IEEE Transactions on Human-Machine Systems最新文献_第8页

IEEE Transactions on Human-Machine Systems Information for Authors 电气和电子工程师学会《人机系统学报》（IEEE Transactions on Human-Machine Systems）为作者提供的信息

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-01-31 DOI: 10.1109/THMS.2024.3356020

引用次数: 0

IEEE Systems, Man, and Cybernetics Society Information 电气和电子工程师学会系统、人和控制论学会信息

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-01-31 DOI: 10.1109/THMS.2024.3356018

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Share Your Preprint Research with the World! 与世界分享您的预印本研究成果

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-01-31 DOI: 10.1109/THMS.2024.3356022

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Present a World of Opportunity 呈现一个充满机遇的世界

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-01-31 DOI: 10.1109/THMS.2024.3356024

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IEEE Systems, Man, and Cybernetics Society Information 电气和电子工程师学会系统、人和控制论学会信息

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-01-31 DOI: 10.1109/THMS.2024.3356016

引用次数: 0

A Deep Learning Based Lightweight Human Activity Recognition System Using Reconstructed WiFi CSI 利用重构 WiFi CSI 的基于深度学习的轻量级人类活动识别系统

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-01-30 DOI: 10.1109/THMS.2023.3348694

Xingcan Chen;Yi Zou;Chenglin Li;Wendong Xiao

引用次数: 0

Head-Pose Estimation Based on Lateral Canthus Localizations in 2-D Images 基于二维图像中侧眦定位的头部姿势估计

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-01-26 DOI: 10.1109/THMS.2024.3351138

Shu-Nung Yao;Chang-Wei Huang

{"title":"Head-Pose Estimation Based on Lateral Canthus Localizations in 2-D Images","authors":"Shu-Nung Yao;Chang-Wei Huang","doi":"10.1109/THMS.2024.3351138","DOIUrl":"10.1109/THMS.2024.3351138","url":null,"abstract":"Head-pose estimation plays an important role in computer vision. The head-pose estimation aims to determine the orientation of a human head by representing the yaw, pitch, and roll angles. Implementations can be achieved by different techniques depending on the type of input and training data. This article presents a simple three-dimensional (3-D) face model for estimating head poses. The personalized 3-D face model is constructed by 2-D face photographs. A frontal face photograph determines the plane coordinates of facial features. By knowing the yaw angles in the other averted face photograph, the depth coordinates can be determined. The yaw angle of the averted face is evaluated by the canthus positions. Once the 3-D face model is constructed, we can find the matching angles for a target head pose in a query 2-D photograph. The personalized 3-D face model rotates itself about the \u0000<italic>x</i>\u0000-, \u0000<italic>y</i>\u0000-, and \u0000<italic>z</i>\u0000-axes and then projects its facial features onto plane coordinates. If the rotation angles are correct, the disparities between the 2-D facial features and those in the query face photograph are supposed to be at their minimum. The personalized 3-D face model is validated with the University of South Florida human-identification database. The performance of the proposed head-pose estimation is evaluated on the Biwi Kinect head-pose database and Pointing’04 head-pose image database. The results show that the proposed method outperforms state-of-the-art technologies on both benchmark databases.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139950559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lightweight Whole-Body Human Pose Estimation With Two-Stage Refinement Training Strategy 采用两阶段细化训练策略的轻量级全身人体姿态估计

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-01-19 DOI: 10.1109/THMS.2024.3349652

