper in obtaining better recognition result with limited data. [ABSTRACT FROM AUTHOR]

Human action recognition (HAR) based on skeleton data is a challenging yet crucial task due to its wide-ranging applications, including patient monitoring, security surveillance, and human- machine interaction. Although numerous algorithms have been proposed to distinguish between various activities, most practical applications require highly accurate detection of specific actions. In this study, we propose a novel, highly accurate spatiotemporal graph autoencoder network for HAR, designated as GA-GCN. Furthermore, an extensive investigation was conducted employing diverse modalities. To this end, a spatiotemporal graph autoencoder was constructed to automatically learn both spatial and temporal patterns from skeleton data. The proposed method achieved accuracies of 92.3% and 96.8% on the NTU RGB+D dataset for cross-subject and cross-view evaluations, respectively. On the more challenging NTU RGB+D 120 dataset, GA-GCN attained accuracies of 88.8% and 90.4% for cross-subject and cross-set evaluations. Overall, our model outperforms the majority of the existing state-of-the-art methods on these common benchmark datasets. [ABSTRACT FROM AUTHOR]

Biologically inspired intelligent methods have been applied to various sensing systems in order to extract features from a huge size of raw sensing data. For example, point cloud data can be applied to human activity recognition, multi-person tracking, and suspicious person detection, but a single RGB-D camera is not enough to perform the above tasks. Therefore, this study propose a 3D environmental map-building method integrating point cloud data measured via multiple RGB-D cameras. First, a fast multi-scale of distributed batch-learning growing neural gas (Fast MS-DBL-GNG) is proposed as a topological feature extraction method in order to reduce computational costs because a single RGB-D camera may output 1 million data. Next, random sample consensus (RANSAC) is applied to integrate two sets of point cloud data using topological features. In order to show the effectiveness of the proposed method, Fast MS-DBL-GNG is applied to perform topological mapping from several point cloud data sets measured in different directions with some overlapping areas included in two images. The experimental results show that the proposed method can extract topological features enough to integrate point cloud data sets, and it runs 14 times faster than the previous GNG method with a 23% reduction in the quantization error. Finally, this paper discuss the advantage and disadvantage of the proposed method through numerical comparison with other methods, and explain future works to improve the proposed method. [ABSTRACT FROM AUTHOR]

In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture "come" alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety. [ABSTRACT FROM AUTHOR]

The current 3D-ConvNet-based action recognition algorithms generally use GAP to compress feature information. However, it leads to issues of information loss, redundancy, and network overfitting. To address these issues and enhance the retention of high-level semantic information extracted by the convolutional layer, this paper proposed an action recognition al- gorithm based on GFDP. Firstly, DCT shows that GAP is a special case of feature decomposition in the frequency domain. Therefore, the algorithm introduced more frequency components to increase the specificity between feature channels and reduce the information redundancy after information compression. Secondly, to better suppress the overfitting problem, the algorithm introduced the batch normalization strategy to the convolutional layer and extended it to the fully connected layer of the action recognition model with ERB-Res3D as the skeleton to optimize the data distribution. Finally, this paper verified the proposed method on the UCF101 dataset. The results reveals that the model's computational load is 3.5 GFlops, with 7.4 million para- meters. The final recognition accuracy improved by 3.9% based on the ERB-Res3D model and 17.4% based on the original Res3D model. This improvement effectively achieves more accurate behavior recognition results. [ABSTRACT FROM AUTHOR]

