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Human Activity Recognition (HAR) is a complex problem in deep learning, and One-Dimensional Convolutional Neural Networks (1D CNNs) have emerged as a popular approach for addressing it. These networks efficiently learn features from data that can be utilized to classify human activities with high performance. However, understanding and explaining the features learned by these networks remains a challenge. This paper presents a novel eXplainable Artificial Intelligence (XAI) method for generating visual explanations of features learned by one-dimensional CNNs in its training process, utilizing t-Distributed Stochastic Neighbor Embedding (t-SNE). By applying this method, we provide insights into the decision-making process through visualizing the information obtained from the model's deepest layer before classification. Our results demonstrate that the learned features from one dataset can be applied to differentiate human activities in other datasets. Our trained networks achieved high performance on two public databases, with 0.98 accuracy on the SHO dataset and 0.93 accuracy on the HAPT dataset. The visualization method proposed in this work offers a powerful means to detect bias issues or explain incorrect predictions. This work introduces a new type of XAI application, enhancing the reliability and practicality of CNN models in real-world scenarios.

Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR , a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Javed, Khan, Alomari, Sarwar, Asim, Almadhor and Khan.)

Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial datasets. However, obtaining such datasets may not be feasible in neurological populations due to, e.g., impaired patient mobility to perform many daily activities. This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations. The framework produces images from numerical inertial time-series data (initial state) and then artificially augments the number of produced images (enhanced state) to achieve a larger dataset. Here, we used convolutional neural network (CNN) architectures by utilizing image input. In addition, CNN enables transfer learning which enables limited datasets to benefit from models that are trained with big data. Initially, two benchmarked public datasets were used to verify the framework. Afterward, the approach was tested in limited local datasets of healthy subjects (HS), Parkinson's disease (PD) population, and stroke survivors (SS) to further investigate validity. The experimental results show that when data augmentation is applied, recognition accuracies have been increased in HS, SS, and PD by 25.6%, 21.4%, and 5.8%, respectively, compared to the no data augmentation state. In addition, data augmentation contributes to better detection of stair ascent and stair descent by 39.1% and 18.0%, respectively, in limited local datasets. Findings also suggest that CNN architectures that have a small number of deep layers can achieve high accuracy. The implication of this study has the potential to reduce the burden on participants and researchers where limited datasets are accrued., Competing Interests: The authors declare no conflict of interest.

The number of older people needing healthcare is a growing global phenomenon. The assistance in long-term care comprises a complex of medical, nursing, rehabilitation, and social assistance services. The cost is substantial, but technology can help reduce spending by ensuring efficient health services and improving the quality of life. Advances in artificial intelligence, wireless communication systems, and nanotechnology allow the creation of intelligent home care systems avoiding hospitalization with evident cost containment. They are capable of ensuring functions of recognition of activities, monitoring of vital functions, and tracking. However, it is essential to also have information on location in order to be able to promptly intervene in case of unforeseen events or assist people in carrying out activities in order to avoid incorrect behavior. In addition, the automatic detection of physical activities performed by human subjects is identified as human activity recognition (HAR). This work presents an overview of the positioning system as part of an integrated HAR system. Lastly, this study contains each technology's concepts, features, accuracy, advantages, and limitations. With this work, we want to highlight the relationship between HAR and the indoor positioning system (IPS), which is poorly documented in the literature.

Wi-Fi-based human activity recognition has attracted broad attention for its advantages, which include being device-free, privacy-protected, unaffected by light, etc. Owing to the development of artificial intelligence techniques, existing methods have made great improvements in sensing accuracy. However, the performance of multi-location recognition is still a challenging issue. According to the principle of wireless sensing, wireless signals that characterize activity are also seriously affected by location variations. Existing solutions depend on adequate data samples at different locations, which are labor-intensive. To solve the above concerns, we present an amplitude- and phase-enhanced deep complex network (AP-DCN)-based multi-location human activity recognition method, which can fully utilize the amplitude and phase information simultaneously so as to mine more abundant information from limited data samples. Furthermore, considering the unbalanced sample number at different locations, we propose a perception method based on the deep complex network-transfer learning (DCN-TL) structure, which effectively realizes knowledge sharing among various locations. To fully evaluate the performance of the proposed method, comprehensive experiments have been carried out with a dataset collected in an office environment with 24 locations and five activities. The experimental results illustrate that the approaches can achieve 96.85% and 94.02% recognition accuracy, respectively.

In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM., (Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.)

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]

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]

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]

With the development of the ageing society, wearable strain sensors have great application prospects in the fields of health detection, artificial intelligence, and motion recognition. However, traditional forms of film‐like strain sensors suffer from low sensitivity, adhesion, and impermeability during use, which severely limit their practical applications. To overcome these problems, we propose a strategy to fabricate polyurethane/polydopamine/MXene (TPU/PDA/MXene) composite fibers by wet spinning. Furthermore, the outside is wrapped with (PDMS), giving mechanical strength and chemical resistance. Through a simple and low‐cost wet spinning process, TPU/PDA/MXene fibers are achieved in the preparation of flexible strain sensors, and they can be woven into various forms with good flexibility and comfort according to practical needs. The results indicated that the prepared flexible strain sensor exhibits a high gauge factor (GF) of 57.15 over a large strain range (300 %), which are superior to those of many fiber‐based sensors. Their application properties were also measured by attaching them to fingers, wrists and knees, showing high sensitivity, mechanical durability and chemical stability, and has great potential for wearable strain sensors and flexible strain sensors in complex environments. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Sensors titled "Human Activity Recognition, Monitoring, and Analysis Facilitated by Novel and Widespread Applications of Sensors." The issue features 10 academic articles selected from 30 submissions. The articles cover a range of topics related to human activity recognition (HAR) and sensor applications, including video-based HAR using graph neural networks, wearable-based HAR with localization, human body movement characteristics for acrophobia study, human activity counting using deep learning, warship commander activities for mental analysis, complex HAR in urban environmental exposure research, accelerometer-based HAR using domain generalization, finger gesture-based user identification using radio frequency technology, associating human behavior, manufacture, and digital interaction with fabrication, and facial expression understanding using deep learning and multimodal large language models. The articles present state-of-the-art approaches and findings in HAR research and are authored by scientists from 14 countries across three continents. [Extracted from the article]

