Pub Date : 2023-10-25DOI: 10.1177/21695067231196249
Lacey M. Davis, Barrett S. Caldwell
Function allocation and distributed task coordination are complex challenges facing many multi-team systems. These challenges are intensified in the case of human expeditions to and exploration of Mars, due to the impact of one-way light-time communication delays that can exceed 20 minutes. Research to identify, enhance, and support new requirements for task coordination and communication include considerations to mitigate the impact of delays through improved state monitoring and crew coordination and knowledge sharing techniques. Effective coordination for human cislunar and Mars exploration operations, including servicing, assembly, and maintenance activities, require effective and adaptive function and task allocation constrained by available bandwidth and crew member workload capability. The authors describe some of their previous research and ongoing activities, including improvements to time-delayed information and data displays to support mission control and spaceflight crew member situational awareness when conducting both routine operations and real-time responses to emerging anomalies.
{"title":"Distributed Supervisory Coordination and Function Allocation Between Multi-Teams in Crewed Space Exploration with Time Delays","authors":"Lacey M. Davis, Barrett S. Caldwell","doi":"10.1177/21695067231196249","DOIUrl":"https://doi.org/10.1177/21695067231196249","url":null,"abstract":"Function allocation and distributed task coordination are complex challenges facing many multi-team systems. These challenges are intensified in the case of human expeditions to and exploration of Mars, due to the impact of one-way light-time communication delays that can exceed 20 minutes. Research to identify, enhance, and support new requirements for task coordination and communication include considerations to mitigate the impact of delays through improved state monitoring and crew coordination and knowledge sharing techniques. Effective coordination for human cislunar and Mars exploration operations, including servicing, assembly, and maintenance activities, require effective and adaptive function and task allocation constrained by available bandwidth and crew member workload capability. The authors describe some of their previous research and ongoing activities, including improvements to time-delayed information and data displays to support mission control and spaceflight crew member situational awareness when conducting both routine operations and real-time responses to emerging anomalies.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"39 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192623
Stephen Bao, Jia-Hua Lin, Ninica Howard, Wonil Lee
This paper presents the development of a Workload Calculator specific to the commercial office building janitorial service industry. This is developed due to the increased need for addressing the high workload issues and increased work-related injury claims in the industry. Actual field data were collected to quantify various workload measures and the risks are evaluated with widely used ergonomics job evaluation methods. These workload measures include work pace, overall workload (steps walked and energy expenditure demands, hand/wrist, shoulder and low back biomechanical exposures). This developed workload calculator can provide industrial practitioners with a tool to estimate the workload of planned or existing jobs. It can also help EHS, safety and ergonomics practitioners to identify issues and develop focused interventions. It can also provide cleaning equipment manufacturers and cleaning method developers means to identify issues in the current system and develop new equipment and methods for improvement.
{"title":"Development of Janitors’ Workload Calculator","authors":"Stephen Bao, Jia-Hua Lin, Ninica Howard, Wonil Lee","doi":"10.1177/21695067231192623","DOIUrl":"https://doi.org/10.1177/21695067231192623","url":null,"abstract":"This paper presents the development of a Workload Calculator specific to the commercial office building janitorial service industry. This is developed due to the increased need for addressing the high workload issues and increased work-related injury claims in the industry. Actual field data were collected to quantify various workload measures and the risks are evaluated with widely used ergonomics job evaluation methods. These workload measures include work pace, overall workload (steps walked and energy expenditure demands, hand/wrist, shoulder and low back biomechanical exposures). This developed workload calculator can provide industrial practitioners with a tool to estimate the workload of planned or existing jobs. It can also help EHS, safety and ergonomics practitioners to identify issues and develop focused interventions. It can also provide cleaning equipment manufacturers and cleaning method developers means to identify issues in the current system and develop new equipment and methods for improvement.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"59 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192578
Shannon C. Roberts, Maryam Zahabi, Lauren McCarthy, Christopher Lanclos, Jonathan Romero
Cyberattacks on law enforcement officers’ vehicles can compromise and disrupt emergency response, yet there is little knowledge of how law enforcement officers understand and respond to cybersecurity concerns within their work vehicles. This study gathered data on law enforcement officers’ perceptions of vehicle cybersecurity. A survey study was conducted with 80 law enforcement officers, focusing on gathering information to quantify the relationship between their experience as an officer, their cybersecurity experience, and their perception of vehicle cybersecurity. Overall, there was a lack of understanding and knowledge regarding vehicle cybersecurity. Results also revealed a small relationship between how many hours an officer spends in the vehicle and vehicle cybersecurity and an even stronger relationship between the officer’s cybercrime experience and their perceptions of vehicle cybersecurity. With appropriate interventions (e.g., training), there is an opportunity to impact law enforcement behavior to positively promote vehicle cybersecurity resiliency.
