IEEE Transactions on Human-Machine Systems最新文献_第10页

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":"54 2","pages":"202-213"},"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":"54 1","pages":"121-130"},"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":"54 1","pages":"79-88"},"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":"54 1","pages":"34-43"},"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

Haptic Shared Control for Dissipating Phantom Traffic Jams 用于消除交通拥堵幻影的触觉共享控制装置

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-01-10 DOI: 10.1109/THMS.2023.3315519

Klaas O. Koerten;David. A. Abbink;Arkady Zgonnikov

{"title":"Haptic Shared Control for Dissipating Phantom Traffic Jams","authors":"Klaas O. Koerten;David. A. Abbink;Arkady Zgonnikov","doi":"10.1109/THMS.2023.3315519","DOIUrl":"https://doi.org/10.1109/THMS.2023.3315519","url":null,"abstract":"Traffic jams occurring on highways cause increased travel time as well as increased fuel consumption and collisions. So-called \u0000<italic>phantom traffic jams</i>\u0000 are traffic jams that do not have a clear cause, such as a merging on-ramp or an accident. Phantom traffic jams make up 50% of all traffic jams and result from instabilities in the traffic flow that are caused by human driving behavior. Automating the longitudinal vehicle motion of only 5% of all cars in the flow can dissipate phantom traffic jams. However, driving automation introduces safety issues when human drivers need to take over the control from the automation. We investigated whether phantom traffic jams can be dissolved using haptic shared control. This keeps humans in the loop and thus bypasses the problem of humans' limited capacity to take over control, while benefiting from most advantages of automation. In an experiment with 24 participants in a driving simulator, we tested the effect of haptic shared control on the dynamics of traffic flow and compared it with manual control and full automation. We also investigated the effect of two control types on participants' behavior during simulated silent automation failures. Results show that haptic shared control can help dissipating phantom traffic jams better than fully manual control but worse than full automation. We also found that haptic shared control reduces the occurrence of unsafe situations caused by silent automation failures compared to full automation. Our results suggest that haptic shared control can dissipate phantom traffic jams while preventing safety risks associated with full automation.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 1","pages":"11-20"},"PeriodicalIF":3.6,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654819","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

Speech Enhancement—A Review of Modern Methods 语音增强--现代方法回顾

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-01-05 DOI: 10.1109/THMS.2023.3339663

Douglas O'Shaughnessy

引用次数: 0

What Challenges Does the Full-Touch HMI Mode Bring to Driver's Lateral Control Ability? A Comparative Study Based on Real Roads 全触控人机界面模式会给驾驶员的横向控制能力带来哪些挑战？基于真实道路的比较研究

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-01-04 DOI: 10.1109/THMS.2023.3342045

Xia Zhao;Zhao Li;Rui Fu;Chang Wang;Yingshi Guo

{"title":"What Challenges Does the Full-Touch HMI Mode Bring to Driver's Lateral Control Ability? A Comparative Study Based on Real Roads","authors":"Xia Zhao;Zhao Li;Rui Fu;Chang Wang;Yingshi Guo","doi":"10.1109/THMS.2023.3342045","DOIUrl":"https://doi.org/10.1109/THMS.2023.3342045","url":null,"abstract":"In recent years, the full-touch human–machine interface (HMI) mode has been widely used in vehicles built by Tesla. This interaction mode replaces the conventional physical interaction modality with a screen, and it has a good sense of technological experience. However, it is unclear whether this mode will make the driver's lateral control more challenging than the conventional mode (CM). To investigate this issue, two most common secondary tasks were designed: dialing and navigation entry tasks and real-world road experiments were conducted using two instrumented vehicles. The vehicle operating parameters and the driver manual data were collected in different modes, respectively. Interestingly, the opposite results were found regarding the effect of the full-touch mode (FTM) on the driver's lateral control ability in different secondary tasks. Compared with the CM, the lateral control ability decreased less during the dialing task relative to the baseline driving in the FTM, while the lateral control ability decreased more in the FTM during the navigation entry task. In addition, drivers’ lateral control decreased further as task difficulty and driving speed increased regardless of mode. This study provides a theoretical basis for the development of laws and regulations regarding full-touch HMI mode.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 1","pages":"21-33"},"PeriodicalIF":3.6,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654665","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

2023 Index IEEE Transactions on Human-Machine Systems Vol. 53 2023 索引《电气和电子工程师学会人机系统学报》第 53 卷

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2023-12-29 DOI: 10.1109/THMS.2023.3344185

引用次数: 0

Assessment of Upper-Body Movement Quality in the Cartesian-Space is Feasible in the Harmony Exoskeleton 在和谐外骨骼中评估笛卡尔空间中的上半身运动质量是可行的

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2023-12-14 DOI: 10.1109/THMS.2023.3305391

Ana C. De Oliveira;Ashish D. Deshpande

{"title":"Assessment of Upper-Body Movement Quality in the Cartesian-Space is Feasible in the Harmony Exoskeleton","authors":"Ana C. De Oliveira;Ashish D. Deshpande","doi":"10.1109/THMS.2023.3305391","DOIUrl":"https://doi.org/10.1109/THMS.2023.3305391","url":null,"abstract":"To determine the most effective interventions for poststroke patients, it is imperative to monitor the recovery process. Robotic exoskeletons' built-in sensing capabilities enable accurate kinematic measurement with no additional setup time. Although position sensors used in exoskeletons are accurate, a mismatch between the robot's and the human's joints can lead to inaccurate measurements. In addition, the robot's residual dynamics can interfere with human's natural movements and the kinematic metrics assessed in the robot would not be representative of the human's movement in free-motion. So far, the accuracy of robotic exoskeletons in assessing upper-body kinematics has not been verified. The bilateral upper-body Harmony exoskeleton has features favorable to minimize joint misalignments and the robot's residual dynamics. In this study, we examined Harmony's ability to accurately assess Cartesian-space kinematic parameters associated with the wearer's movement quality. We analyzed data collected from eight healthy participants that executed point-to-point movements with and without the presence of the robot and at fast and slow speeds. Ground truth was acquired with an optical motion capture, and we extracted the kinematic parameters from the measured data. The results suggest that Harmony can accurately measure kinematic parameters associated with movement quality, and these parameters could appropriately reflect wearer's natural movements at a slow speed. Therefore, Harmony could aid the evaluation of the effectiveness of different interventions, which is more sensitive and efficient than currently adopted clinical outcomes. This allows for individualization of a treatment plan and a detailed follow-up.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"53 6","pages":"985-995"},"PeriodicalIF":3.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633789","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