Zhewei Zhang;Mingen Liu;Junyu Shen;Yujun Cheng;Shengjin Wang

{"title":"Lightweight Whole-Body Human Pose Estimation With Two-Stage Refinement Training Strategy","authors":"Zhewei Zhang;Mingen Liu;Junyu Shen;Yujun Cheng;Shengjin Wang","doi":"10.1109/THMS.2024.3349652","DOIUrl":"https://doi.org/10.1109/THMS.2024.3349652","url":null,"abstract":"Human whole-body pose estimation is a challenging task since the model needs to learn more keypoints than the body-only case. To meet the needs of real-time performance while maintaining accuracy is also a hard issue in whole-body pose estimation due to the learning capability of lightweight networks. In order to solve the above problems to a large extent, we propose a light whole-body pose estimation method with an optimized training strategy. The model is designed based on bottom-up architecture as a base network followed by a refinement network. We propose a two-stage training process, which learns rough features in the first stage and then improves estimation precision in the second stage. An online data augmentation procedure is proposed in the second stage to improve refinement performance. We also introduce a separate learning refinement structure that fine-tunes for body, foot, and hand part independently. Experimental results show that our method improves over 8%–10% average precision compared with other lightweight state-of-the-art approaches in the whole-body pose estimation task, with nearly a quarter (25%) size of model parameters saved.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using $B$-Spline Model on Depth Camera Data to Predict Physical Activity Energy Expenditure of Different Levels of Human Exercise 在深度相机数据上使用 B$-样条模型预测人类不同运动水平的体力活动能量消耗

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-01-18 DOI: 10.1109/THMS.2023.3349030

Yi-Ting Hwang;Ya-Ru Hsu;Bor-Shing Lin

{"title":"Using $B$-Spline Model on Depth Camera Data to Predict Physical Activity Energy Expenditure of Different Levels of Human Exercise","authors":"Yi-Ting Hwang;Ya-Ru Hsu;Bor-Shing Lin","doi":"10.1109/THMS.2023.3349030","DOIUrl":"https://doi.org/10.1109/THMS.2023.3349030","url":null,"abstract":"Energy expenditure (EE) is often used to quantify physical activity. Currently, EE is estimated with data collected by inertial measurement units or depth cameras and verified by oxygen consumption data. Due to the different data collection time spans in this system, raw data were split into minute-by-minute windows, and summary statistics for each window were computed. However, using summary statistics to aggregate data might be influenced by redundant noise or result in the loss of valuable information. This article presents a modeling method using functional analysis to characterize the trajectory of the collected skeletal data, thus enabling the effective use of the complete data. Next, the fitted values of the skeletal data can be aligned to the overall EE data and used to predict the overall EE as well as the task-based EE. The study results revealed for metabolic equivalent of task prediction that the root-mean-square error (RMSE) derived for the proposed method was \u0000<inline-formula><tex-math>$< $</tex-math></inline-formula>\u00000.5 and that the mean absolute error (MAE) was approximately 0.3. Models for estimating task-based EE, including EE related to standing and walking task, also exhibited low RMSE and MAE values. Accordingly, the proposed modeling approach is superior to summary statistics for estimating EE in depth camera systems.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EMG-Based Detection of Minimum Effective Load With Robotic-Resistance Leg Extensor Training 基于肌电图的机器人阻力腿部伸展训练最小有效负荷检测

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-01-15 DOI: 10.1109/THMS.2023.3347404

Tamon Miyake;Hiromasa Ito;Naomi Okamura;Yo Kobayashi;Masakatsu G. Fujie;Shigeki Sugano

{"title":"EMG-Based Detection of Minimum Effective Load With Robotic-Resistance Leg Extensor Training","authors":"Tamon Miyake;Hiromasa Ito;Naomi Okamura;Yo Kobayashi;Masakatsu G. Fujie;Shigeki Sugano","doi":"10.1109/THMS.2023.3347404","DOIUrl":"https://doi.org/10.1109/THMS.2023.3347404","url":null,"abstract":"To promote rapid recovery and quality of life after a musculoskeletal disorder, rehabilitation exercises that are suitable for each individual's physical condition are important. In cases of disuse muscle atrophy of the quadriceps, inappropriate training can cause injury. Although resistance-training robotic systems have been developed and could adjust resistance load, a systematic detection method with appropriate force strength for automatic adjustment for each individual has not yet been established. In the current study, we developed an electromyogram (EMG) based method that determines the minimum effective resistance load for muscle growth. Using an integrated EMG (IEMG) model of incremental resistance load focused, we constructed a method to determine the minimum effective resistance load with logarithmic functions. The feasibility of our method was tested with a slow training protocol using a wire-driven leg extension training robot to measure the relationship between IEMG and resistance load by applying the incremental resistance load. The proposed model was found to be suitable for six young and four elderly subjects with different levels of muscle mass, and the load derived for each person was shown to induce effectively acute thigh circumference expansion, which is a factor leading to future muscle hypertrophy.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0