Over the past decade, there have been great advancements in radio frequency sensor technology for human–computer interaction applications, such as gesture recognition, and human activity recognition more broadly. While there is a significant amount of study on these topics, in most cases, experimental data are acquired in controlled settings by directing participants what motion to articulate. However, especially for communicative motions, such as sign language, such directed data sets do not accurately capture natural, in situ articulations. This results in a difference in the distribution of directed American Sign Language (ASL) versus natural ASL, which severely degrades natural sign language recognition in real‐world scenarios. To overcome these challenges and acquire more representative data for training deep models, the authors develop an interactive gaming environment, ChessSIGN, which records video and radar data of participants as they play the game without any external direction. The authors investigate various ways of generating synthetic samples from directed ASL data, but show that ultimately such data does not offer much improvement over just initialising using imagery from ImageNet. In contrast, an interactive learning paradigm is proposed by the authors in which model training is shown to improve as more and more natural ASL samples are acquired and augmented via synthetic samples generated from a physics‐aware generative adversarial network. The authors show that the proposed approach enables the recognition of natural ASL in a real‐world setting, achieving an accuracy of 69% for 29 ASL signs—a 60% improvement over conventional training with directed ASL data. [ABSTRACT FROM AUTHOR]

Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human–machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction. [ABSTRACT FROM AUTHOR]

Human action recognition has facilitated the development of artificial intelligence devices focusing on human activities and services. This technology has progressed by introducing 3D point clouds derived from depth cameras or radars. However, human behavior is intricate, and the involved point clouds are vast, disordered, and complicated, posing challenges to 3D action recognition. To solve these problems, we propose a Symmetric Fine-coarse Neural Network (SFCNet) that simultaneously analyzes human actions' appearance and details. Firstly, the point cloud sequences are transformed and voxelized into structured 3D voxel sets. These sets are then augmented with an interval-frequency descriptor to generate 6D features capturing spatiotemporal dynamic information. By evaluating voxel space occupancy using thresholding, we can effectively identify the essential parts. After that, all the voxels with the 6D feature are directed to the global coarse stream, while the voxels within the key parts are routed to the local fine stream. These two streams extract global appearance features and critical body parts by utilizing symmetric PointNet++. Subsequently, attention feature fusion is employed to capture more discriminative motion patterns adaptively. Experiments conducted on public benchmark datasets NTU RGB+D 60 and NTU RGB+D 120 validate SFCNet's effectiveness and superiority for 3D action recognition. [ABSTRACT FROM AUTHOR]

This paper focuses on human activity recognition (HAR) for elderly care using the Light Gradient Boosting Machine (LGBM) algorithm. HAR plays a vital role in monitoring and ensuring the well-being of older individuals. By analysing sensor data, this system can accurately detect activities such as jogging, walking, sitting, and standing. The proposed framework integrates LGBM with an Android application that reads user movement data, classifies activities, displays step counts per day, and provides rewards for achieving movement targets. To address privacy concerns, user data is anonymized using Elliptic Curve Cryptography (ECC) Blind Signature. The system leverages the power of artificial intelligence models in the Mobile Crowd Sensing (MCS) server to effectively distinguish between different activities with high accuracy and reliability. By remotely monitoring the elderly's activities, healthcare providers can ensure their safety. Experiment results show the practicality of proposed system by achieving overall accuracy of activity recognition when sitting, standing, walking, and jogging 97.5%. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) research has gained significant momentum due to recent technological advancements, artificial intelligence algorithms, the need for smart cities, and socioeconomic transformation. However, existing computer vision and sensor-based HAR solutions have limitations such as privacy issues, memory and power consumption, and discomfort in wearing sensors for which researchers are observing a paradigm shift in HAR research. In response, WiFi-based HAR is gaining popularity due to the availability of more coarse-grained Channel State Information. However, existing WiFi-based HAR approaches are limited to classifying independent and non-concurrent human activities performed within equal time duration. Recent research commonly utilizes a Single Input Multiple Output communication link with a WiFi signal of 5 GHz channel frequency, using two WiFi routers or two Intel 5300 NICs as transmitter-receiver. Our study, on the other hand, utilizes a Multiple Input Multiple Output radio link between a WiFi router and an Intel 5300 NIC, with the time-series Wi-Fi channel state information based on 2.4 GHz channel frequency for mutual human-to-human concurrent interaction recognition. The proposed Self-Attention guided Bidirectional Gated Recurrent Neural Network (Attention-BiGRU) deep learning model can classify 13 mutual interactions with a maximum benchmark accuracy of 94% for a single subject-pair. This has been expanded for ten subject pairs, which secured a benchmark accuracy of 88% with improved classification around the interaction-transition region. An executable graphical user interface (GUI) software has also been developed in this study using the PyQt5 python module to classify, save, and display the overall mutual concurrent human interactions performed within a given time duration. Finally, this article concludes with a discussion of the possible solutions to the observed limitations and identifies areas for further research. Such a Wi-Fi channel perturbation pattern analysis is believed to be an efficient, economical, and privacy-friendly approach to be potentially utilized in mutual human interaction recognition for indoor activity monitoring, surveillance system, smart health monitoring systems, and independent assisted living. [ABSTRACT FROM AUTHOR]