Human action recognition from videos requires a sequence of frames to be analyzed involving high processing power and time. Moreover, subsequent frames in a video contain redundancy that does not contribute to extracting distinguishable features but rather consumes processing resources. To recognize action with lower processing overhead, researchers are focusing on key frames and have proposed different techniques based on inter-frame clustering, fix-frame interval, and inter-frame differences. Clustering and fixed interval approaches consider the predefined number of frames or fixed intervals, therefore, not equally effective for both faster and slower actions which differ in terms of frequency of change. Furthermore, all existing approaches consider pixels? Intensities only but neglect another equally important aspect of motion, i.e., direction. The three-channel optical flows are one of the motion representations that depict both the magnitude and the direction of movement between pairs of frames in the form of colors. Here we proposed a novel "KFF-algorithm" that processes a sequence of three-channel optical flows to extract key flow frames for action recognition. Being dynamic in terms of frame interval, it efficiently extracts key frames for both slower and faster actions by analyzing changes in direction, magnitude, and coverage of motion in subsequent frames. Furthermore, in comparison with other approaches, KFF-algorithm covers relatively extended motion patterns with least number of frames or frames with significant change only. Also, for the majority of classes, KFF-algorithm has achieved substantial per-class accuracy when evaluated with the 3D-ConvNet model over the UCF-101 benchmark dataset for human action recognition. [ABSTRACT FROM AUTHOR]

This study investigates the feasibility of using a basic 24 GHz one-dimensional (1D) radar for Human Activity Recognition (HAR), focusing on differentiating between walking and standing movements. We evaluate the radar's performance using various machine learning models, including K-means, GMM, SVM, and LSTM. Using the silhouette score and the Davies-Bouldin index, we evaluate the intra- and interclass results of K-means and GMM, while SVM and LSTM are used to analyze their performance. The results indicate that the LSTM model achieves high accuracy in both vertical and horizontal dimensions, with precision, recall, and F1-scores all above 98% for both standing and walking movements. However, the SVM model faces challenges in horizontal movement detection, consistent with the unsupervised learning results where the inter-class and intra-class distances for the horizontal dimension are not significant, making differentiation difficult. These findings delineate the boundaries and capabilities of a lower-specification radar for HAR, providing insights into its practical applications and limitations. [ABSTRACT FROM AUTHOR]

This 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. [ABSTRACT FROM AUTHOR]

Research on smartphone-based human activity recognition (HAR) is prevalent in the field of healthcare, especially for elderly activity monitoring. Researchers usually propose to use of accelerometers, gyroscopes or magnetometers that are equipped in smartphones as an individual sensing modality for human activity recognition. However, any of these alone is limited in capturing comprehensive movement information for accurate human activity analysis. Thus, we propose a smartphone-based HAR approach by leveraging the inertial signals captured by these three sensors to classify human activities. These heterogeneous sensors deliver information on various aspects of nature, motion and orientation, offering a richer set of features for more accurate representations of the activities. Hence, a deep learning approach that amalgamates long short-term memory (LSTM) in temporal convolutional network (TCN) is proposed. We use independent temporal convolutional networks, coined as temporal convolutional streams, to independently analyse the temporal data of each sensing modality. We name this architecture multi-stream TC-LSTM. The performance of multi-stream TC-LSTM is assessed on the self-collected elderly activity database. Empirical results exhibit that multi-stream TC-LSTM outperforms the existing machine learning and deep learning models, with an F1 score of 98.3%. [ABSTRACT FROM AUTHOR]

Human action recognition from skeleton motion sequences is widely applied in various fields such as virtual reality, human-computer interaction and kinematic rehabilitation. With the wide use of graph neural networks for extracting spatial features from skeleton anatomy connectivity, spatial-temporal extension of single graph models on human skeleton may improve the network performance. In this paper, we propose a priori separation graph convolution (PS-GCN) network composition with a priori mixed GCN by introducing a hypergraph representation of the skeleton spatial features, and a dynamic adaptive GCN to describe the respective graph model for each sample at each layer of the network of spatial features. For temporal feature analysis, a global attention unit is added to describe the long-term relationship. Moreover, a feature fusion structure is applied for short-term temporal features in the input of the network. The proposed model is evaluated on the NTU-RGB+D, NTU-RGB+D 120 and NW-UCLA datasets via a comprehensive ablative study. The results show that our model is comparable in accuracy to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) typically uses wearable sensors to identify and analyze the time-series data they collect, enabling recognition of specific actions. As such, HAR is increasingly applied in human–computer interaction, healthcare, and other fields, making accurate and efficient recognition of various human activities. In recent years, deep learning methods have been extensively applied in sensor-based HAR, yielding remarkable results. However, complex HAR research, which involves specific human behaviors in varied contexts, still faces several challenges. To solve these problems, we propose a multi-head neural network based on the attention mechanism. This framework contains three convolutional heads, with each head designed using one-dimensional CNN to extract features from sensory data. The model uses a channel attention module (squeeze–excitation module) to enhance the representational capabilities of convolutional neural networks. We conducted experiments on two publicly available benchmark datasets, UCI-HAR and WISDM, to evaluate our model. The results were satisfactory, with overall recognition accuracies of 96.72% and 97.73% on their respective datasets. The experimental results demonstrate the effectiveness of the network structure for the HAR, which ensures a higher level of accuracy. [ABSTRACT FROM AUTHOR]

