Pub Date : 2024-01-31DOI: 10.1109/THMS.2024.3356020
{"title":"IEEE Transactions on Human-Machine Systems Information for Authors","authors":"","doi":"10.1109/THMS.2024.3356020","DOIUrl":"https://doi.org/10.1109/THMS.2024.3356020","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-31DOI: 10.1109/THMS.2024.3356018
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2024.3356018","DOIUrl":"https://doi.org/10.1109/THMS.2024.3356018","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-31DOI: 10.1109/THMS.2024.3356022
{"title":"Share Your Preprint Research with the World!","authors":"","doi":"10.1109/THMS.2024.3356022","DOIUrl":"https://doi.org/10.1109/THMS.2024.3356022","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-31DOI: 10.1109/THMS.2024.3356024
{"title":"Present a World of Opportunity","authors":"","doi":"10.1109/THMS.2024.3356024","DOIUrl":"https://doi.org/10.1109/THMS.2024.3356024","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-31DOI: 10.1109/THMS.2024.3356016
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2024.3356016","DOIUrl":"https://doi.org/10.1109/THMS.2024.3356016","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-30DOI: 10.1109/THMS.2023.3348694
Xingcan Chen;Yi Zou;Chenglin Li;Wendong Xiao
Human activity recognition (HAR) is a key technology in the field of human–computer interaction. Unlike systems using sensors or special devices, the WiFi channel state information (CSI)-based HAR systems are noncontact and low cost, but they are limited by high computational complexity and poor cross-domain generalization performance. In order to address the above problems, a reconstructed WiFi CSI tensor and deep learning based lightweight HAR system (Wisor-DL) is proposed, which firstly reconstructs WiFi CSI signals with a sparse signal representation algorithm, and a CSI tensor construction and decomposition algorithm. Then, gated temporal convolutional network with residual connections is designed to enhance and fuse the features of the reconstructed WiFi CSI signals. Finally, dendrite network makes the final decision of activity instead of the traditional dense layer. Experimental results show that Wisor-DL is a lightweight HAR system with high recognition accuracy and satisfactory cross-domain generalization ability.
人类活动识别（HAR）是人机交互领域的一项关键技术。与使用传感器或特殊设备的系统不同，基于 WiFi 信道状态信息（CSI）的人类活动识别系统具有非接触、成本低的特点，但受限于计算复杂度高和跨域泛化性能差。针对上述问题，本文提出了一种基于重构 WiFi CSI 张量和深度学习的轻量级 HAR 系统（Wisor-DL），该系统首先利用稀疏信号表示算法和 CSI 张量构建与分解算法重构 WiFi CSI 信号。然后，设计具有残差连接的门控时序卷积网络，以增强和融合重构的 WiFi CSI 信号的特征。最后，树突网络代替传统的密集层做出活动的最终决定。实验结果表明，Wisor-DL 是一种轻量级 HAR 系统，具有较高的识别准确率和令人满意的跨域泛化能力。
{"title":"A Deep Learning Based Lightweight Human Activity Recognition System Using Reconstructed WiFi CSI","authors":"Xingcan Chen;Yi Zou;Chenglin Li;Wendong Xiao","doi":"10.1109/THMS.2023.3348694","DOIUrl":"https://doi.org/10.1109/THMS.2023.3348694","url":null,"abstract":"Human activity recognition (HAR) is a key technology in the field of human–computer interaction. Unlike systems using sensors or special devices, the WiFi channel state information (CSI)-based HAR systems are noncontact and low cost, but they are limited by high computational complexity and poor cross-domain generalization performance. In order to address the above problems, a reconstructed WiFi CSI tensor and deep learning based lightweight HAR system (Wisor-DL) is proposed, which firstly reconstructs WiFi CSI signals with a sparse signal representation algorithm, and a CSI tensor construction and decomposition algorithm. Then, gated temporal convolutional network with residual connections is designed to enhance and fuse the features of the reconstructed WiFi CSI signals. Finally, dendrite network makes the final decision of activity instead of the traditional dense layer. Experimental results show that Wisor-DL is a lightweight HAR system with high recognition accuracy and satisfactory cross-domain generalization ability.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654773","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}
Pub Date : 2024-01-26DOI: 10.1109/THMS.2024.3351138
Shu-Nung Yao;Chang-Wei Huang
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 x -, y -, and z -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.
{"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}
Pub Date : 2024-01-19DOI: 10.1109/THMS.2024.3349652
Zhewei Zhang;Mingen Liu;Junyu Shen;Yujun Cheng;Shengjin Wang
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.
{"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}
Pub Date : 2024-01-18DOI: 10.1109/THMS.2023.3349030
Yi-Ting Hwang;Ya-Ru Hsu;Bor-Shing Lin
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 $< $ 0.5 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.
能量消耗（EE）通常用于量化体力活动。目前，能量消耗是通过惯性测量装置或深度摄像头收集的数据进行估算，并通过耗氧量数据进行验证。由于该系统的数据收集时间跨度不同，原始数据被分割成每分钟的窗口，并计算每个窗口的汇总统计数据。然而，使用汇总统计来汇总数据可能会受到冗余噪声的影响，或导致有价值的信息丢失。本文介绍了一种使用函数分析的建模方法，以描述所收集的骨骼数据的轨迹，从而有效利用完整的数据。接下来，骨骼数据的拟合值可与整体 EE 数据相匹配，并用于预测整体 EE 以及基于任务的 EE。研究结果表明，对于新陈代谢等效任务预测，拟议方法得出的均方根误差（RMSE）为 0.5 美元，平均绝对误差（MAE）约为 0.3 美元。用于估算基于任务的 EE（包括与站立和行走任务相关的 EE）的模型也显示出较低的 RMSE 值和 MAE 值。因此，在估计深度摄像系统中的 EE 时，建议的建模方法优于汇总统计方法。
{"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}
Pub Date : 2024-01-15DOI: 10.1109/THMS.2023.3347404
Tamon Miyake;Hiromasa Ito;Naomi Okamura;Yo Kobayashi;Masakatsu G. Fujie;Shigeki Sugano
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.
{"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}
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