State-of-the-Art on Brain-Computer Interface Technology

Sensors, Год журнала: 2023, Номер 23(13), С. 6001 - 6001

Опубликована: Июнь 28, 2023

This paper provides a comprehensive overview of the state-of-the-art in brain–computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The then examines various components BCI system, such as hardware, software, signal processing algorithms. Finally, it looks at current trends research related use for medical, educational, other purposes, well potential future applications this technology. concludes highlighting some key challenges that still need be addressed before widespread adoption can occur. By presenting up-to-date assessment technology, will provide valuable insight into where field is heading terms progress innovation.

Язык: Английский

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Online human motion analysis in industrial context: A review

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 131, С. 107850 - 107850

Опубликована: Янв. 13, 2024

Язык: Английский

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Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation

Sensors, Год журнала: 2023, Номер 23(2), С. 1039 - 1039

Опубликована: Янв. 16, 2023

Embedded hardware systems, such as wearable devices, are widely used for health status monitoring of ageing people to improve their well-being. In this context, it becomes increasingly important develop portable, easy-to-use, compact, and energy-efficient hardware-software platforms, enhance the level usability promote deployment. With purpose an automatic tri-axial accelerometer-based system postural recognition has been developed, useful in detecting potential inappropriate behavioral habits elderly. Systems literature on market type analysis mostly use personal computers with high computing resources, which not easily portable have power consumption. To overcome these limitations, a real-time posture Machine Learning algorithm was developed optimized that could perform highly platforms low computational capacity The software integrated tested two low-cost embedded platform (Raspberry Pi 4 Odroid N2+). experimentation stage performed various pre-trained classifiers using data seven elderly users. preliminary results showed activity classification accuracy about 98% four analyzed postures (Standing, Sitting, Bending, Lying down), similar load state-of-the-art running computers.

Язык: Английский

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Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges

Sensors, Год журнала: 2024, Номер 24(15), С. 5045 - 5045

Опубликована: Авг. 4, 2024

The proliferation of wearable technology enables the generation vast amounts sensor data, offering significant opportunities for advancements in health monitoring, activity recognition, and personalized medicine. However, complexity volume these data present substantial challenges modeling analysis, which have been addressed with approaches spanning time series to deep learning techniques. latest frontier this domain is adoption large language models (LLMs), such as GPT-4 Llama, modeling, understanding, human behavior monitoring through lens data. This survey explores current trends applying LLMs sensor-based recognition modeling. We discuss nature capabilities limitations them, their integration traditional machine also identify key challenges, including quality, computational requirements, interpretability, privacy concerns. By examining case studies successful applications, we highlight potential enhancing analysis interpretation Finally, propose future directions research, emphasizing need improved preprocessing techniques, more efficient scalable models, interdisciplinary collaboration. aims provide a comprehensive overview intersection between LLMs, insights into state prospects emerging field.

Язык: Английский

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Graphene Nanoplatelet Integrated Thermally Drawn PVDF Triboelectric Nanocomposite Fibers for Extreme Environmental Conditions

Advanced Electronic Materials, Год журнала: 2024, Номер 10(4)

Опубликована: Янв. 3, 2024

Abstract Triboelectric nanogenerators (TENGs) utilize the synergetic effect of triboelectrification and electrostatic induction to guide electrons through an external circuit, enabling low‐frequency mechanical biomechanical energy harvesting self‐powered sensing. Integrating 2D material with a high specific surface area into flexible ferroelectric polymers such as polyvinylidene difluoride (PVDF) has proven be efficient strategy improve performance TENG devices. Scalable fabrication graphene‐integrated PVDF nanocomposite fiber using thermal drawing process is demonstrated for first time in this study. The open‐circuit voltage short‐circuit current show 1.41 times 1.48 improvement integration 5% graphene fibers, respectively. fabric shows maximum power output 32.14 µW at matching load 7 MΩ density 53.57 mW m −2 . fibers exhibit excellent stability harsh environmental conditions alkaline medium, high/low temperature, multi‐washing cycle, long‐time usage.

Язык: Английский

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Polyacrylamide/sodium alginate/sodium chloride photochromic hydrogel with high conductivity, anti-freezing property and fast response for information storage and electronic skin

International Journal of Biological Macromolecules, Год журнала: 2024, Номер 268, С. 131972 - 131972

Опубликована: Апрель 30, 2024

Photochromic hydrogels have promising prospects in areas such as wearable device, information encryption technology, optoelectronic display and electronic skin. However, there are strict requirements for the properties of photochromic practical engineering applications, especially some extreme application environments. The preparation with high transparency, toughness, fast response, colour reversibility, excellent electrical conductivity, anti-freezing property remains a challenge. In this study, novel hydrogel (PAAm/SA/NaCl-Mo7) was prepared by loading ammonium molybdate (Mo7) sodium chloride (NaCl) into dual-network polyacrylamide (PAAm) alginate (SA) using simple one-pot method. PAAm/SA/NaCl-Mo7 has conductivity (175.9 S/cm), water retention capacity properties, which can work normally at low temperature −28.4 °C. addition, exhibits response (<15 s), transparency (>70 %), good toughness (maximum elongation up to 1500 cyclic compression compressive strains (60 biocompatibility (78.5 stable reversible discolouration sensing be used photoelectric display, storage motion monitoring. This provides new inspiration development flexible skin devices.