Human pose estimation (HPE) is a technique used in computer vision and artificial intelligence to detect and track human body parts and poses using images or videos. Widely used in augmented reality, animation, fitness applications, and surveillance, HPE methods that employ monocular cameras are highly versatile and applicable to standard videos and CCTV footage. These methods have evolved from two-dimensional (2D) to three-dimensional (3D) pose estimation. However, in real-world environments, current 3D HPE methods trained on laboratory-based motion capture data encounter challenges, such as limited training data, depth ambiguity, left/right switching, and issues with occlusions. In this study, four 3D HPE methods were compared based on their strengths and weaknesses using real-world videos. Joint position correction techniques were proposed to eliminate and correct anomalies such as left/right inversion and false detections of joint positions in daily life motions. Joint angle trajectories were obtained for intuitive and informative human activity recognition using an optimization method based on a 3D humanoid simulator, with the joint position corrected by the proposed technique as the input. The efficacy of the proposed method was verified by applying it to three types of freehand gymnastic exercises and comparing the joint angle trajectories during motion. [ABSTRACT FROM AUTHOR]

Respiratory Inductance Plethysmography (RIP) is a non-invasive method for the measurement of respiratory rates and lung volumes. Accurate detection of respiratory rates and volumes is crucial for the diagnosis and monitoring of prognosis of lung diseases, for which spirometry is classically used in clinical applications. RIP has been studied as an alternative to spirometry and shown promising results. Moreover, RIP data can be analyzed through machine learning (ML)-based approaches for some other purposes, i.e., detection of apneas, work of breathing (WoB) measurement, and recognition of human activity based on breathing patterns. The goal of this study is to provide an in-depth systematic review of the scope of usage of RIP and current RIP device developments, as well as to evaluate the performance, usability, and reliability of ML-based data analysis techniques within its designated scope while adhering to the PRISMA guidelines. This work also identifies research gaps in the field and highlights the potential scope for future work. The IEEE Explore, Springer, PLoS One, Science Direct, and Google Scholar databases were examined, and 40 publications were included in this work through a structured screening and quality assessment procedure. Studies with conclusive experimentation on RIP published between 2012 and 2023 were included, while unvalidated studies were excluded. The findings indicate that RIP is an effective method to a certain extent for testing and monitoring respiratory functions, though its accuracy is lacking in some settings. However, RIP possesses some advantages over spirometry due to its non-invasive nature and functionality for both stationary and ambulatory uses. RIP also demonstrates its capabilities in ML-based applications, such as detection of breathing asynchrony, classification of apnea, identification of sleep stage, and human activity recognition (HAR). It is our conclusion that, though RIP is not yet ready to replace spirometry and other established methods, it can provide crucial insights into subjects' condition associated to respiratory illnesses. The implementation of artificial intelligence (AI) could play a potential role in improving the overall effectiveness of RIP, as suggested in some of the selected studies. [ABSTRACT FROM AUTHOR]

This paper aims to discuss the implementation of data analysis and information management for elderly nursing care from a data-driven perspective. It addresses the current challenges of in-home caregivers, providing a basis for decision making in analyzing nursing service content and evaluating job performance. The characteristics of caregivers' activities were analyzed during the design of a wearable device-wearing scheme and a sensor data collection system. XGBoost, SVM, and Random Forest models were used in the experiments, with the Cuckoo search algorithm employed to optimize the XGBoost model parameters. Based on the control group experiment, it was confirmed that the XGBoost model, after adjusting the parameters using the Cuckoo search algorithm, exhibited better recognition performance than the SVM and RandomForest models, and the accuracy reached 0.9438. Wearable devices present high recognition accuracy in caregiver activity recognition research, which greatly improves the inspection of caregivers' work and further promotes the completion of services. This study actively explores the applications of information technology and artificial intelligence theory to address practical problems and effectively promote the digitalization and intelligent development of the elderly nursing care industry. [ABSTRACT FROM AUTHOR]

This article discusses the role of Artificial Intelligence (AI) and Digital Health Technologies (DHTs) in drug development and the need for a harmonized approach to assess and implement these technologies. The Critical Path Institute (C-Path) is highlighted as a neutral entity that can address the pivotal roles of AI and DHTs in drug development through its competencies and infrastructure. C-Path's core competencies include data management and standards, modeling and analytics, biomarkers, clinical outcome assessments, and regulatory science. The article emphasizes C-Path's expertise in AI and DHTs and its ability to drive impactful advancements in the field through collaboration and innovation. [Extracted from the article]

