Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics—An Overview of Current Applications, Challenges, and Future Opportunities

Sensors, Journal Year: 2023, Volume and Issue: 23(9), P. 4259 - 4259

Published: April 25, 2023

Work-related musculoskeletal disorders (WMSDs) are a major contributor to disability worldwide and substantial societal costs. The use of wearable motion capture instruments has role in preventing WMSDs by contributing improvements exposure risk assessment potentially improved effectiveness work technique training. Given the versatile potential for wearables, this article aims provide an overview their application related prevention trunk upper limbs discusses challenges technology support measures future opportunities, including research needs. relevant literature was identified from screening recent systematic reviews overviews, more studies were search using Web Science platform. Wearable enables continuous measurements multiple body segments superior accuracy precision compared observational tools. also real-time visualization exposures, automatic analyses, feedback user. While miniaturization usability wearability can expand occupational settings increase among safety health practitioners, several fundamental remain be resolved. opportunities increased usage devices work-related may require international collaborations creating common standards measurements, metrics, which epidemiologically based categories disorders.

Language: Английский

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A Systematic Review of Human Activity Recognition Based on Mobile Devices: Overview, Progress and Trends

IEEE Communications Surveys & Tutorials, Journal Year: 2024, Volume and Issue: 26(2), P. 890 - 929

Published: Jan. 1, 2024

Due to the ever-growing powers in sensing, computing, communicating and storing, mobile devices (e.g., smartphone, smartwatch, smart glasses) become ubiquitous an indispensable part of people's daily life. Until now, have been adopted many applications, e.g., exercise assessment, life monitoring, human-computer interactions, user authentication, etc. Among various Human Activity Recognition (HAR) is core technology behind them. Specifically, HAR gets sensor data corresponding human activities based on built-in sensors devices, then adopts suitable recognition approaches infer type activity data. The last two decades witnessed ever-increasing research HAR. However, new challenges opportunities are emerging, especially for devices. Therefore, this paper, we review aiming advance following area. Firstly, give overview including general rationales, main components challenges. Secondly, analyze progress from each aspect, activities, data, preprocessing, approaches, evaluation standards application cases. Finally, present some promising trends future research.

Language: Английский

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Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition

Sensors, Journal Year: 2025, Volume and Issue: 25(3), P. 724 - 724

Published: Jan. 25, 2025

Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convolutional neural networks (CNNs) with conventional signal processing methods to improve accuracy efficiency HAR. Three distinct, two-dimensional techniques, specifically range-fast Fourier transform (FFT)-based time-range maps, time-Doppler-based short-time (STFT) smoothed pseudo-Wigner–Ville distribution (SPWVD) are evaluated combination four state-of-the-art CNN architectures: VGG-16, VGG-19, ResNet-50, MobileNetV2. positions radar-generated maps a form visual data, bridging image representation domains while ensuring privacy sensitive applications. In total, twelve preprocessing configurations analyzed, focusing on trade-offs between complexity accuracy, all which essential real-time Among these results, MobileNetV2, combined STFT preprocessing, showed an ideal balance, achieving high computational rate 96.30%, spectrogram generation time 220 ms inference 2.57 per sample. The comprehensive evaluation underscores importance interpretable features resource-constrained environments, expanding applicability radar-based HAR systems augmented reality, autonomous edge

Language: Английский

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Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing

Sensors, Journal Year: 2023, Volume and Issue: 23(11), P. 5281 - 5281

Published: June 2, 2023

Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies homes and cities to enhance the quality of life for citizens. Sensing human action recognition are crucial aspects this concept. living applications span various domains, such as energy consumption, healthcare, transportation, education, which greatly benefit from effective recognition. This field, originating computer vision, seeks recognize actions activities using not only visual data but also many other sensor modalities. paper comprehensively reviews literature on smart environments, synthesizing main contributions, challenges, future research directions. review selects five key i.e., Technology, Multimodality, Real-time Processing, Interoperability, Resource-Constrained they encompass critical required successfully deploying living. These domains highlight essential role sensing play developing implementing solutions. serves valuable resource researchers practitioners seeking further explore advance field

Language: Английский

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Surface Electromyography and Artificial Intelligence for Human Activity Recognition—A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 105140 - 105169