{"title":"Law Enforcement Perspectives on Police Vehicle Cybersecurity","authors":"Shannon C. Roberts, Maryam Zahabi, Lauren McCarthy, Christopher Lanclos, Jonathan Romero","doi":"10.1177/21695067231192578","DOIUrl":"https://doi.org/10.1177/21695067231192578","url":null,"abstract":"Cyberattacks on law enforcement officers’ vehicles can compromise and disrupt emergency response, yet there is little knowledge of how law enforcement officers understand and respond to cybersecurity concerns within their work vehicles. This study gathered data on law enforcement officers’ perceptions of vehicle cybersecurity. A survey study was conducted with 80 law enforcement officers, focusing on gathering information to quantify the relationship between their experience as an officer, their cybersecurity experience, and their perception of vehicle cybersecurity. Overall, there was a lack of understanding and knowledge regarding vehicle cybersecurity. Results also revealed a small relationship between how many hours an officer spends in the vehicle and vehicle cybersecurity and an even stronger relationship between the officer’s cybercrime experience and their perceptions of vehicle cybersecurity. With appropriate interventions (e.g., training), there is an opportunity to impact law enforcement behavior to positively promote vehicle cybersecurity resiliency.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"56 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135170210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192575
Ronald L. Boring, Thomas A. Ulrich, Roger Lew
Much has been written about levels of automation (LOA), but comparatively little has been written about levels of digitization (LODi) and levels of digitalization (LODa). Digitization is a digital representation of analog information and is typical of migration from analog to digital control systems, digitalization involves enhancing the functionality of digital information, and automation changes control from humans to machines. Each of these technology implementations has its own scales, and each forms a viable type of functionality that should be considered not as a continuum toward automation but rather as separate categories of solutions that meet the needs of advanced reactors. In this paper we develop separate LODi, LODa, and LOA scales and demonstrate how conflation of these technologies, using the example of computer-based procedures, can lead to confusion in the design process. With the race to develop advanced reactors, the surest metric of success and safety is proper consideration of the right technology requirements for different control systems.
{"title":"Levels of Digitization, Digitalization, and Automation for Advanced Reactors","authors":"Ronald L. Boring, Thomas A. Ulrich, Roger Lew","doi":"10.1177/21695067231192575","DOIUrl":"https://doi.org/10.1177/21695067231192575","url":null,"abstract":"Much has been written about levels of automation (LOA), but comparatively little has been written about levels of digitization (LODi) and levels of digitalization (LODa). Digitization is a digital representation of analog information and is typical of migration from analog to digital control systems, digitalization involves enhancing the functionality of digital information, and automation changes control from humans to machines. Each of these technology implementations has its own scales, and each forms a viable type of functionality that should be considered not as a continuum toward automation but rather as separate categories of solutions that meet the needs of advanced reactors. In this paper we develop separate LODi, LODa, and LOA scales and demonstrate how conflation of these technologies, using the example of computer-based procedures, can lead to confusion in the design process. With the race to develop advanced reactors, the surest metric of success and safety is proper consideration of the right technology requirements for different control systems.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"29 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135216321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192858
Shruti M. Amre, Kelly S. Steelman
Advanced driver-assist systems (ADAS) enable drivers to relinquish operational control of the vehicle to automation for part of the total drive. While these features are engaged, drivers have an increased risk of losing awareness of their environment. Current ADAS broadly utilizes hands-on-the-wheel or eyes-on-the-road driver supervision strategies to continually monitor steering-wheel torque and drivers’ head and eye positions to ensure driver attention. The current work examines the effect of hands-on-the-wheel and eyes-on-the-road driver supervision strategies on change detection, mind wandering, and gaze behavior in a low-fidelity semi-autonomous driving task.