The class imbalance problem occurs when there is an unequal distribution of classes in a dataset and is a significant issue in various artificial intelligence applications. This study focuses on the severe multiclass imbalance problem of human activity recognition in rehabilitation exercises for people with disabilities. To overcome this problem, we present a novel human action-centric augmentation method for human skeleton-based pose estimation. This study proposes the state transition-oriented conditional variational autoencoder (STO-CVAE) to capture action patterns in repeated exercises. The proposed approach generates action samples by capturing temporal information of human skeletons to improve the identification of minority disability classes. We conducted experimental studies with a real-world dataset gathered from rehabilitation exercises and confirmed the superiority and effectiveness of the proposed method. Specifically, all investigated classifiers (i.e., random forest, support vector machine, extreme gradient boosting, light gradient boosting machine, and TabNet) trained with the proposed augmentation method outperformed the models trained without augmentation in terms of the F1-score and accuracy, with F1-score showing the most improvement. Overall, the prediction accuracy of most classes was improved; in particular, the prediction accuracy of the minority classes was greatly improved. Hence, the proposed STO-CVAE can be used to improve the accuracy of disability classification in the field of physical medicine and rehabilitation and to provide suitable personal training and rehabilitation exercise programs. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology has become integral in the realm of medicine and healthcare, particularly in human activity recognition (HAR) applications such as fitness and rehabilitation tracking. This study introduces a robust coupling analysis framework that integrates four AI-enabled models, combining both machine learning (ML) and deep learning (DL) approaches to evaluate their effectiveness in HAR. The analytical dataset comprises 561 features sourced from the UCI-HAR database, forming the foundation for training the models. Additionally, the MHEALTH database is employed to replicate the modeling process for comparative purposes, while inclusion of the WISDM database, renowned for its challenging features, supports the framework's resilience and adaptability. The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and random forest (RF), for data training. In contrast, a DL-based model utilizes one dimensional convolution neural network (1dCNN) to automate feature extraction. Furthermore, the recursive feature elimination (RFE) algorithm, which drives an ML-based estimator to eliminate low-participation features, helps identify the optimal features for enhancing model performance. The best accuracies of the ANFIS, SVM, RF, and 1dCNN models with meticulous featuring process achieve around 90%, 96%, 91%, and 93%, respectively. Comparative analysis using the MHEALTH dataset showcases the 1dCNN model's remarkable perfect accuracy (100%), while the RF, SVM, and ANFIS models equipped with selected features achieve accuracies of 99.8%, 99.7%, and 96.5%, respectively. Finally, when applied to the WISDM dataset, the DL-based and ML-based models attain accuracies of 91.4% and 87.3%, respectively, aligning with prior research findings. In conclusion, the proposed framework yields HAR models with commendable performance metrics, exhibiting its suitability for integration into the healthcare services system through AI-driven applications. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) involves the prediction of movement type of a human, based on raw data being captured from wearable sensors or vision based sensors. HAR systems have sapped great interest over the period of time due to its wide applications in the field of healthcare, surveillance and also in the coming generation of metaverse. This article proposes an artificial intelligence (AI) based system using hybrid deep learning model to recognise various human activities from the video footages. The deep convolutional layers and recurrent neural network (RNN) have been combined to generate the hybrid deep learning model known as convolutional recurrent neural network (CRNN). The deep convolutional layers of deep convolutional neural network Inception V3 Net have been used to extract the feature values from the video frames corresponding to each human activity and each generated feature vector has been classified to the appropriate activity class by the gated recurrent unit (GRU) variant of RNN classifier. The performance of the proposed HAR system has been evaluated on the three widely used public datasets—KTH, UCF101, and UCF sports action dataset. GRU variant of RNN classifier is capable to store and remember a long temporal sequence of video frames, generating the pattern of any human activity, over a long duration and so experimental results exhibit that the proposed hybrid deep learning based HAR system outperforms the state-of-the-art methods available in this research domain. [ABSTRACT FROM AUTHOR]

General movements (GMs) have been widely used for the early clinical evaluation of infant brain development, allowing immediate evaluation of potential development disorders and timely rehabilitation. The infants' general movements can be captured digitally, but the lack of quantitative assessment and well‐trained clinical pediatricians presents an obstacle for many years to achieve wider deployment, especially in low‐resource settings. There is a high potential to explore wearable sensors for movement analysis due to outstanding privacy, low cost, and easy‐to‐use features. This work presents a sparse sensor network with soft wireless IMU devices (SWDs) for automatic early evaluation of general movements in infants. The sparse network consisting of only five sensor nodes (SWDs) with robust mechanical properties and excellent biocompatibility continuously and stably captures full‐body motion data. The proof‐of‐the‐concept clinical testing with 23 infants showcases outstanding performance in recognizing neonatal activities, confirming the reliability of the system. Taken together with a tiny machine learning algorithm, the system can automatically identify risky infants based on the GMs, with an accuracy of up to 100% (99.9%). The wearable sparse sensor network with an artificial intelligence‐based algorithm facilitates intelligent evaluation of infant brain development and early diagnosis of development disorders. [ABSTRACT FROM AUTHOR]

This document discusses the intersection of artificial intelligence (AI) and deep learning with sensor technologies. It highlights the applications of AI and deep learning in various fields such as healthcare, manufacturing, agriculture, transportation, and environmental monitoring. The document also provides a summary of twelve research articles and reviews that explore the integration of AI and deep learning with sensor data for different purposes, including lung cancer screening, reinforcement learning, point cloud sampling, anomaly detection, human activity recognition, semantic segmentation, face recognition, audio super-resolution, industrial control systems, traffic flow prediction, weed detection, and medical diagnostics. These contributions demonstrate the potential of AI and deep learning in optimizing sensor data utilization and solving complex real-world problems. [Extracted from the article]

