With the paradigm shift from hospital-centric healthcare to home-centric healthcare in Healthcare 4.0, healthcare robotics has become one of the fastest growing fields of robotics. The combination of robot capabilities with human intelligence, for example, telerobotics for home care, is gradually showing promising potentials. In this paper, the Home-TeleBot system, a generalized IoT-enabled telerobotic architecture designed to support home-centric healthcare system, is proposed. In particular, the implementation of it is realized by integrating human-motion-capture subsystem with robot-control subsystem. The dual-arm cooperative robot, YuMi, imitates human motion captured by a set of wearable inertial motion capture devices to complete tasks. The proposed approach using workspace mapping and path planning of robot manipulators, facilitates telerobot to execute tasks in a natural and human-like way. Based on the constant of proportionality calculated by comparing the human original workspace with the robot original workspace, the workspace mapping is achieved by making assumptions of the distance between end-effectors (human hands, robot's grippers) and shoulders. Additionally, robot manipulators' path is planned by setting virtual obstacles to constrain robot motion, which aims to improve the performance of robot's human-like motion. As a specific example of application, we apply the proposed architecture to a fetching task based on dual-arm motion capture and mapping for telerobotics in home care.
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http://dx.doi.org/10.1109/JBHI.2019.2953885 | DOI Listing |
PeerJ
January 2025
Department of Oceanography and Coastal Sciences, Louisiana State University and Agricultural and Mechanical College, Baton Rouge, LA, United States of America.
As a key determinant of how efficiently lionfish ( sp.) locate and capture prey, swimming speed plays a crucial role in shaping the predator-prey interactions and broader ecological dynamics within the invaded ecosystems. Swimming speed on a small temporal and spatial scale is difficult to measure because of the need for precise measurements of both distance and duration of the behavior.
View Article and Find Full Text PDFClin Biomech (Bristol)
January 2025
Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, F-59313 Valenciennes, France.
Background: Multiple sclerosis induces locomotor impairments. The objective was to characterize the effects of Multiple Sclerosis on whole-body angular momentum control during gait initiation.
Methods: Fifteen patients with Multiple Sclerosis with Expanded Disability status scale of 2.
Optom Vis Sci
January 2025
Indiana University School of Optometry, Bloomington, Indiana.
Purpose: This study investigated how obstacle contrast altered gait behavior of healthy younger and older adults.
Methods: Twenty normally sighted adults, 11 older (mean [standard deviation] age, 68.1 [5.
Biomed Signal Process Control
August 2024
CNRS-University of Montpellier LIRMM, UMR5506, Interactive Digital Human, Montpellier, France.
Correlation coefficients play a pivotal role in quantifying linear relationships between random variables. Yet, their application to time series data is very challenging due to temporal dependencies. This paper introduces a novel approach to estimate the statistical significance of correlation coefficients in time series data, addressing the limitations of traditional methods based on the concept of effective degrees of freedom (or effective sample size, ESS).
View Article and Find Full Text PDFGait Posture
January 2025
Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE 68182, USA; Department of Surgery and Research Service, Nebraska-Western Iowa Veterans Affairs Medical Center, Omaha, NE 68105, USA. Electronic address:
Background: This study leverages Artificial Neural Networks (ANNs) to predict lower limb joint moments and electromyography (EMG) signals from Ground Reaction Forces (GRF), providing a novel perspective on human gait analysis. This approach aims to enhance the accessibility and affordability of biomechanical assessments using GRF data, thus eliminating the need for costly motion capture systems.
Research Question: Can ANNs use GRF data to accurately predict joint moments in the lower limbs and EMG signals?
Methods: We employed ANNs to analyze GRF data and to use them to predict joint moments (363-trials; 4-datasets) and EMG signals (63-trials; 2-datasets).
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