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http://dx.doi.org/10.3389/frobt.2024.1421697 | DOI Listing |
Front Hum Neurosci
January 2025
Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
Background: Bimanual motor training is an effective neurological rehabilitation strategy. However, its use has rarely been investigated in patients with paralysis caused by spinal cord injury (SCI). Therefore, we conducted a case study to investigate the effects of robot-assisted task-oriented bimanual training (RBMT) on upper limb function, activities of daily living, and movement-related sensorimotor activity in a patient with SCI.
View Article and Find Full Text PDFFront Neurol
January 2025
Department of Rehabilitation Medicine, Hirosaki University Graduate School of Medicine, Hirosaki, Japan.
Sensors (Basel)
January 2025
Future Robotics Organization, Waseda University, Tokyo 169-8050, Japan.
Wearable sensor technologies are rapidly evolving and expanding their reach into critical wellness and healthcare applications [...
View Article and Find Full Text PDFBrain Sci
January 2025
Behavioral and Robotic Neurorehab Unit, IRCCS Centro Neurolesi "Bonino Pulejo", 98123 Messina, Italy.
It was a great pleasure to be the guest editor for the Special Issue on understanding the use of rehabilitation devices in neurological diseases [...
View Article and Find Full Text PDFFront Plant Sci
January 2025
Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, China.
Three-dimensional (3D) LiDAR is crucial for the autonomous navigation of orchard mobile robots, offering comprehensive and accurate environmental perception. However, the increased richness of information provided by 3D LiDAR also leads to a higher computational burden for point cloud data processing, posing challenges to real-time navigation. To address these issues, this paper proposes a 3D point cloud optimization method based on the octree data structure for autonomous navigation of orchard mobile robots.
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