The advancement of various computing technologies such as Artificial Intelligence (AI), cloud computing, mobile computing, and Internet of Things (IoT) is widely applied in several applications. The rapid evolvement of IoT is determined in advance with embedded systems, data analytics, and wireless connectivity. However, the Deep Learning (DL) method is highly applicable to evaluating human activity which is a crucial element to determine the surveillance of human behavior. But the common issue obtained in the detection of human activity is the time series problem and failure to detect accurately in heterogeneous sensors. So a novel Multi-Modal Deep-Learning (MMDL) is proposed to detect human activity accurately with reduced time consumption. However, in real-time applications, huge amounts of data are monitored and the activity of every individual is detected independently by integrating the proposed method with 2D Deep Canonical Correlation Analysis (DCCA). Furthermore, the detection of human activity is employed through IoT devices like multi-modal sensors. The multi-head CNN is used to decrease the cross channels and spatial interactions while training with high-dimensional datasets. The MMDL and binary Support Vector Machine (SVM) architectures overcome the drawbacks of the existing techniques such as the need for increased width/depth for improved performance and the rare usage of hierarchical representations. Thus the effectiveness of the proposed method is validated by STISEN and GAIT datasets. The experimentation results revealed that the proposed method attained a better accuracy of 97% with reduced time in various scenarios related to existing methods. [ABSTRACT FROM AUTHOR]

To achieve the energy transition, energy and energy efficiency are becoming more and more important in society. New methods, such as Artificial Intelligence (AI) and Machine Learning (ML) models, are needed to coordinate supply and demand and address the challenges of the energy transition. AI and ML are already being applied to a growing number of energy infrastructure applications, ranging from energy generation to energy forecasting and human activity recognition services. Given the rapid development of AI and ML, the importance of Trustworthy AI is growing as it takes on increasingly responsible tasks. Particularly in the energy domain, Trustworthy AI plays a decisive role in designing and implementing efficient and reliable solutions. Trustworthy AI can be considered from two perspectives, the Model-Centric AI (MCAI) and the Data-Centric AI (DCAI) approach. We focus on the DCAI approach, which relies on large amounts of data of sufficient quality. These data are becoming more and more synthetically generated. To address this trend, we introduce the concept of Synthetic Data-Centric AI (SDCAI). In this survey, we examine Trustworthy AI within a Synthetic Data-Centric AI context, focusing specifically on the role of simulation and synthetic data in enhancing the level of Trustworthy AI in the energy domain. [ABSTRACT FROM AUTHOR]

This article introduces the concept of tinyML, which focuses on implementing machine learning on low-power devices such as microcontrollers and custom-designed circuits. The goal is to enable these devices to perform machine learning tasks while consuming minimal power and lasting for extended periods on a single battery charge. The special issue covers a range of topics related to tinyML, including energy efficiency in computer vision, automatic design and code generation, regression-based profiling, and the application of binary neural networks. The article discusses various techniques and approaches in the field, such as accelerating neural networks on microcontrollers and software frameworks for machine learning libraries. It also explores specific applications, such as real-time video anonymization and predicting gas concentration with low-cost sensors. Overall, the article provides insights into the advancements and challenges in tinyML and offers potential solutions for resource-constrained devices. [Extracted from the article]

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This document is an editorial for a special issue of the journal Micromachines on the methodology, microfabrication, and applications of advanced sensing and smart systems. The editorial highlights the increasing integration of sensing technology with other cutting-edge technologies, such as machine learning, wearable sensors, and the Internet of Things. The special issue includes articles on topics such as intelligent manufacturing methodologies, fluorescence microscopy image inpainting, MEMS micromirrors, wearable robotics, quantitative analysis of particle size, and medical monitoring systems. The authors express gratitude to the contributors and reviewers and hope that the articles will inspire new innovations in advanced sensing and smart systems. [Extracted from the article]

In recent years, researchers have successfully recognised human activities using commercially available WiFi (Wireless Fidelity) devices. The channel state information (CSI) can be gathered at the access point with the help of a network interface controller (NIC card). These CSI streams are sensitive to human body motions and produce abrupt changes (fluctuations) in their magnitude and phase values when a moving object interacts with a transmitter and receiver pair. This sensing methodology is gaining popularity compared to traditional approaches involving wearable technology, as it is a contactless sensing strategy with no cumbersome sensing equipments fitted on the target with preserved privacy since no personal information of the subject is collected. In previous investigations, internal validation statistics have been promising. However, external validation results have been poor, due to model application to varying subjects with remarkably different environments. To address this problem, we propose an adversarial Artificial Intelligence AI model that learns and utilises domain-invariant features. We analyse model results in terms of suitability for inter-domain and intra-domain alignment techniques, to identify which is better at robustly matching the source to target domain, and hence improve recognition accuracy in cross-user conditions for HAR using wireless signals. We evaluate our model performance on different target training data percentages to assess model reliability on data scarcity. After extensive evaluation, our architecture shows improved predictive performance across target training data proportions when compared to a non-adversarial model for nine cross-user conditions with comparatively less simulation time. We conclude that inter-domain alignment is preferable for HAR applications using wireless signals, and confirm that the dataset used is suitable for investigations of this type. Our architecture can form the basis of future studies using other datasets and/or investigating combined cross-environmental and cross-user features. [ABSTRACT FROM AUTHOR]

