This paper presents two robot devices for use in the rehabilitation of upper limb movements and reports the quantitative parameters obtained to characterize the rate of improvement, thus allowing a precise monitoring of patient's recovery. A one degree of freedom (DoF) wrist manipulator and a two-DoF elbow-shoulder manipulator were designed using an admittance control strategy; if the patient could not move the handle, the devices completed the motor task. Two groups of chronic post-stroke patients (G1 n = 7, and G2 n = 9) were enrolled in a three week rehabilitation program including standard physical therapy (45 min daily) plus treatment by means of robot devices, respectively, for wrist and elbow-shoulder movements (40 min, twice daily). Both groups were evaluated by means of standard clinical assessment scales and a new robot measured evaluation metrics that included an active movement index quantifying the patient's ability to execute the assigned motor task without robot assistance, the mean velocity, and a movement accuracy index measuring the distance of the executed path from the theoretic one. After treatment, both groups improved their motor deficit and disability. In G1, there was a significant change in the clinical scale values (p < 0.05) and range of motion wrist extension (p < 0.02). G2 showed a significant change in clinical scales (p < 0.01), in strength (p < 0.05) and in the robot measured parameters (p < 0.01). The relationship between robot measured parameters and the clinical assessment scales showed a moderate and significant correlation (r > 0.53 p < 0.03). Our findings suggest that robot-aided neurorehabilitation may improve the motor outcome and disability of chronic post-stroke patients. The new robot measured parameters may provide useful information about the course of treatment and its effectiveness at discharge.
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http://dx.doi.org/10.1109/TNSRE.2005.848352 | DOI Listing |
Polymers (Basel)
January 2025
Department of Mechanical Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
Metamaterials are pushing the limits of traditional materials and are fascinating frontiers in scientific innovation. Mechanical metamaterials (MMs) are a category of metamaterials that display properties and performances that cannot be realized in conventional materials. Exploring the mechanical properties and various aspects of vibration and damping control is becoming a crucial research area.
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January 2025
Department of Biomedical and Robotics Engineering, Incheon National University, Incheon 22012, Republic of Korea.
With the rise of modern healthcare monitoring, heart rate (HR) estimation using remote photoplethysmography (rPPG) has gained attention for its non-contact, continuous tracking capabilities. However, most HR estimation methods rely on stable, fixed sampling intervals, while practical image capture often involves irregular frame rates and missing data, leading to inaccuracies in HR measurements. This study addresses these issues by introducing low-complexity timing correction methods, including linear, cubic, and filter interpolation, to improve HR estimation from rPPG signals under conditions of irregular sampling and data loss.
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January 2025
College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.
This paper aims to address the challenge of precise robotic grasping of molecular sieve drying bags during automated packaging by proposing a six-dimensional (6D) pose estimation method based on an red green blue-depth (RGB-D) camera. The method consists of three components: point cloud pre-segmentation, target extraction, and pose estimation. A minimum bounding box-based pre-segmentation method was designed to minimize the impact of packaging wrinkles and skirt curling.
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January 2025
The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.
Video instance segmentation, a key technology for intelligent sensing in visual perception, plays a key role in automated surveillance, robotics, and smart cities. These scenarios rely on real-time and efficient target-tracking capabilities for accurate perception and intelligent analysis of dynamic environments. However, traditional video instance segmentation methods face complex models, high computational overheads, and slow segmentation speeds in time-series feature extraction, especially in resource-constrained environments.
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January 2025
College of Science and Technology, Ningbo University, Ningbo 315300, China.
Industrial robotic arms are often subject to significant end-effector pose deviations from the target position due to the combined effects of nonlinear deformations such as link flexibility, joint compliance, and end-effector load. To address this issue, a study was conducted on the analysis and compensation of end-position errors in a six-degree-of-freedom robotic arm. The kinematic model of the robotic arm was established using the Denavit-Hartenberg (DH) parameter method, and a rigid-flexible coupled virtual prototype model was developed using ANSYS and ADAMS.
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