The effects of various auditory takeover requests: A simulated driving study considering the modality of non-driving-related tasks
Appl Ergon. 2024 Feb 27;118:104252. doi: 10.1016/j.apergo.2024.104252. Online ahead of print.ABSTRACTWith the era of automated driving approaching, designing an effective auditory takeover request (TOR) is critical to ensure automated driving safety. The present study investigated the effects of speech-based (speech and spearcon) and non-speech-based (earcon and auditory icon) TORs on takeover performance and subjective preferences. The potential impact of the non-driving-related task (NDRT) modality on auditory TORs was considered. Thirty-two participants were recruited in the present study and assigned to two groups, wit...
Source: Applied Ergonomics - February 28, 2024 Category: Occupational Health Authors: Chunlei Chai Yu Lei Haoran Wei Changxu Wu Wei Zhang Preben Hansen Hao Fan Jinlei Shi Source Type: research
Machine learning prediction of footwear slip resistance on glycerol-contaminated surfaces: A pilot study
In this study, we aimed to develop an Artificial Intelligence model capable of classifying footwear as having either high or low slip resistance based on the geometric characteristics and material parameters of their outsoles. Our model was trained on a unique dataset comprising images of 37 indoor work footwear outsoles made of rubber. To evaluate the slip resistant property of the footwear, all samples were tested using a cart-type friction measurement device, and the static and dynamic Coefficient of Frictions (COFs) of each outsole was determined on a glycerol-contaminated surface. Machine learning techniques were impl...
Source: Applied Ergonomics - February 18, 2024 Category: Occupational Health Authors: Kaylie Lau Takeshi Yamaguchi Kei Shibata Toshiaki Nishi Geoff Fernie Atena Roshan Fekr Source Type: research
Machine learning prediction of footwear slip resistance on glycerol-contaminated surfaces: A pilot study
In this study, we aimed to develop an Artificial Intelligence model capable of classifying footwear as having either high or low slip resistance based on the geometric characteristics and material parameters of their outsoles. Our model was trained on a unique dataset comprising images of 37 indoor work footwear outsoles made of rubber. To evaluate the slip resistant property of the footwear, all samples were tested using a cart-type friction measurement device, and the static and dynamic Coefficient of Frictions (COFs) of each outsole was determined on a glycerol-contaminated surface. Machine learning techniques were impl...
Source: Applied Ergonomics - February 18, 2024 Category: Occupational Health Authors: Kaylie Lau Takeshi Yamaguchi Kei Shibata Toshiaki Nishi Geoff Fernie Atena Roshan Fekr Source Type: research
Machine learning prediction of footwear slip resistance on glycerol-contaminated surfaces: A pilot study
In this study, we aimed to develop an Artificial Intelligence model capable of classifying footwear as having either high or low slip resistance based on the geometric characteristics and material parameters of their outsoles. Our model was trained on a unique dataset comprising images of 37 indoor work footwear outsoles made of rubber. To evaluate the slip resistant property of the footwear, all samples were tested using a cart-type friction measurement device, and the static and dynamic Coefficient of Frictions (COFs) of each outsole was determined on a glycerol-contaminated surface. Machine learning techniques were impl...
Source: Applied Ergonomics - February 18, 2024 Category: Occupational Health Authors: Kaylie Lau Takeshi Yamaguchi Kei Shibata Toshiaki Nishi Geoff Fernie Atena Roshan Fekr Source Type: research
Machine learning prediction of footwear slip resistance on glycerol-contaminated surfaces: A pilot study
In this study, we aimed to develop an Artificial Intelligence model capable of classifying footwear as having either high or low slip resistance based on the geometric characteristics and material parameters of their outsoles. Our model was trained on a unique dataset comprising images of 37 indoor work footwear outsoles made of rubber. To evaluate the slip resistant property of the footwear, all samples were tested using a cart-type friction measurement device, and the static and dynamic Coefficient of Frictions (COFs) of each outsole was determined on a glycerol-contaminated surface. Machine learning techniques were impl...