This document is a guest editorial introducing a special issue of the journal Electronics on real-world applications of machine learning. The issue includes 11 research and review articles that cover a range of topics, such as sentiment analysis, fault diagnosis, load recognition, and optimization of transportation systems. The articles highlight the importance of machine learning in practical scenarios and demonstrate its potential for improving various tasks and processes. The authors express their gratitude to the contributors and reviewers for their valuable contributions to the special issue. [Extracted from the article]

Frameworks for human activity recognition (HAR) can be applied in the clinical environment for monitoring patients' motor and functional abilities either remotely or within a rehabilitation program. Deep Learning (DL) models can be exploited to perform HAR by means of raw data, thus avoiding time-demanding feature engineering operations. Most works targeting HAR with DL-based architectures have tested the workflow performance on data related to a separate execution of the tasks. Hence, a paucity in the literature has been found with regard to frameworks aimed at recognizing continuously executed motor actions. In this article, the authors present the design, development, and testing of a DL-based workflow targeting continuous human activity recognition (CHAR). The model was trained on the data recorded from ten healthy subjects and tested on eight different subjects. Despite the limited sample size, the authors claim the capability of the proposed framework to accurately classify motor actions within a feasible time, thus making it potentially useful in a clinical scenario. [ABSTRACT FROM AUTHOR]

The video camera is essential for reliable activity monitoring, and a robust analysis helps in efficient interpretation. The systematic assessment of classroom activity through videos can help understand engagement levels from the perspective of both students and teachers. This practice can also help in robot-assistive classroom monitoring in the context of human–robot interaction. Therefore, we propose a novel algorithm for student–teacher activity recognition using 3D CNN (STAR-3D). The experiment is carried out using India's indigenously developed supercomputer PARAM Shivay by the Centre for Development of Advanced Computing (C-DAC), Pune, India, under the National Supercomputing Mission (NSM), with a peak performance of 837 TeraFlops. The EduNet dataset (registered under the trademark of the DRSTATM dataset), a self-developed video dataset for classroom activities with 20 action classes, is used to train the model. Due to the unavailability of similar datasets containing both students' and teachers' actions, training, testing, and validation are only carried out on the EduNet dataset with 83.5% accuracy. To the best of our knowledge, this is the first attempt to develop an end-to-end algorithm that recognises both the students' and teachers' activities in the classroom environment, and it mainly focuses on school levels (K-12). In addition, a comparison with other approaches in the same domain shows our work's novelty. This novel algorithm will also influence the researcher in exploring research on the "Convergence of High-Performance Computing and Artificial Intelligence". We also present future research directions to integrate the STAR-3D algorithm with robots for classroom monitoring. [ABSTRACT FROM AUTHOR]

E-health systems rely on information and communication technology to support and improve various aspects of health services, delivery, and management. The success of artificial intelligence techniques has led to the emergence of a variety of systems designed to address a wide range of healthcare issues. In particular, gathering data on patient activity and behavior has enabled the development of reliable predictive systems for detecting chronic diseases and forecasting their progression. Human activity detection is a vast and emerging field, and various datasets have been collected for training different machine learning and deep learning (DL) models. The University of Milano Bicocca smartphone-based human activity recognition (UniMiB-SHAR) dataset is widely used for analyzing and recognizing human actions, including walking, running, and other daily activities. However, the autoencoder (AE) technique trained on this dataset yields poor performance. This paper aims to enhance the performance of AEs on the challenging UniMiB-SHAR dataset by introducing a convolutional AE model and employing novel preprocessing techniques, including normalization, magnitude, principal component analysis (PCA), and balancing methods such as SMOTEEN and ADASYNE. The experimental results demonstrate that the proposed AE model achieved successful performance, surpassing the state-of-the-art methods, with accuracies of 96.56% for activities of daily living (ADL), 98.86% for Fall, and 88.47% for the full dataset. [ABSTRACT FROM AUTHOR]

Emotion recognition remains an intricate task at the crossroads of psychology and artificial intelligence, necessitating real-time, accurate discernment of implicit emotional states. Here, we introduce a pioneering wearable dual-modal device, synergizing functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to meet this demand. The first-of-its-kind fNIRS-EEG ensemble exploits a temporal convolutional network (TC-ResNet) that takes 24 fNIRS and 16 EEG channels as input for the extraction and recognition of emotional features. Our system has many advantages including its portability, battery efficiency, wireless capabilities, and scalable architecture. It offers a real-time visual interface for the observation of cerebral electrical and hemodynamic changes, tailored for a variety of real-world scenarios. Our approach is a comprehensive emotional detection strategy, with new designs in system architecture and deployment and improvement in signal processing and interpretation. We examine the interplay of emotions and physiological responses to elucidate the cognitive processes of emotion regulation. An extensive evaluation of 30 subjects under four emotion induction protocols demonstrates our bimodal system's excellence in detecting emotions, with an impressive classification accuracy of 99.81% and its ability to reveal the interconnection between fNIRS and EEG signals. Compared with the latest unimodal identification methods, our bimodal approach shows significant accuracy gains of 0.24% for EEG and 8.37% for fNIRS. Moreover, our proposed TC-ResNet-driven temporal convolutional fusion technique outperforms conventional EEG-fNIRS fusion methods, improving the recognition accuracy from 0.7% to 32.98%. This research presents a groundbreaking advancement in affective computing that combines biological engineering and artificial intelligence. Our integrated solution facilitates nuanced and responsive affective intelligence in practical applications, with far-reaching impacts on personalized healthcare, education, and human–computer interaction paradigms. [ABSTRACT FROM AUTHOR]