Язык: Английский

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Wearables for Monitoring and Postural Feedback in the Work Context: A Scoping Review

Sensors, Год журнала: 2024, Номер 24(4), С. 1341 - 1341

Опубликована: Фев. 19, 2024

Wearables offer a promising solution for simultaneous posture monitoring and/or corrective feedback. The main objective was to identify, synthesise, and characterise the wearables used in workplace monitor postural feedback workers. PRISMA-ScR guidelines were followed. Studies included between 1 January 2000 22 March 2023 Spanish, French, English, Portuguese without geographical restriction. databases selected research PubMed®, Web of Science®, Scopus®, Google Scholar®. Qualitative studies, theses, reviews, meta-analyses excluded. Twelve studies included, involving total 304 workers, mostly health professionals (n = 8). remaining covered workers industry 2), construction 1), welders 1). For assessment purposes, most one 5) or two sensors characterised as accelerometers 7), sixaxial 2) nonaxialinertial measurement units 3). common source sensor itself 6) smartphones 4). Haptic prevalent 6), followed by auditory visual Most employed prototype emphasising kinematic variables human movement. Healthcare primary focus study along with haptic that proved be effective method correcting during work activities.

Язык: Английский

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Capability of Machine Learning Algorithms to Classify Safe and Unsafe Postures during Weight Lifting Tasks Using Inertial Sensors

Diagnostics, Год журнала: 2024, Номер 14(6), С. 576 - 576

Опубликована: Март 8, 2024

Occupational ergonomics aims to optimize the work environment and enhance both productivity worker well-being. Work-related exposure assessment, such as lifting loads, is a crucial aspect of this discipline, it involves evaluation physical stressors their impact on workers’ health safety, in order prevent development musculoskeletal pathologies. In study, we explore feasibility machine learning (ML) algorithms, fed with time- frequency-domain features extracted from inertial signals (linear acceleration angular velocity), automatically accurately discriminate safe unsafe postures during weight tasks. The were acquired by means one measurement unit (IMU) placed sternums 15 subjects, subsequently segmented extract several features. A supervised dataset, including features, was used feed ML models assess prediction power. Interesting results terms metrics for binary safe/unsafe posture classification obtained logistic regression algorithm, which outperformed others, accuracy area under receiver operating characteristic curve values up 96% 99%, respectively. This result indicates proposed methodology—based single sensor artificial intelligence—to associated load activities. Future investigation wider study population using additional scenarios could confirm potentiality methodology, supporting its applicability occupational field.

Язык: Английский

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Artificial Intelligence and Occupational Health and Safety, Benefits and Drawbacks

˜La œMedicina del lavoro, Год журнала: 2024, Номер 115(2), С. e2024014 - e2024014

Опубликована: Апрель 24, 2024

This paper discusses the impact of artificial intelligence (AI) on occupational health and safety. Although integration AI into field safety is still in its early stages, it has numerous applications workplace. Some these offer benefits for workers, such as continuous monitoring workers' workplace environment through wearable devices sensors. However, might have negative impacts workplace, ethical worries data privacy concerns. To maximize minimize drawbacks certain measures should be applied, training both employers employees setting policies guidelines regulating

Язык: Английский

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Combining Postural Sway Parameters and Machine Learning to Assess Biomechanical Risk Associated with Load-Lifting Activities

Diagnostics, Год журнала: 2025, Номер 15(1), С. 105 - 105

Опубликована: Янв. 4, 2025

Background/Objectives: Long-term work-related musculoskeletal disorders are predominantly influenced by factors such as the duration, intensity, and repetitive nature of load lifting. Although traditional ergonomic assessment tools can be effective, they often challenging complex to apply due absence a streamlined, standardized framework. Recently, integrating wearable sensors with artificial intelligence has emerged promising approach effectively monitor mitigate biomechanical risks. This study aimed evaluate potential machine learning models, trained on postural sway metrics derived from an inertial measurement unit (IMU) placed at lumbar region, classify risk levels associated lifting based Revised NIOSH Lifting Equation. Methods: To compute parameters, IMU captured acceleration data in both anteroposterior mediolateral directions, aligning closely body’s center mass. Eight participants undertook two scenarios, each involving twenty consecutive tasks. classifiers were tested utilizing validation strategies, Gradient Boost Tree algorithm achieving highest accuracy Area under ROC Curve 91.2% 94.5%, respectively. Additionally, feature importance analysis was conducted identify most influential parameters directions. Results: The results indicate that combination model offers feasible for predicting risks Conclusions: Further studies broader participant pool varied conditions could enhance applicability this method occupational ergonomics.

Язык: Английский

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