Source: Applied Ergonomics - February 18, 2024 Category: Occupational Health Authors: Kaylie Lau Takeshi Yamaguchi Kei Shibata Toshiaki Nishi Geoff Fernie Atena Roshan Fekr Source Type: research
Machine learning prediction of footwear slip resistance on glycerol-contaminated surfaces: A pilot study
In this study, we aimed to develop an Artificial Intelligence model capable of classifying footwear as having either high or low slip resistance based on the geometric characteristics and material parameters of their outsoles. Our model was trained on a unique dataset comprising images of 37 indoor work footwear outsoles made of rubber. To evaluate the slip resistant property of the footwear, all samples were tested using a cart-type friction measurement device, and the static and dynamic Coefficient of Frictions (COFs) of each outsole was determined on a glycerol-contaminated surface. Machine learning techniques were impl...
Source: Applied Ergonomics - February 18, 2024 Category: Occupational Health Authors: Kaylie Lau Takeshi Yamaguchi Kei Shibata Toshiaki Nishi Geoff Fernie Atena Roshan Fekr Source Type: research
Machine learning prediction of footwear slip resistance on glycerol-contaminated surfaces: A pilot study
In this study, we aimed to develop an Artificial Intelligence model capable of classifying footwear as having either high or low slip resistance based on the geometric characteristics and material parameters of their outsoles. Our model was trained on a unique dataset comprising images of 37 indoor work footwear outsoles made of rubber. To evaluate the slip resistant property of the footwear, all samples were tested using a cart-type friction measurement device, and the static and dynamic Coefficient of Frictions (COFs) of each outsole was determined on a glycerol-contaminated surface. Machine learning techniques were impl...
Source: Applied Ergonomics - February 18, 2024 Category: Occupational Health Authors: Kaylie Lau Takeshi Yamaguchi Kei Shibata Toshiaki Nishi Geoff Fernie Atena Roshan Fekr Source Type: research
Machine learning prediction of footwear slip resistance on glycerol-contaminated surfaces: A pilot study
In this study, we aimed to develop an Artificial Intelligence model capable of classifying footwear as having either high or low slip resistance based on the geometric characteristics and material parameters of their outsoles. Our model was trained on a unique dataset comprising images of 37 indoor work footwear outsoles made of rubber. To evaluate the slip resistant property of the footwear, all samples were tested using a cart-type friction measurement device, and the static and dynamic Coefficient of Frictions (COFs) of each outsole was determined on a glycerol-contaminated surface. Machine learning techniques were impl...
Source: Applied Ergonomics - February 18, 2024 Category: Occupational Health Authors: Kaylie Lau Takeshi Yamaguchi Kei Shibata Toshiaki Nishi Geoff Fernie Atena Roshan Fekr Source Type: research
Machine learning prediction of footwear slip resistance on glycerol-contaminated surfaces: A pilot study
In this study, we aimed to develop an Artificial Intelligence model capable of classifying footwear as having either high or low slip resistance based on the geometric characteristics and material parameters of their outsoles. Our model was trained on a unique dataset comprising images of 37 indoor work footwear outsoles made of rubber. To evaluate the slip resistant property of the footwear, all samples were tested using a cart-type friction measurement device, and the static and dynamic Coefficient of Frictions (COFs) of each outsole was determined on a glycerol-contaminated surface. Machine learning techniques were impl...
Source: Applied Ergonomics - February 18, 2024 Category: Occupational Health Authors: Kaylie Lau Takeshi Yamaguchi Kei Shibata Toshiaki Nishi Geoff Fernie Atena Roshan Fekr Source Type: research
Resilient interactions between cyclists and drivers, and what does this mean for automated vehicles?
Appl Ergon. 2024 Feb 13;117:104237. doi: 10.1016/j.apergo.2024.104237. Online ahead of print.ABSTRACTThe road transport system is a complex sociotechnical system that relies on a number of formal and informal rules of the road to ensure safety and resilience. Interactions between vulnerable road users and drivers often includes informal communication channels that are tightly linked to social norms, user expectations and the environmental context. Automated vehicles have a challenge in being able to communicate and respond to these informal rules of the road, therefore additional technologies are required to better support...