{"title":"Keep your hands on the wheel: The effect of driver supervision strategy on change detection, mind wandering, and gaze behavior","authors":"Shruti M. Amre, Kelly S. Steelman","doi":"10.1177/21695067231192858","DOIUrl":"https://doi.org/10.1177/21695067231192858","url":null,"abstract":"Advanced driver-assist systems (ADAS) enable drivers to relinquish operational control of the vehicle to automation for part of the total drive. While these features are engaged, drivers have an increased risk of losing awareness of their environment. Current ADAS broadly utilizes hands-on-the-wheel or eyes-on-the-road driver supervision strategies to continually monitor steering-wheel torque and drivers’ head and eye positions to ensure driver attention. The current work examines the effect of hands-on-the-wheel and eyes-on-the-road driver supervision strategies on change detection, mind wandering, and gaze behavior in a low-fidelity semi-autonomous driving task.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"46 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135217671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231193675
Jiahui Ma, Elizabeth A. Johnson, Bernadette McCrory
Multimodal online learning environment improves learning experience through different modalities such as visual, auditory, and kinesthetic interactions. Multimodal learning analytics (MMLA) with multiple biosensors provides a way to overcome the challenge of analyzing the multiple interaction types simultaneously. Galvanic skin response/electrodermal activity (GSR/EDA), eye tracking and facial expression were used to measure the learning interaction in a multimodal online learning environment. iMotions and R software were used to post-process and analyze the time-synchronized biosensor data. GSR/EDA, eye tracking and facial expression showed real-time cognitive, emotional, and visual learning engagement for each interaction type. There is a tremendous potential for using MMLA with multiple biosensors to understand learning engagement in a multimodal online learning environment was shown in this study.
{"title":"Understanding Learning Engagement with User-Centered Human-Computer Interaction in a Multimodal Online Learning Environment","authors":"Jiahui Ma, Elizabeth A. Johnson, Bernadette McCrory","doi":"10.1177/21695067231193675","DOIUrl":"https://doi.org/10.1177/21695067231193675","url":null,"abstract":"Multimodal online learning environment improves learning experience through different modalities such as visual, auditory, and kinesthetic interactions. Multimodal learning analytics (MMLA) with multiple biosensors provides a way to overcome the challenge of analyzing the multiple interaction types simultaneously. Galvanic skin response/electrodermal activity (GSR/EDA), eye tracking and facial expression were used to measure the learning interaction in a multimodal online learning environment. iMotions and R software were used to post-process and analyze the time-synchronized biosensor data. GSR/EDA, eye tracking and facial expression showed real-time cognitive, emotional, and visual learning engagement for each interaction type. There is a tremendous potential for using MMLA with multiple biosensors to understand learning engagement in a multimodal online learning environment was shown in this study.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"63 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192653
Jacob J. Banks, Jie Zhou, Chelsea O. Riehle, Neal E. Wiggermann
Transferring a patient from a bed to a wheelchair is important for patient well-being. Research has shown that manually performing this task exposes healthcare workers (HCWs) to lower back kinetic demands that can exceed safety standards, necessitating the use of mechanical lift equipment. However, HCWs still commonly perform this task manually, especially for lighter patients who are capable of assisting. Although lower back kinetic demands are presumably dependent upon the patients (in)ability to assist during the transfer, this has not been systematically tested. Therefore, the primary aim of this research was to compare the peak L5/S1 intervertebral joint (IVJ) compressive force demands during a bed-to-wheelchair manual transfer across different levels of simulated patient assist (0%, 18%, and 36% of patient bodyweight). We also compared peak IVJ compressive forces from an approach using external forces directly measured at the hands of the HCW, with an alternative traditional approach that assumed the patient’s mass was fully lifted by the HCW throughout the transfer. Peak L5/S1 IVJ compressive forces were lower ( p < .001) during the 36% than the 0% and 18% bodyweight patient assist conditions when applying the measured forces at the hand. Overall, peak compressive forces were lower ( p < .001) and tended to occur at different phases of the transfer when applying the measured forces at the hand versus assuming all the patient’s mass was being lifted. Our results emphasize the importance of accurately modeling the forces at the hands when estimating in vivo demands. Further, these findings suggest that encouraging the patient to assist during transfers may reduce IVJ forces on HCWs, but for heavier patients even a modest degree of patient assistance is not likely to protect the HCW from elevated spine loads. In most circumstances, lift equipment is warranted.
