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Working Conditions of Industrial Robot Operators–An Overview of Technology Dissemination, Job Characteristics, and Health Indicators in Modern Production Workplaces IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-03-06 Matthias Hartwig, Patricia Rosen, Sascha Wischniewski
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Human-Like Trajectory Planning Based on Postural Synergistic Kernelized Movement Primitives for Robot-Assisted Rehabilitation IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-02-21 Zemin Liu, Qingsong Ai, Haojie Liu, Wei Meng, Quan Liu
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Extracting Human Levels of Trust in Human–Swarm Interaction Using EEG Signals IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-02-07 Jesus A. Orozco, Panagiotis Artemiadis
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A Deep Learning Based Lightweight Human Activity Recognition System Using Reconstructed WiFi CSI IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-01-30 Xingcan Chen, Yi Zou, Chenglin Li, Wendong Xiao
Human activity recognition (HAR) is a key technology in the field of human窶田omputer 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
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Design and Investigation of a Suspended Backpack With Wide-Range Variable Stiffness Suspension for Reducing Energetic Cost IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-02-02 Xin Lin, Shucong Yin, Hao Du, Yuquan Leng, Chenglong Fu
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Behavioral, Peripheral, and Central Neural Correlates of Augmented Reality Guidance of Manual Tasks IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-02-01 Alejandro L. Callara, Gianluca Rho, Sara Condino, Vincenzo Ferrari, Enzo Pasquale Scilingo, Alberto Greco
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Share Your Preprint Research with the World! IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-01-31
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IEEE Systems, Man, and Cybernetics Society Information IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-01-31
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IEEE Systems, Man, and Cybernetics Society Information IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-01-31
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IEEE Transactions on Human-Machine Systems Information for Authors IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-01-31
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Head-Pose Estimation Based on Lateral Canthus Localizations in 2-D Images IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-01-26 Shu-Nung Yao, Chang-Wei Huang
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Lightweight Whole-Body Human Pose Estimation With Two-Stage Refinement Training Strategy IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-01-19 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
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Using $B$-Spline Model on Depth Camera Data to Predict Physical Activity Energy Expenditure of Different Levels of Human Exercise IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-01-18 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
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EMG-Based Detection of Minimum Effective Load With Robotic-Resistance Leg Extensor Training IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-01-15 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
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Haptic Shared Control for Dissipating Phantom Traffic Jams IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-01-10 Klaas O. Koerten, David. A. Abbink, Arkady Zgonnikov
Traffic jams occurring on highways cause increased travel time as well as increased fuel consumption and collisions. So-called phantom traffic jams 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
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Speech Enhancement—A Review of Modern Methods IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-01-05 Douglas O'Shaughnessy
A review of techniques to improve distorted speech is presented, noting the strengths and weaknesses of common methods. Speech signals are discussed from the point of view of which features should be preserved to retain both naturalness and intelligibility. Enhancement methods range from classical spectral subtraction and Wiener filtering to recent deep neural network approaches. The difficulty of
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What Challenges Does the Full-Touch HMI Mode Bring to Driver's Lateral Control Ability? A Comparative Study Based on Real Roads IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2024-01-04 Xia Zhao, Zhao Li, Rui Fu, Chang Wang, Yingshi Guo
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
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Assessment of Upper-Body Movement Quality in the Cartesian-Space is Feasible in the Harmony Exoskeleton IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-12-14 Ana C. De Oliveira, Ashish D. Deshpande
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
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A Multidataset Characterization of Window-Based Hyperparameters for Deep CNN-Driven sEMG Pattern Recognition IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-12-14 Frank Kulwa, Haoshi Zhang, Oluwarotimi Williams Samuel, Mojisola Grace Asogbon, Erik Scheme, Rami Khushaba, Alistair A. McEwan, Guanglin Li
