Published: Oct. 28, 2024
Language: Английский
Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 650 - 650
Published: Jan. 10, 2025
This study addresses a critical gap in human activity recognition (HAR) research by enhancing both the explainability and efficiency of classification collaborative human–robot systems, particularly agricultural environments. While traditional HAR models often prioritize improving overall accuracy, they typically lack transparency how sensor data contribute to decision-making. To fill this gap, integrates explainable artificial intelligence, specifically SHapley Additive exPlanations (SHAP), thus interpretability model. Data were collected from 20 participants who wore five inertial measurement units (IMUs) at various body positions while performing material handling tasks involving an unmanned ground vehicle field harvesting scenario. The results highlight central role torso-mounted sensors, lumbar region, cervix, chest, capturing core movements, wrist sensors provided useful complementary information, especially for load-related activities. XGBoost-based model, selected mainly allowing in-depth analysis feature contributions considerably reducing complexity calculations, demonstrated strong performance HAR. findings indicate that future should focus on enlarging dataset, investigating use additional placements, real-world trials enhance model’s generalizability adaptability practical applications.
Language: Английский
Citations
3
Sensors, Journal Year: 2024, Volume and Issue: 24(17), P. 5473 - 5473
Published: Aug. 23, 2024
The paradigm of Industry 5.0 pushes the transition from traditional to a novel, smart, digital, and connected industry, where well-being is key enhance productivity, optimize man–machine interaction guarantee workers’ safety. This work aims conduct systematic review current methodologies for monitoring analyzing physical cognitive ergonomics. Three research questions are addressed: (1) which technologies used assess workers in workplace, (2) how acquired data processed, (3) what purpose this evaluated for. way, individual factors within holistic assessment worker highlighted, information provided synthetically. analysis was conducted following PRISMA 2020 statement guidelines. From sixty-five articles collected, most adopted technological solutions, parameters, processing were identified. Wearable inertial measurement units RGB-D cameras prevalent devices monitoring; ergonomics, cardiac activity physiological parameter. Furthermore, insights on practical issues future developments provided. Future should focus developing multi-modal systems that combine these aspects with particular emphasis their application real industrial settings.
Language: Английский
Citations
16
Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103110 - 103110
Published: Jan. 13, 2025
Language: Английский
Citations
1
Advances in medical technologies and clinical practice book series, Journal Year: 2024, Volume and Issue: unknown, P. 221 - 246
Published: June 14, 2024
This chapter focuses on how human-centred methods and cutting-edge technologies are combined to create new opportunities for creativity synergy. Industry 6.0 aims improve decision-making, communication, problem-solving within the industrial ecosystem by utilizing emotional intelligence. explores role that intelligence plays in maximizing human-machine collaboration, discussing opportunities, problems, consequences advancement of industry future. It reveals revolutionary power influencing dynamics through a thorough analysis. deals with summary 6.0, emphasizing its focus using enhance rather than replace human talents. The shift towards emotionally intelligent collaboration represents key milestone evolution holds potential revolutionize future work.
Language: Английский
Citations
4
Published: Jan. 1, 2025
Mental workload assessment is critical in professional environments where cognitive demands influence productivity and well-being. Traditional methods for assessing mental workload, which often rely on subjective measures, lack the reliability required real-time applications. This study presents an innovative approach to measuring by integrating machine learning with electroencephalography data enhance objectivity. Using Emotiv headset, brain activity was collected while participants performed job simulation tasks. The employed a dual-model framework: ResNet-34 deep model analyzed Power Spectral Density images of activity, achieving classification accuracy 70\%, Support Vector Machine trained task performance metrics NASA Task Load Index self-assessment independently classified levels. outputs these models were combined meta-learning framework, further improved achieved significant gains. validated incorporated into mobile application, enabling workload. framework demonstrates potential scalable monitoring adaptive management across various industries. Future research aims incorporate additional physiological explore clinical
Language: Английский
Citations
0
Pervasive and Mobile Computing, Journal Year: 2025, Volume and Issue: unknown, P. 102048 - 102048
Published: April 1, 2025
Language: Английский
Citations
0
American Journal of Industrial Medicine, Journal Year: 2025, Volume and Issue: unknown
Published: May 1, 2025
Starting in the 1970s with robots that were physically isolated from contact their human co-workers, now collaborate workers towards a common task goal shared workspace. This type of robotic device represents new era workplace automation. Industrial robotics is rapidly evolving due to advances sensor technology, artificial intelligence (AI), wireless communications, mechanical engineering, and materials science. While these devices are used mainly manufacturing warehousing, human-robot collaboration seen across multiple goods-producing service-delivery industry sectors. Assessing controlling risks critical challenge for occupational safety health research practice as industrial becomes pervasive feature future work. Understanding physical, psychosocial, work organization, cybersecurity associated increasing use technologies ensuring safe development implementation robotics. commentary provides brief review uses selected sectors; current applications worker employer alike; strategies integrating into management system; role standards
Language: Английский
Citations
0
Journal of Manufacturing Systems, Journal Year: 2024, Volume and Issue: 78, P. 26 - 45
Published: Nov. 23, 2024
Language: Английский
Citations
3
Health Scope, Journal Year: 2025, Volume and Issue: 14(1)
Published: Feb. 19, 2025
Background: The rapid advancement of robotics and artificial intelligence is poised to revolutionize industrial settings through widespread automation. This study investigates the impact robotic assistance on human operator mental workload (MWL) within a simulated environment. Utilizing electroencephalography (EEG) measure changes in alpha theta band power, we aim identify cognitive challenges associated with human-robot collaboration (HRC) inform design safer more efficient collaborative systems. Objectives: main objective current was assess MWL interaction (HRI) task. Methods: EEG data were collected from 17 participants (aged 25 - 35 years) using 64-channel system while they engaged an ecologically valid task that induced three distinct levels load: Low, medium, high. Subsequent analysis focused power frequency bands, employing repeated-measures ANOVA load brain activity. Results: A revealed significant across different difficulty levels. bands F3, F4, Fz, as well alpha, beta, gamma P3, P4, Pz, emerged promising indicators for differentiating between varying tasks. Conclusions: Electroencephalography spectral particularly reliable indicator MWL. These exhibit dynamic response fluctuating demands, especially human-robotic
Language: Английский
Citations
0
Applied Ergonomics, Journal Year: 2024, Volume and Issue: 123, P. 104418 - 104418
Published: Nov. 16, 2024
In the era of Industry 4.0, study Human-Robot Collaboration (HRC) in advancing modern manufacturing and automation is paramount. An operator approaching a collaborative robot (cobot) may have feelings distrust, experience discomfort stress, especially during early stages training. Human factors cannot be neglected: for efficient implementation, complex psycho-physiological state responses must taken into consideration. this study, volunteers were asked to carry out set cobot programming tasks, while several physiological signals, such as electroencephalogram (EEG), electrocardiogram (ECG), Galvanic skin response (GSR), facial expressions recorded. addition, subjective questionnaire (NASA-TLX) was administered at end, assess if derived parameters are related perception stress. Parameters exhibiting higher degree alignment with mean Theta (76.67%), Alpha (70.53%) Beta (67.65%) power extracted from EEG, recovery time (72.86%) rise (71.43%) GSR heart rate variability (HRV) metrics PNN25 (71.58%), SDNN (70.53%), PNN50 (68.95%) RMSSD (66.84%). raw RR Intervals appear more variable less accurate (42.11%) so recorded emotions (51.43%).
Language: Английский
Citations
2