Recognizing human activity (HAR) from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases. Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not. Typically, smartphones and their associated sensing devices operate in distributed and unstable environments. Therefore, collecting their data and extracting useful information is a significant challenge. In this context, the aimof this paper is twofold: The first is to analyze human behavior based on the recognition of physical activities. Using the results of physical activity detection and classification, the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities. This system is based on the calculation of calories burned by each user during physical activities. In this way, conclusions can be drawn about a person's physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts. To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory (LSTM), stacked LSTM, and bidirectional LSTM. Since human activity data contains both spatial and temporal information, we proposed, in this paper, to use of an architecture allowing the extraction of the two types of information simultaneously. While Convolutional Neural Networks (CNN) has an architecture designed for spatial information, our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data. The results obtained achieved an accuracy of 96%. On the other side, the data learned by these algorithms is prone to error and uncertainty. To overcome this constraint and improve performance (96%), we proposed to use the fusion mechanisms. The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making. The Voting and Dempster-Shafer (DS) approaches are employed. The results showed that fused classifiers based on DS theory outperformed individual classifiers (96%) with the highest accuracy level of 98%. Also, the findings disclosed that participants engaging in physical activities are healthy, showcasing a disparity in the distribution of physical activities between men and women. [ABSTRACT FROM AUTHOR]

Road building has long been under-mapped globally, arguably more than any other human activity threatening environmental integrity. Millions of kilometers of unmapped roads have challenged environmental governance and conservation in remote frontiers. Prior attempts to map roads at large scales have proven inefficient, incomplete, and unamenable to continuous road monitoring. Recent developments in automated road detection using artificial intelligence have been promising but have neglected the relatively irregular, sparse, rustic roadways characteristic of remote semi-natural areas. In response, we tested the accuracy of automated approaches to large-scale road mapping across remote rural and semi-forested areas of equatorial Asia-Pacific. Three machine learning models based on convolutional neural networks (UNet and two ResNet variants) were trained on road data derived from visual interpretations of freely available high-resolution satellite imagery. The models mapped roads with appreciable accuracies, with F1 scores of 72–81% and intersection over union scores of 43–58%. These results, as well as the purposeful simplicity and availability of our input data, support the possibility of concerted program of exhaustive, automated road mapping and monitoring across large, remote, tropical areas threatened by human encroachment. [ABSTRACT FROM AUTHOR]

Environmental management systems (EMS) are essential in promoting sustainable practices and mitigating the adverse effects of human activities on the environment. As technology continues to advance, there is an increasing opportunity to utilize advanced technologies to improve environmental management systems. This article examines the potential of different advanced technologies, such as artificial intelligence (AI), blockchain, big data, and the Internet of Things (IoT), within the context of environmental management systems. This article intends to offer valuable insights to researchers, practitioners, and policymakers by examining the potential uses of AI, blockchain, big data, and IoT in environmental management systems. The goal is to demonstrate how these advanced technologies can be leveraged to enhance sustainability, boost environmental performance, and yield favourable environmental results across different sectors and industries. [ABSTRACT FROM AUTHOR]

The recognition problem often rises in the classification of multidimensional data and is an integral part of artificial intelligence. Currently, there are many well-known methods and algorithms for solving the recognition problem. However, these methods are mainly aimed at solving such problems where objects are described by a set of independent features. A large number of features describe recognition problems in various fields of human activity. In such problems, the probability of violating the assumption of feature independence is quite high and this determines the relevance of this study. The purpose of this study is to develop a model of recognition algorithms based on the method of making statistical decisions, taking into account the high dimensionality of feature space. A statistical approach to solving the problem of recognition under the conditions of the high dimensionality of feature space is proposed. On the basis of this approach, a model of statistical recognition algorithms based on the construction of two-dimensional threshold rule is proposed. The main advantage of the proposed model is the reduction in the volume of computational operations when recognizing objects in the test set. At that, the reduction of the volume of computational operations is ensured using a number of procedures: selection of representative features; building a two-dimensional threshold rule; determination of a set of reference two-dimensional threshold rules; determination of basic two-dimensional threshold rules and calculation of a probabilistic assessment of the object belonging. This characteristic is very important for real-time recognition systems. The operability of the proposed model of recognition algorithms was investigated in the course of experiments in solving a model problem and the problem of recognizing a person's identity from a signature image. The results of the computational experiment confirm the operability of the proposed approach in solving the recognition problem under conditions when the assumption of feature independence is not satisfied. The results of experimental studies show that the proposed statistical algorithms increase the accuracy of recognition and significantly reduce the volume of computational operations by recognizing an unknown object specified in the space of interrelated features. These algorithms can be used in the compilation of various software systems focused on solving applied recognition problems in the conditions of the high dimensionality of feature space. [ABSTRACT FROM AUTHOR]

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate estrus detection by farmers is key to successful sow breeding as it offers the opportunity for timely mating, successful reproduction, and increased productivity. Traditional estrus detection methods based on physiological changes, mounting, and back pressure tests often fall short in accuracy, leading to lower conception rates and smaller litters. To address these issues, researchers are exploring modern technologies to study parameters such as vulvar temperature, posture, movement, and sound in relation to estrus. This review examines the effectiveness of these modern estrus detection techniques, and the findings indicate that they can enhance the accuracy of heat detection compared to conventional methods. In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved. [ABSTRACT FROM AUTHOR]