{"title":"Applying Forces Measured at the Hands to Estimate L5/S1 Compression During Manual Patient Bed-to-Wheelchair Transfers Across Different Levels of Patient Assist","authors":"Jacob J. Banks, Jie Zhou, Chelsea O. Riehle, Neal E. Wiggermann","doi":"10.1177/21695067231192653","DOIUrl":"https://doi.org/10.1177/21695067231192653","url":null,"abstract":"Transferring a patient from a bed to a wheelchair is important for patient well-being. Research has shown that manually performing this task exposes healthcare workers (HCWs) to lower back kinetic demands that can exceed safety standards, necessitating the use of mechanical lift equipment. However, HCWs still commonly perform this task manually, especially for lighter patients who are capable of assisting. Although lower back kinetic demands are presumably dependent upon the patients (in)ability to assist during the transfer, this has not been systematically tested. Therefore, the primary aim of this research was to compare the peak L5/S1 intervertebral joint (IVJ) compressive force demands during a bed-to-wheelchair manual transfer across different levels of simulated patient assist (0%, 18%, and 36% of patient bodyweight). We also compared peak IVJ compressive forces from an approach using external forces directly measured at the hands of the HCW, with an alternative traditional approach that assumed the patient’s mass was fully lifted by the HCW throughout the transfer. Peak L5/S1 IVJ compressive forces were lower ( p < .001) during the 36% than the 0% and 18% bodyweight patient assist conditions when applying the measured forces at the hand. Overall, peak compressive forces were lower ( p < .001) and tended to occur at different phases of the transfer when applying the measured forces at the hand versus assuming all the patient’s mass was being lifted. Our results emphasize the importance of accurately modeling the forces at the hands when estimating in vivo demands. Further, these findings suggest that encouraging the patient to assist during transfers may reduce IVJ forces on HCWs, but for heavier patients even a modest degree of patient assistance is not likely to protect the HCW from elevated spine loads. In most circumstances, lift equipment is warranted.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192713
Isra K. Elsaadany, Jessica M. Gonzalez-Vargas, Jason Z. Moore, Scarlett R. Miller
Progressive learning gradually increases task difficulty as students advance in their education. One area that can benefit from it is medical education since it can optimize medical trainees’ skill acquisition. While progressive learning can allow for skill transfer to patient encounters, personalized learning increases the efficiency and effectiveness of learning. However, it is not well understood the number of practice trials needed to reach proficiency. To evaluate whether progressive and personalized learning can enhance medical trainees’ learning gains, the learning interface of the Dynamic Haptic Robotic Trainer (DHRT) for Central Venous Catheterization was assessed. Results showed that residents’ performance on the DHRT did not differ based on task difficulty and residents’ performance was as effective with less number of trials. The findings imply a need to integrate progressive and personalized learning on the DHRT simulator to ensure that residents are fully prepared for any patient scenario in a real-life encounter.