Source: Applied Ergonomics - February 14, 2024 Category: Occupational Health Authors: Katie J Parnell Siobhan E Merriman Katherine L Plant Source Type: research
Updating design guidelines for cognitive ergonomics in human-centred collaborative robotics applications: An expert survey
Appl Ergon. 2024 Feb 13;117:104246. doi: 10.1016/j.apergo.2024.104246. Online ahead of print.ABSTRACTWithin the framework of Industry 5.0, human factors are essential for enhancing the work conditions and well-being of operators interacting with even more advanced and smart manufacturing systems and machines and increasing production performances. Nevertheless, cognitive ergonomics is often underestimated when implementing advanced industrial human-robot interaction. Thus, this work aims to systematically update, develop, and validate guidelines to assist non-experts in the early stages of the design of anthropocentric and...
Source: Applied Ergonomics - February 14, 2024 Category: Occupational Health Authors: Luca Gualtieri Federico Fraboni Hannah Brendel Luca Pietrantoni Renato Vidoni Patrick Dallasega Source Type: research
A seasonal comparison of a 14-day swing on cognitive function and psycho-physiological responses in mine service workers
This study assessed the effect of season on cognitive function and psycho-physiological responses during a 14-day swing in mine-service workers. Cognitive function, thermal sensation and comfort, rating of perceived exertion, fatigue, hydration, core temperature and heart rate were assessed throughout a shift, on three separate days over a swing. Working memory and processing efficiency did not differ between seasons (p > 0.05), however counting and recall latencies improved throughout the swing (p < 0.05). Participants reported greater fatigue post-shift compared to pre-shift (p < 0.05). Thermal sensation, therma...
Source: Applied Ergonomics - February 14, 2024 Category: Occupational Health Authors: Sarah M Taggart Olivier Girard Grant J Landers Ullrich K H Ecker Karen E Wallman Source Type: research
Resilient interactions between cyclists and drivers, and what does this mean for automated vehicles?
Appl Ergon. 2024 Feb 13;117:104237. doi: 10.1016/j.apergo.2024.104237. Online ahead of print.ABSTRACTThe road transport system is a complex sociotechnical system that relies on a number of formal and informal rules of the road to ensure safety and resilience. Interactions between vulnerable road users and drivers often includes informal communication channels that are tightly linked to social norms, user expectations and the environmental context. Automated vehicles have a challenge in being able to communicate and respond to these informal rules of the road, therefore additional technologies are required to better support...
Source: Applied Ergonomics - February 14, 2024 Category: Occupational Health Authors: Katie J Parnell Siobhan E Merriman Katherine L Plant Source Type: research
Updating design guidelines for cognitive ergonomics in human-centred collaborative robotics applications: An expert survey
Appl Ergon. 2024 Feb 13;117:104246. doi: 10.1016/j.apergo.2024.104246. Online ahead of print.ABSTRACTWithin the framework of Industry 5.0, human factors are essential for enhancing the work conditions and well-being of operators interacting with even more advanced and smart manufacturing systems and machines and increasing production performances. Nevertheless, cognitive ergonomics is often underestimated when implementing advanced industrial human-robot interaction. Thus, this work aims to systematically update, develop, and validate guidelines to assist non-experts in the early stages of the design of anthropocentric and...
Source: Applied Ergonomics - February 14, 2024 Category: Occupational Health Authors: Luca Gualtieri Federico Fraboni Hannah Brendel Luca Pietrantoni Renato Vidoni Patrick Dallasega Source Type: research
A seasonal comparison of a 14-day swing on cognitive function and psycho-physiological responses in mine service workers
This study assessed the effect of season on cognitive function and psycho-physiological responses during a 14-day swing in mine-service workers. Cognitive function, thermal sensation and comfort, rating of perceived exertion, fatigue, hydration, core temperature and heart rate were assessed throughout a shift, on three separate days over a swing. Working memory and processing efficiency did not differ between seasons (p > 0.05), however counting and recall latencies improved throughout the swing (p < 0.05). Participants reported greater fatigue post-shift compared to pre-shift (p < 0.05). Thermal sensation, therma...
Source: Applied Ergonomics - February 14, 2024 Category: Occupational Health Authors: Sarah M Taggart Olivier Girard Grant J Landers Ullrich K H Ecker Karen E Wallman Source Type: research