Published: Jan. 1, 2023

Human activity recognition (HAR) has become increasingly popular in recent years due to its potential meet the growing needs of various industries. Electromyography (EMG) is essential clinical and biological settings. It a metric that helps doctors diagnose conditions affect muscle activation patterns monitor patients' progress rehabilitation. Despite widespread Application, existing methods for recording interpreting EMG data need more signal detection robust categorization. Recent material science Artificial Intelligence (AI) developments have significantly improved detection. With an elderly patient population, HAR used Activities Daily Living (ADLs) healthcare also being security settings identify suspect behavior, Surface (sEMG) non-invasive treatment since it monitors contractions during exercise. sEMG AI revolutionized systems years. Sophisticated are required recognize, break down, manufacture, classify signals obtained by muscles. This review summarizes research papers based on with EMG. made tremendous contributions biomedical classification. The different approaches preprocessing, feature extraction, Reduction techniques, Deep Learning (DL) Machine (ML) classification then briefly explained. We focused latest ML/DL HAR, Hardware involved Application. discovered open issues future direction may point new lines inquiry ongoing toward EMG-based

Language: Английский

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Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform

Algorithms, Journal Year: 2023, Volume and Issue: 16(2), P. 77 - 77

Published: Feb. 1, 2023

Over the last few years, human activity recognition (HAR) has drawn increasing interest from scientific community. This attention is mainly attributable to proliferation of wearable sensors and expanding role HAR in such fields as healthcare, sports, monitoring. Convolutional neural networks (CNN) are becoming a popular approach for addressing problems. However, this method requires extensive training datasets perform adequately on new data. paper proposes novel deep learning model pre-trained scalograms generated using continuous wavelet transform (CWT). Nine CNN architectures different CWT configurations were considered select best performing combination, resulting evaluation more than 300 models. On source KU-HAR dataset, selected achieved classification accuracy an F1 score 97.48% 97.52%, respectively, which outperformed contemporary state-of-the-art works where dataset was employed. target UCI-HAPT proposed resulted maximum F1-score increase 0.21% 0.33%, whole 2.82% 2.89%, subset. It concluded that usage model, particularly with frozen layers, results improved performance, faster training, smoother gradient descent small datasets. use sufficiently large may lead negative transfer degradation.

Language: Английский

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A Novel Front Door Security (FDS) Algorithm Using GoogleNet-BiLSTM Hybridization

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 19122 - 19134

Published: Jan. 1, 2023

Security has always been a significant concern since the dawn of human civilization. That is why we build houses to keep ourselves and our belongings safe. And do not hesitate spend lot on front-door locks install CCTV cameras monitor security threats. This paper presents an innovative automatic Front Door (FDS) algorithm that uses Human Activity Recognition (HAR) detect four different threats at front door from real-time video feed with 73.18% accuracy. The activities are recognized using combination GoogleNet-BiLSTM hybrid network. network receives camera classifies activities. proposed this classification alert any attempts break by kicking, punching, or hitting. Furthermore, FDS effective in detecting gun violence door, which further strengthens security. (HAR)-based novel demonstrates potential ensuring better safety 71.49% precision, 68.2% recall, F1-score 0.65.

Language: Английский

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A Hybrid Approach for Sports Activity Recognition Using Key Body Descriptors and Hybrid Deep Learning Classifier

Sensors, Journal Year: 2025, Volume and Issue: 25(2), P. 441 - 441

Published: Jan. 13, 2025

This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key points were approximated to track monitor presence complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, DOF (Degrees of Freedom), applied skeleton points, while BRIEF (Binary Independent Elementary HOG (Histogram Oriented Gradients), FAST (Features from Accelerated Segment Test), Optical Flow used on silhouettes or full-body capture both geometric motion-based features. Feature fusion was employed enhance the discriminative power extracted data physical parameters calculated by different extraction techniques. The system utilized a hybrid CNN (Convolutional Neural Network) + RNN (Recurrent classifier recognition, with Grey Wolf Optimization (GWO) selection. Experimental results showed significant accuracy, achieving 98.5% UCF-101 dataset 99.2% YouTube dataset. Compared state-of-the-art methods, our achieved better performance recognition.

Language: Английский

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Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing

Electronics, Journal Year: 2023, Volume and Issue: 12(8), P. 1892 - 1892

Published: April 17, 2023

Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to attacks, including data loss, theft, financial reputation harm, company interruption, damage, ransomware espionage. A cyber-physical attack harms both physical assets. system more challenging than software-level because it requires inspection monitoring. This paper proposes an innovative effective algorithm strengthen (CPS) with minimal human intervention. It approach based on activity recognition (HAR), where GoogleNet–BiLSTM network hybridization has been used recognize suspicious activities in the perimeter. The proposed HAR-CPS classifies from real-time video surveillance average accuracy of 73.15%. incorporates machine vision at IoT edge (Mez) technology make latency tolerant. Dual-layer ensured by operating hybrid a cloud server, which ensures system. optimization scheme makes possible only USD 4.29±0.29 per month.

Language: Английский

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Application of machine learning in the study of cobalt-based oxide catalysts for antibiotic degradation: An innovative reverse synthesis strategy

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 471, P. 134309 - 134309

Published: April 16, 2024

Language: Английский

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