{"title":"Progressive Medical Simulation: An Analysis of the Integration of Progressive and Personalized Learning in Central Line Simulators","authors":"Isra K. Elsaadany, Jessica M. Gonzalez-Vargas, Jason Z. Moore, Scarlett R. Miller","doi":"10.1177/21695067231192713","DOIUrl":"https://doi.org/10.1177/21695067231192713","url":null,"abstract":"Progressive learning gradually increases task difficulty as students advance in their education. One area that can benefit from it is medical education since it can optimize medical trainees’ skill acquisition. While progressive learning can allow for skill transfer to patient encounters, personalized learning increases the efficiency and effectiveness of learning. However, it is not well understood the number of practice trials needed to reach proficiency. To evaluate whether progressive and personalized learning can enhance medical trainees’ learning gains, the learning interface of the Dynamic Haptic Robotic Trainer (DHRT) for Central Venous Catheterization was assessed. Results showed that residents’ performance on the DHRT did not differ based on task difficulty and residents’ performance was as effective with less number of trials. The findings imply a need to integrate progressive and personalized learning on the DHRT simulator to ensure that residents are fully prepared for any patient scenario in a real-life encounter.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"17 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192890
Elizabeth Phillips, Lilia Moshkina, Karina Roundtree, Adam Norton, Holly Yanco
A growing number of domains for Human-Robot and Human-Machine Interactions (HRI/HMI) will involve fleets of autonomous machines. In these fleet environments, robots will encounter not just primary interactors in one-on-one encounters, but also secondary, and even tertiary ones who are bystanders to direct human-to-robot interactions. Thus, the interaction paradigms used by such robots may need to be reconsidered to meet a growing diversity of interactions as fleet HRI/HMI applications continue to grow. Relying on use cases from field testing in mock urban environments, the purpose of this paper is to discuss our lessons learned when supporting multiple “layers” of interactors and how they relate to needs that Human Factors and Ergonomic (HF/E) science can help to address.
{"title":"Primary, Secondary, and Tertiary Interactions for Fleet HumanRobot Interaction: Insights from Field Testing","authors":"Elizabeth Phillips, Lilia Moshkina, Karina Roundtree, Adam Norton, Holly Yanco","doi":"10.1177/21695067231192890","DOIUrl":"https://doi.org/10.1177/21695067231192890","url":null,"abstract":"A growing number of domains for Human-Robot and Human-Machine Interactions (HRI/HMI) will involve fleets of autonomous machines. In these fleet environments, robots will encounter not just primary interactors in one-on-one encounters, but also secondary, and even tertiary ones who are bystanders to direct human-to-robot interactions. Thus, the interaction paradigms used by such robots may need to be reconsidered to meet a growing diversity of interactions as fleet HRI/HMI applications continue to grow. Relying on use cases from field testing in mock urban environments, the purpose of this paper is to discuss our lessons learned when supporting multiple “layers” of interactors and how they relate to needs that Human Factors and Ergonomic (HF/E) science can help to address.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"37 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135168440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192435
Sakshi Pranay Taori, Sol Lim
The objective of this study was to evaluate the performance of machine learning (ML) algorithms developed using surface electromyography (EMG) armband sensor data in predicting hand-load levels (5 lb and 15 lb) from diverse lifting trials. Twelve healthy participants (six male and six female) performed repetitive lifting with three different lifting conditions, i.e., symmetric (S), asymmetric (A), and free-dynamic (F) lifts. ML models were developed with four lifting datasets (S, A, S+A, and F) and were cross-validated using F as the test dataset. Mean classification accuracy was significantly lower in models developed with the S dataset (78.8%) compared to A (83.2%) and F (83.4%). Findings indicate that the ML model developed with controlled symmetric lifts was less accurate in predicting the load of more dynamic, unconstrained lifts, which is common in real-world settings.
{"title":"Comparing Armband EMG-based Lifting Load Classification Algorithms using Various Lifting Trials","authors":"Sakshi Pranay Taori, Sol Lim","doi":"10.1177/21695067231192435","DOIUrl":"https://doi.org/10.1177/21695067231192435","url":null,"abstract":"The objective of this study was to evaluate the performance of machine learning (ML) algorithms developed using surface electromyography (EMG) armband sensor data in predicting hand-load levels (5 lb and 15 lb) from diverse lifting trials. Twelve healthy participants (six male and six female) performed repetitive lifting with three different lifting conditions, i.e., symmetric (S), asymmetric (A), and free-dynamic (F) lifts. ML models were developed with four lifting datasets (S, A, S+A, and F) and were cross-validated using F as the test dataset. Mean classification accuracy was significantly lower in models developed with the S dataset (78.8%) compared to A (83.2%) and F (83.4%). Findings indicate that the ML model developed with controlled symmetric lifts was less accurate in predicting the load of more dynamic, unconstrained lifts, which is common in real-world settings.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135215970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：