The control performance of myoelectric prostheses would not only depend on the feature extraction and classification algorithms but also on interactions of dynamic window-based hyperparameters (WBHP) used to construct input signals. However, the relationship between these hyperparameters and how they influence the performance of the convolutional neural networks (CNNs) during motor intent decoding
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A Novel Measure of Human Safety Perception in Response to Flight Characteristics of Collocated UAVs in Virtual Reality IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-12-11 Christopher Widdowson, Hyung-Jin Yoon, Naira Hovakimyan, Ranxiao Frances Wang
This article examines how people respond to the presence of a flying robot under various operating conditions using traditional human physiological measures and a novel head movement measurement. A central issue to the integration of flying robotic systems into human-populated environments is how to improve the level of comfort and safety for people around them. Traditional motion control algorithms
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Modeling Awareness Requirements in Groupware: From Cards to Diagrams IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-12-07 Crescencio Bravo, Rafael Duque, Ana I. Molina, Jesús Gallardo
Up to now, groupware has enjoyed a certain stability in terms of the users’ technical requirements, being the awareness dimension one of its key services to provide usability and improve collaboration. Nonetheless, currently, groupware technologies are being stressed: on the one hand, the pandemic of COVID-19 has greatly driven the massive use of groupware tools to overcome physical distancing; on
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Situated Interpretation and Data: Explainability to Convey Machine Misalignment IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-12-07 Dane Anthony Morey, Michael F. Rayo
Explainable AI must simultaneously help people understand the world, the AI, and when the AI is misaligned to the world. We propose situated interpretation and data (SID) as a design technique to satisfy these requirements. We trained two machine learning algorithms, one transparent and one opaque, to predict future patient events that would require an emergency response team (ERT) mobilization. An
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EEG-Based Familiar and Unfamiliar Face Classification Using Filter-Bank Differential Entropy Features IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-12-04 Guoyang Liu, Yiming Wen, Janet H. Hsiao, Di Zhang, Lan Tian, Weidong Zhou
The face recognition of familiar and unfamiliar people is an essential part of our daily lives. However, its neural mechanism and relevant electroencephalography (EEG) features are still unclear. In this study, a new EEG-based familiar and unfamiliar faces classification method is proposed. We record the multichannel EEG with three different face-recall paradigms, and these EEG signals are temporally
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Classifying Human Manual Control Behavior Using LSTM Recurrent Neural Networks IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-11-29 Rogier Versteeg, Daan M. Pool, Max Mulder
This article discusses a long short-term memory (LSTM) recurrent neural network that uses raw time-domain data obtained in compensatory tracking tasks as input features for classifying (the adaptation of) human manual control with single- and double-integrator controlled element dynamics. Data from two different experiments were used to train and validate the LSTM classifier, including investigating
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Single-Belt Versus Split-Belt: Intelligent Treadmill Control via Microphase Gait Capture for Poststroke Rehabilitation IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-11-21 Shengting Cao, Mansoo Ko, Chih-Ying Li, David Brown, Xuefeng Wang, Fei Hu, Yu Gan
Stroke is the leading long-term disability and causes a significant financial burden associated with rehabilitation. In poststroke rehabilitation, individuals with hemiparesis have a specialized demand for coordinated movement between the paretic and the nonparetic legs. The split-belt treadmill can effectively facilitate the paretic leg by slowing down the belt speed for that leg while the patient
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Human–Robot Interaction Video Sequencing Task (HRIVST) for Robot's Behavior Legibility IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-11-14 Silvia Rossi, Alessia Coppola, Mariachiara Gaita, Alessandra Rossi
People's acceptance and trust in robots are a direct consequence of people's ability to infer and predict the robot's behavior. However, there is no clear consensus on how the legibility of a robot's behavior and explanations should be assessed. In this work, the construct of the Theory of Mind (i.e., the ability to attribute mental states to others) is taken into account and a computerized version
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Oropharynx Visual Detection by Using a Multi-Attention Single-Shot Multibox Detector for Human–Robot Collaborative Oropharynx Sampling IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-11-01 Qing Gao, Yongquan Chen, Zhaojie Ju
