{"title":"基于肌电图的假体装置研究中认知负荷评估技术的比较","authors":"Junho Park, Maryam Zahabi","doi":"10.1109/SMC42975.2020.9283229","DOIUrl":null,"url":null,"abstract":"Previous studies have found that electromyography (EMG)-based prosthetic devices provide higher grasping force, increase functional performance, and have greater range of motion over conventional prostheses. However, cognitive workload (CW) is still one of the issues that can negatively affect device usability and satisfaction. In order to evaluate CW of prosthetic devices early in the design cycle, it is first necessary to select the most appropriate measures. Therefore, the objectives of this study were to: (1) review the CW measurement techniques used in prior EMG-based prosthetic device evaluations; and (2) provide guidelines to select the most appropriate measurement techniques. The findings suggested that cognitive performance models (CPM), subjective measures, task performance measures, and some physiological measures were sensitive in detecting CW differences among prosthetic device configurations and therefore could be useful tools in usability evaluation of these technologies. However, in order to reduce intrusiveness and cost, methods such as subjective workload measures, task performance, and CPM are more beneficial as compared to physiological measurements. Guidelines proposed in this study can be beneficial to select the most appropriate CW measurement techniques in order to improve sensitivity and accuracy and reduce intrusiveness and cost.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"370 1","pages":"1242-1248"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Comparison of Cognitive Workload Assessment Techniques in EMG-based Prosthetic Device Studies\",\"authors\":\"Junho Park, Maryam Zahabi\",\"doi\":\"10.1109/SMC42975.2020.9283229\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Previous studies have found that electromyography (EMG)-based prosthetic devices provide higher grasping force, increase functional performance, and have greater range of motion over conventional prostheses. However, cognitive workload (CW) is still one of the issues that can negatively affect device usability and satisfaction. In order to evaluate CW of prosthetic devices early in the design cycle, it is first necessary to select the most appropriate measures. Therefore, the objectives of this study were to: (1) review the CW measurement techniques used in prior EMG-based prosthetic device evaluations; and (2) provide guidelines to select the most appropriate measurement techniques. The findings suggested that cognitive performance models (CPM), subjective measures, task performance measures, and some physiological measures were sensitive in detecting CW differences among prosthetic device configurations and therefore could be useful tools in usability evaluation of these technologies. However, in order to reduce intrusiveness and cost, methods such as subjective workload measures, task performance, and CPM are more beneficial as compared to physiological measurements. Guidelines proposed in this study can be beneficial to select the most appropriate CW measurement techniques in order to improve sensitivity and accuracy and reduce intrusiveness and cost.\",\"PeriodicalId\":6718,\"journal\":{\"name\":\"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)\",\"volume\":\"370 1\",\"pages\":\"1242-1248\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMC42975.2020.9283229\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMC42975.2020.9283229","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of Cognitive Workload Assessment Techniques in EMG-based Prosthetic Device Studies
Previous studies have found that electromyography (EMG)-based prosthetic devices provide higher grasping force, increase functional performance, and have greater range of motion over conventional prostheses. However, cognitive workload (CW) is still one of the issues that can negatively affect device usability and satisfaction. In order to evaluate CW of prosthetic devices early in the design cycle, it is first necessary to select the most appropriate measures. Therefore, the objectives of this study were to: (1) review the CW measurement techniques used in prior EMG-based prosthetic device evaluations; and (2) provide guidelines to select the most appropriate measurement techniques. The findings suggested that cognitive performance models (CPM), subjective measures, task performance measures, and some physiological measures were sensitive in detecting CW differences among prosthetic device configurations and therefore could be useful tools in usability evaluation of these technologies. However, in order to reduce intrusiveness and cost, methods such as subjective workload measures, task performance, and CPM are more beneficial as compared to physiological measurements. Guidelines proposed in this study can be beneficial to select the most appropriate CW measurement techniques in order to improve sensitivity and accuracy and reduce intrusiveness and cost.