Owing to the advancement of interdisciplinary concepts, for example, wearable electronics, bioelectronics, and intelligent sensing, during the microelectronics industrial revolution, nowadays, extensively mature wearable sensing devices have become new favorites in the noninvasive human healthcare industry. The combination of wearable sensing devices with bionics is driving frontier developments in various fields, such as personalized medical monitoring and flexible electronics, due to the superior biocompatibilities and diverse sensing mechanisms. It is noticed that the integration of desired functions into wearable device materials can be realized by grafting biomimetic intelligence. Therefore, herein, the mechanism by which biomimetic materials satisfy and further enhance system functionality is reviewed. Next, wearable artificial sensory systems that integrate biomimetic sensing into portable sensing devices are introduced, which have received significant attention from the industry owing to their novel sensing approaches and portabilities. To address the limitations encountered by important signal and data units in biomimetic wearable sensing systems, two paths forward are identified and current challenges and opportunities are presented in this field. In summary, this review provides a further comprehensive understanding of the development of biomimetic wearable sensing devices from both breadth and depth perspectives, offering valuable guidance for future research and application expansion of these devices. [ABSTRACT FROM AUTHOR]

Building on previous radar‐based human activity recognition (HAR), we expand the micro‐Doppler signature to 6 domains and exploit each domain with a set of handcrafted features derived from the literature and our patents. An adaptive thresholding method to isolate the region of interest is employed, which is then applied in other domains. To reduce the computational burden and accelerate the convergence to an optimal solution for classification accuracy, a holistic approach to HAR optimisation is proposed using a surrogate model‐assisted differential evolutionary algorithm (SADEA‐I) to jointly optimise signal processing, adaptive thresholding and classification parameters for HAR. Two distinct classification models are evaluated with holistic optimisation: SADEA‐I with support vector machine classifiers (SVM) and SADEA‐I with AlexNet. They achieve an accuracy of 89.41% and 93.54%, respectively. This is an improvement of ∼11.3% for SVM and ∼2.7% for AlexNet when compared to the performance without SADEA‐I. The effectiveness of our holistic approach is validated using the University of Glasgow human radar signatures dataset. This proof of concept is significant for dimensionality reduction and computational efficiency when facing a multiplication of radar representation domains/feature spaces and transmitting/receiving channels that could be individually tuned in modern radar systems. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Radar, Sonar & Navigation. It discusses the advances in AI-assisted radar sensing applications and the challenges that hinder its adoption in this field. The special issue of the journal features nine papers that address these challenges and offer innovative ideas and experimental results. The papers cover a range of topics, including health monitoring, human activity recognition, voice identification, elderly care health monitoring, track-to-track association, signal pre-processing, traffic congestion alleviation, and target recognition. The authors express their gratitude to the contributors and reviewers and believe that the research presented will inspire further exploration and innovation in this field. [Extracted from the article]

In an effort to improve short-sea shipping in Europe, we present a 3D world interpreter (3DWI) system as part of a robotic container-handling system. The 3DWI is an advanced sensor suite combined with AI-based software and the communication infrastructure to connect to both the crane control and the shore control center. On input of LiDAR data and stereo captures, the 3DWI builds a world model of the operating environment and detects containers. The 3DWI and crane control are the core of an autonomously operating crane that monitors the environment and may trigger an emergency stop while alerting the remote operator of the danger. During container handling, the 3DWI scans for human activity and continuously updates a 3D-Twin model for the operator, enabling situational awareness. The presented methodology includes the sensor suite design, creation of the world model and the 3D-Twin, innovations in AI-detection software, and interaction with the crane and operator. Supporting experiments quantify the performance of the 3DWI, its AI detectors, and safety measures; the detectors reach the top of VisDrone's leaderboard and the pilot tests show the safe autonomous operation of the crane. [ABSTRACT FROM AUTHOR]

Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation. The evolution of machine vision reflects an ambitious journey to emulate human visual perception. Interdisciplinary collaboration and the integration of deep learning techniques have propelled remarkable advancements in emulating human behavior and perception. Through this research, the field of machine vision continues to shape the future of computer systems and artificial intelligence applications. [ABSTRACT FROM AUTHOR]

Nowadays, the advancements of wearable consumer devices have become a predominant role in healthcare gadgets. There is always a demand to obtain robust recognition of heterogeneous human activities in complicated IoT environments. The knowledge attained using these recognition models will be then combined with healthcare applications. In this way, the paper proposed a novel deep learning framework to recognize heterogeneous human activities using multimodal sensor data. The proposed framework is composed of four phases: employing dataset and processing, implementation of deep learning model, performance analysis, and application development. The paper utilized the recent KU-HAR database with eighteen different activities of 90 individuals. After preprocessing, the hybrid model integrating Extreme Learning Machine (ELM) and Gated Recurrent Unit (GRU) architecture is used. An attention mechanism is then included for further enhancing the robustness of human activity recognition in the IoT environment. Finally, the performance of the proposed model is evaluated and comparatively analyzed with conventional CNN, LSTM, GRU, ELM, Transformer and Ensemble algorithms. To the end, an application is developed using the Qt framework which can be deployed on any consumer device. In this way, the research sheds light on monitoring the activities of critical patients by healthcare professionals remotely. The proposed ELM-GRUaM model achieved supreme performance in recognizing multimodal human activities with an overall accuracy of 96.71% as compared with existing models.