The pandemic of COVID-19 has increased the demand for the oropharynx sampling robots. For an automatic oropharynx sampling, detection and localization of the oropharynx objects are essential. First, in response to the small-object and real-time needs of visual oropharynx detection, a lightweight multi-attention single-shot multibox detector (MASSD) method is designed. This method can effectively improve
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Mouth Cavity Visual Analysis Based on Deep Learning for Oropharyngeal Swab Robot Sampling IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-11-01 Qing Gao, Zhaojie Ju, Yongquan Chen, Tianwei Zhang, Yuquan Leng
The visual analysis of the mouth cavity plays a significant role in the pathogen specimen sampling and disease diagnosis of the mouth cavity. Aiming at performance defects of general detectors based on deep learning in detecting mouth cavity components, this article proposes a mouth cavity analysis network (MCNet), which is an instance segmentation method with spatial features, and a mouth cavity dataset
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PC-GNN: Pearson Correlation-Based Graph Neural Network for Recognition of Human Lower Limb Activity Using sEMG Signal IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-10-12 Ankit Vijayvargiya, Rajesh Kumar, Parul Sharma
Artificial intelligence has a plethora of applications in the realm of biomedical sciences, such as pattern recognition, diagnosis of disease, human–machine interaction, medical image processing, robotic limbs, or exoskeletons. Robotic limbs, or exoskeletons, are widely employed to assist with lower limb movement. To increase the exoskeleton's flexibility in the lower extremities, it is critical to
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Evaluating the Impact of Time-to-Collision Constraint and Head Gaze on Usability for Robot Navigation in a Corridor IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-10-12 Guilhem Buisan, Nathan Compan, Loïc Caroux, Aurélie Clodic, Ophélie Carreras, Camille Vrignaud, Rachid Alami
Navigation of robots among humans is still an open problem, especially in confined locations (e.g. narrow corridors, doors). This article aims at finding how an anthropomorphic robot, like a PR2 robot with a height of 1.33 m, should behave when crossing a human in a narrow corridor in order to increase its usability. Two experiments studied how a combination of robot head behavior and navigation strategy
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A State-Space Control Approach for Tracking Isometric Grip Force During BMI Enabled Neuromuscular Stimulation IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-10-12 Nikunj A. Bhagat, Gerard E. Francisco, Jose L. Contreras-Vidal
Sixty percent of elderly hand movements involve grasping, which is unarguably why grasp restoration is a major component of upper-limb rehabilitation therapy. Neuromuscular electrical stimulation is effective in assisting grasping, but challenges around patient engagement and control, as well as poor movement regulation due to fatigue and muscle nonlinearity continue to hinder its adoption for clinical
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NLP-Crowdsourcing Hybrid Framework for Inter-Researcher Similarity Detection IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-10-12 António Correia, Diogo Guimarães, Hugo Paredes, Benjamim Fonseca, Dennis Paulino, Luís Trigo, Pavel Brazdil, Daniel Schneider, Andrea Grover, Shoaib Jameel
Visualizing and examining the intellectual landscape and evolution of scientific communities to support collaboration is crucial for multiple research purposes. In some cases, measuring similarities and matching patterns between research publication document sets can help to identify people with similar interests for building research collaboration networks and university–industry linkages. The premise
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An Integrated in Situ Image Acquisition and Annotation Scheme for Instance Segmentation Models in Open Scenes With a Human–Robot Interaction Approach IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-09-22 Liang Gong, Zhiyu Yang, Yihang Yao, Binhao Chen, Wenjie Wang, Xiaofeng Du, Yidong He, Chengliang Liu
A large amount of data acquisition and annotation work is required to train a supervised machine learning model for open scenes. However, traditional manual approaches are inefficient. Here, a method is proposed for on-site image acquisition and semiautomatic annotation based on eye-tracking. This method uses the recognition capabilities and computational advantages of humans and machines to improve
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Driver Response to Take-Over Requests in Real Traffic IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-09-22 Linda Pipkorn, Emma Tivesten, Carol Flannagan, Marco Dozza
Existing research on control-transitions from automated driving (AD) to manual driving mainly stems from studies in virtual settings. There is a need for studies conducted in real settings to better understand the impacts of increasing vehicle automation on traffic safety. This study aims specifically to understand how drivers respond to take-over requests (TORs) in real traffic by investigating the