In recent years, wearable devices-based Human Activity Recognition (HAR) models have received significant attention. Previously developed HAR models use hand-crafted features to recognize human activities, leading to the extraction of basic features. The images captured by wearable sensors contain advanced features, allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions. Poor lighting and limited sensor capabilities can impact data quality, making the recognition of human actions a challenging task. The unimodal-based HAR approaches are not suitable in a real-time environment. Therefore, an updated HAR model is developed using multiple types of data and an advanced deep-learning approach. Firstly, the required signals and sensor data are accumulated from the standard databases. From these signals, the wave features are retrieved. Then the extracted wave features and sensor data are given as the input to recognize the human activity. An Adaptive Hybrid Deep Attentive Network (AHDAN) is developed by incorporating a "1D Convolutional Neural Network (1DCNN)" with a "Gated Recurrent Unit (GRU)" for the human activity recognition process. Additionally, the Enhanced Archerfish Hunting Optimizer (EAHO) is suggested to fine-tune the network parameters for enhancing the recognition process. An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model. The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36, 95.25 for recall, 95.48 for specificity, and 95.47 for precision, respectively. The result proved that the developed model is effective in recognizing human action by taking less time. Additionally, it reduces the computation complexity and overfitting issue through using an optimization approach. [ABSTRACT FROM AUTHOR]

Wearable human action recognition (HAR) has practical applications in daily life. However, traditional HAR methods solely focus on identifying user movements, lacking interactivity and user engagement. This paper proposes a novel immersive HAR method called MovPosVR. Virtual reality (VR) technology is employed to create realistic scenes and enhance the user experience. To improve the accuracy of user action recognition in immersive HAR, a multi‐scale spatio‐temporal attention network (MSSTANet) is proposed. The network combines the convolutional residual squeeze and excitation (CRSE) module with the multi‐branch convolution and long short‐term memory (MCLSTM) module to extract spatio‐temporal features and automatically select relevant features from action signals. Additionally, a multi‐head attention with shared linear mechanism (MHASLM) module is designed to facilitate information interaction, further enhancing feature extraction and improving accuracy. The MSSTANet network achieves superior performance, with accuracy rates of 99.33% and 98.83% on the publicly available WISDM and PAMPA2 datasets, respectively, surpassing state‐of‐the‐art networks. Our method showcases the potential to display user actions and position information in a virtual world, enriching user experiences and interactions across diverse application scenarios. [ABSTRACT FROM AUTHOR]