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Fast, Accurate, But Sometimes Too-Compelling Support: The Impact of Imperfectly Automated Cues in an Augmented-Reality Head-Mounted Display on Visual Search Performance IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-09-20 Amelia C. Warden, Christopher D. Wickens, Daniel Rehberg, Francisco R. Ortega, Benjamin A. Clegg
While the visual search for targets in a complex scene might benefit from using augmented-reality (AR) head-mounted display (HMD) technologies by helping to efficiently direct human attention, imperfectly reliable automation support could manifest in occasional errors. The current study examined the effectiveness of different HMD cues that might support visual search performance and their respective
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MobiEye: An Efficient Shopping-Assistance System for the Visually Impaired With Mobile Phone Sensing IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-09-19 Ziqi Wang, Bin Guo, Qianru Wang, Daqing Zhang, Zhiwen Yu
The lack of rich visual information affects the shopping experience of the visually impaired (VI), including identifying and selecting commodities. Recent studies on VI assistance have focused on commodity identification but neglected to provide fine-grained and intuitive pick-up guidance, which is not user-friendly enough. Therefore, we propose a user-driven shopping assistance system to improve the
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Human–Machine Interactive Learning Method Based on Active Learning for Smart Workshop Dynamic Scheduling IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-09-15 Dongyuan Wang, Liuen Guan, Juan Liu, Chen Ding, Fei Qiao
In the field of dynamic scheduling, workers and scheduling models (SMs) play a crucial role in decision-making. Workers are able to help SM training by sample labeling, thereby enhancing the decision-making ability of SMs. However, existing supervised learning methods require a large number of labeled samples to train SMs, which limits the learning efficiency between workers and SMs. In this article
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A Novel Interval Type-2 Fuzzy Classifier Based on Explainable Neural Network for Surface Electromyogram Gesture Recognition IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-09-12 Shuai Lv, Zhijun Li, Jin Huang, Peng Shi
The existing hand gesture classification research based on surface electromyogram (sEMG) faces the challenges of low classification accuracy, weak real-time ability, weak robustness, few categories, and lack of explainability. In this article, we investigate how to classify sEMG signals for grasp recognition and human–robot interaction to consider these issues. A novel interval type-2 (IT2) fuzzy classifier
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Augmented-Reality-Based Human Memory Enhancement Using Artificial Intelligence IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-09-11 Zhanat Makhataeva, Tolegen Akhmetov, Huseyin Atakan Varol
This work presents a human memory augmentation system that uses augmented reality (AR), computer vision (CV), and artificial intelligence to replace the internal mental representation of objects in the environment with an external augmented representation. The system consists of two components: 1) an AR headset; and 2) a computing station. The AR headset runs an application that senses the indoor environment
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Self-Supervised Human Activity Recognition With Localized Time-Frequency Contrastive Representation Learning IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-08-31 Setareh Rahimi Taghanaki, Michael Rainbow, Ali Etemad
In this article, we propose a self-supervised learning solution for human activity recognition with smartphone accelerometer data. We aim to develop a model that learns strong representations from accelerometer signals, in order to perform robust human activity classification, while reducing the model's reliance on class labels. Specifically, we intend to enable cross-dataset transfer learning such
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Multivariate Analysis of Gaze Behavior and Task Performance Within Interface Design Evaluation IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-08-31 James Blundell, Charlotte Collins, Rod Sears, Tassos Plioutsias, John Huddlestone, Don Harris, James Harrison, Anthony Kershaw, Paul Harrison, Phil Lamb
Eye tracking technologies have frequently been used in sport research to understand the interrelations between gaze behavior and performance, using a paradigm known as vision-for-action. This methodology has not been robustly applied within the field of interface design. The present work demonstrates the benefit of employing a vision-for-action paradigm for interface evaluation. This is demonstrated
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Mapping Intrinsic and Extrinsic Muscle Myoelectric Activity During Natural Dynamic Movements Into Finger and Wrist Kinematics Using Deep Learning Prediction Models IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-08-23 Marcus Panchal, Simone Tanzarella, Moon Ki Jung, Dario Farina
Objective: We investigate the use of high-density surface electromyographic (HDsEMG) recordings of intrinsic hand muscles, along with those from extrinsic muscles, on finger and wrist kinematic prediction performance. We incorporate these HDsEMG signals using a framework based on a custom hybrid convolutional-recurrent deep learning model. Methods: Five healthy subjects performed a wide variety of
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Gamification of Driver Distraction Feedback: A Simulator Study With Younger Drivers IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-08-18 Huei-Yen Winnie Chen, Jeanne Y. Xie, Birsen Donmez
Providing personalized behavioral information as feedback to drivers can lead to safer practices. However, feedback efficacy is likely moderated by the driver's level of motivation towards behavioral change. Gamification of feedback, which is the incorporation of game design elements intended to motivate drivers toward safe behaviors, could potentially reduce unsafe behaviors in the long term. This
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Design of Smart Clothing With Automatic Cardiovascular Diseases Detection IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-08-08 Wei-Ting Chang, Bor-Shing Lin, Yung-Lin Chen, Heng-Yin Chen, Chengyu Liu, Yi-Ting Hwang, Bor-Shyh Lin
Electrocardiogram (ECG) is one of the most important information for cardiovascular diseases (CVDs) diagnosis. In recent year, several dry electrode-based smart clothes have been widely developed to improve the skin allergic reaction and gel-drying issue from conventional Ag/AgCl electrode under long-term measurement. However, most of these dry electrodes still have to contact with skin and may encounter
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A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-08-02 Zhiguang Zhou, Fengling Zheng, Jin Wen, Yuanyuan Chen, Xinyu Li, Yuhua Liu, Yigang Wang, Wei Chen
Numerous sampling strategies have been proposed to reduce the visual clutter of large-scale geographical point data visualization, which focus on the preservation of original data features, such as randomness, spatial distribution, and associated relationship. However, user preferences and demands are not taken into account in the course of sampling, which will lead to the sampled results deviating
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Stiffness-Switchable Hydrostatic Transmission Toward Safe Physical Human–Robot Interaction IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-07-25 Sungbin Park, Kyungseo Park, Wonseok Shin, Jung Kim
A lightweight and compliant manipulator design has been considered crucial in safe physical human–robot interaction. Remote actuation relocating the massive parts to the robot base and transmitting power to the distal joint minimizes the actuator inertia and provides series elasticity to the actuator. Rolling diaphragm hydrostatic transmission (RDHT), one of the remote actuation, has recently been
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Detection of Foot Motions for Interaction With Exergames Using Shoe-Mounted Inertial Sensors IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-07-20 Vânia Guimarães, Inês Sousa, Miguel Velhote Correia
Inertial sensors are widely used to measure human movement. Although inertial sensors have been successfully applied to exergaming in the past, the problem of detecting foot motions to interact with stepping exergames is still largely understudied. In this work, we developed a new method to detect and classify step directions relying on inertial sensor data captured by two shoe-mounted inertial sensors
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Performance and Usability Evaluation Scheme for Mobile Manipulator Teleoperation IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-07-13 Yuhui Wan, Jingcheng Sun, Christopher Peers, Joseph Humphreys, Dimitrios Kanoulas, Chengxu Zhou
This article presents a standardized human–robot teleoperation interface (HRTI) evaluation scheme for mobile manipulators. Teleoperation remains the predominant control type for mobile manipulators in open environments, particularly for quadruped manipulators. However, mobile manipulators, especially quadruped manipulators, are relatively novel systems to be implemented in the industry compared to
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A Layered sEMG–FMG Hybrid Sensor for Hand Motion Recognition From Forearm Muscle Activities IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-07-10 Peiji Chen, Ziye Li, Shunta Togo, Hiroshi Yokoi, Yinlai Jiang
The activities of muscles in the forearm have been widely investigated to develop human interfaces involving hand motions, especially in the fields of prosthetic hands and teleoperation. Although surface electromyography (sEMG) is considered as an effective biological signal from which hand motions can be recognized, the availability and quality of sEMG data can limit the usability and intuitiveness
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Choice, Uncertainty, and Decision Superiority: Is Less AI-Enabled Decision Support More? IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-07-10 Paul Ward
Providing decision makers with more information is often expected to result in more informed and superior decisions. This is especially true when leveraging artificial intelligence (AI) to explore and find complex patterns in vast amounts of data. Although AI can enable an “information advantage,” truly intelligent systems should buffer scarce human cognitive resources from information overload and
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Modeling Team Interaction and Decision-Making in Agile Human–Machine Teams: Quantum and Dynamical Systems Perspective IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-06-23 Mustafa Demir, Mustafa Canan, Myke C. Cohen