A daily activity routine is vital for overall health and well-being, supporting physical and mental fitness. Consistent physical activity is linked to a multitude of benefits for the body, mind, and emotions, playing a key role in raising a healthy lifestyle. The use of wearable devices has become essential in the realm of health and fitness, facilitating the monitoring of daily activities. While convolutional neural networks (CNN) have proven effective, challenges remain in quickly adapting to a variety of activities. This study aimed to develop a model for precise recognition of human activities to revolutionize health monitoring by integrating transformer models with multi-head attention for precise human activity recognition using wearable devices. The Human Activity Recognition (HAR) algorithm uses deep learning to classify human activities using spectrogram data. It uses a pretrained convolution neural network (CNN) with a MobileNetV2 model to extract features, a dense residual transformer network (DRTN), and a multi-head multi-level attention architecture (MH-MLA) to capture time-related patterns. The model then blends information from both layers through an adaptive attention mechanism and uses a SoftMax function to provide classification probabilities for various human activities. The integrated approach, combining pretrained CNN with transformer models to create a thorough and effective system for recognizing human activities from spectrogram data, outperformed these methods in various datasets – HARTH, KU-HAR, and HuGaDB produced accuracies of 92.81%, 97.98%, and 95.32%, respectively. This suggests that the integration of diverse methodologies yields good results in capturing nuanced human activities across different activities. The comparison analysis showed that the integrated system consistently performs better for dynamic human activity recognition datasets. In conclusion, maintaining a routine of daily activities is crucial for overall health and well-being. Regular physical activity contributes substantially to a healthy lifestyle, benefiting both the body and the mind. The integration of wearable devices has simplified the monitoring of daily routines. This research introduces an innovative approach to human activity recognition, combining the CNN model with a dense residual transformer network (DRTN) with multi-head multi-level attention (MH-MLA) within the transformer architecture to enhance its capability. [ABSTRACT FROM AUTHOR]

Over the past few decades, wearable sensor technology has played a pivotal role in patient information acquisition. A new paradigm for unconstrained medical data collection emerged with noncontact sensors such as Kinect and industrial-grade RGB cameras. These innovations have enhanced patient experiences and offer vast potential for long-term monitoring, telemedicine, and remote healthcare in underserved areas. However, while many research efforts have zeroed in on specific components, such as sensor development or algorithm design, this has occasionally led to noncontact systems' reliability, accuracy, and connectivity challenges. Consequently, there is a pressing need for research that adopts a more holistic approach, ensuring the optimal integration of sensors and algorithms. In this study, we introduce a method of constructing a noncontact diagnostic system powered by deep learning vision algorithms that showcase strong resilience against viewpoint changes and obstructions in human motion identification and assessment. Using four RGB cameras, we capture human dynamics and leverage a pose estimator to generate comprehensive 3D human postures. Afterward, these postures are refined to aid in subsequent behavior prediction. Our multitask-trained model significantly bolsters the system's adaptability to posture discrepancies. Notably, this noncontact diagnostic system thrives in challenging environments, such as 360-degree surveillance, intricate situations, and low-light settings where traditional sensors often falter. In addition, we have assembled a multiview autism behavior dataset. Through it, our embedded deep learning algorithm showcases exemplary action category recognition (reaching up to 95.09%), highlighting further its practical implications. [ABSTRACT FROM AUTHOR]

Human activity recognition is a burgeoning field with the aim of observing and understanding human actions, focusing on appearance and movement. The goal of this field is to develop advanced systems for creating lifelike models and facilitating interactions. Computer vision is the foundation, serving surveillance, robot learning, user interfaces, and human–computer interaction. CCTV advancements enable impactful applications like home nursing and elderly care through video surveillance. However, extracting behavior from varied camera sources remains challenging. This paper introduces a proof of concept: a cost-effective fall detection system using a 5MP Pi camera connected via MIPI. A three-stage hybrid Haar Cascade model coupled with video frame sequences, utilizing background subtraction, achieves 89.21% accuracy with 2.5% false positives and 2.0% false negatives, surpassing state-of-the-art methods. The system's core purpose is to accurately monitor human movement, particularly detecting falls, and in emergencies, alert family via SMS or Email for the safety of solitary elderly individuals having age 60 years and above. [ABSTRACT FROM AUTHOR]

This document is a list of articles published in the Journal of Intelligent & Fuzzy Systems. The articles cover a wide range of topics, including IoT-based networks, credit default discrimination models, clustering algorithms, image processing techniques, predictive models for drug responses, neural network mechanisms for cell categorization, and various other topics related to intelligent systems and fuzzy logic. The articles provide valuable insights and research findings in their respective fields. [Extracted from the article]