In this study, we define team agility as a function of exploration and exploitation of team coordination. Based on these two coordination concepts, we examined interactive decision-making in a dynamic task environment by applying: first, the principles of quantum cognition for the decision-making processes at the confluence of teamwork and taskwork (to discern the effects of ontic uncertainty for each
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Asynchronous Remote Usability Tests Using Web-Based Tools Versus Laboratory Usability Tests: An Experimental Study IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-06-23 Giuseppe Desolda, Rosa Lanzilotti, Danilo Caivano, Maria Francesca Costabile, Paolo Buono
Remote usability testing is performed by evaluators who are in different physical locations from the participants (synchronous remote testing) and possibly operating at different times (asynchronous remote testing). The tools developed in recent years to support remote tests exploit web technology based on HTML5 and JavaScript ES6 and thus enable previously unexplored scenarios. However, studies providing
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Enhancing Screen Reader Intelligibility in Noisy Environments IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-06-23 Dragan Ahmetovic, Gabriele Galimberti, Federico Avanzini, Cristian Bernareggi, Luca Andrea Ludovico, Giorgio Presti, Gianluca Vasco, Sergio Mascetti
People with blindness or severe low vision access mobile devices using screen readers. However, noisy environments can impair screen reader intelligibility. During mobility, this could disorient or even endanger the user. To address this issue, we propose three screen reader speech compensation techniques based on environmental noise: speech rate slowing, adaptive volume increase, and adaptive equalization
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Human Performance in Highly Automated, Cyber Vulnerable Unmanned Aerial Systems: Effects of Operators’ Background and Task Load IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-06-20 Ike R. Stutts, Mark C. Schall, Jennifer Matthews, Sean Gallagher, Grayson H. Phillips, David Umphress
This article evaluated the effects of operator background and task load on operator ability to detect and respond to cyber events while controlling multiple unmanned aerial systems (UAS). Cyber physical systems (CPS) are susceptible to cyber threats and associated events via connections critical to their operations. The effects of cyber intrusions on UAS operator performance as a function of background
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Inertial Measurement Units and Partial Least Square Regression to Predict Perceived Exertion During Repetitive Fatiguing Piano Tasks IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-06-16 Etienne Goubault, Felipe Verdugo, François Bailly, Mickaël Begon, Fabien Dal Maso
Aim. Predict the rate of perceived exertion (RPE) of pianists using inertial measurement units (IMUs)-based kinematic descriptors. Method. Fifty expert pianists played Digital (right-hand 16-tone sequence) and Chord (right-hand chord sequence) excerpts in a continuous loop for 12 min or until exhaustion. Partial least square regression was used to predict RPE with IMUs-based kinematic descriptors.
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Topological Nonlinear Analysis of Dynamical Systems in Wearable Sensor-Based Human Physical Activity Inference IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-06-07 Yan Yan, Yi-Chun Huang, Jinjin Zhao, Yu-Shi Liu, Liang Ma, Jing Yang, Xu-Dong Yan, Jing Xiong, Lei Wang
This work presents a topological nonlinear analysis approach for dynamical system measurements, frequently appearing in sensor-based inference tasks in human physical activity analysis. Traditional approaches to dynamical modeling included linear and nonlinear methods with specific representational abilities and some drawbacks. A novel approach we investigate is using topological descriptors of the
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Multi-Brain Coding Expands the Instruction Set in SSVEP-Based Brain-Computer Interfaces IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-06-01 Xingxing Chu, Yang Yu, Kaixuan Liu, Zeqi Ye, Dewen Hu, Ling-Li Zeng
Previous studies have made great efforts to expand the instruction set in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces. However, most systems are limited to single persons and expand the instruction set by increasing the flicker stimulation frequency range or via multiple frequencies sequential coding or joint frequency/phase coding. In this article, we propose a multibrain
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Emotion Recognition Through Combining EEG and EOG Over Relevant Channels With Optimal Windowing IEEE Trans. Hum. Mach. Syst. (IF 3.6) Pub Date : 2023-05-26 Huili Cai, Xiaofeng Liu, Rongrong Ni, Siyang Song, Angelo Cangelosi
For dimensional emotion recognition, electroencephalography (EEG) signals and electrooculogram (EOG) signals are often combined to improve the performance of classifiers, as each of them provides complementary features to the other. In this article, we combine the EEG signal on the relevant channels with the EOG signal to boost the recognition accuracy. We first explore the mutual information (MI)