Background: Robotic systems are becoming increasingly common in upper extremity stroke rehabilitation. Recent studies have already shown that the use of rehabilitation robots can improve recovery. This paper evaluates the effect of different modes of robot-assistances in a complex virtual environment on the subjects' ability to complete the task as well as on various haptic parameters arising from the human-robot interaction.
Methods: The MIMICS multimodal system that includes the haptic robot HapticMaster and a dynamic virtual environment is used. The goal of the task is to catch a ball that rolls down a sloped table and place it in a basket above the table. Our study examines the influence of catching assistance, pick-and-place movement assistance and grasping assistance on the catching efficiency, placing efficiency and on movement-dependent parameters: mean reaching forces, deviation error, mechanical work and correlation between the grasping force and the load force.
Results: The results with groups of subjects (23 subacute hemiparetic subjects, 10 chronic hemiparetic subjects and 23 control subjects) showed that the assistance raises the catching efficiency and pick-and-place efficiency. The pick-and-place movement assistance greatly limits the movements of the subject and results in decreased work toward the basket. The correlation between the load force and the grasping force exists in a certain phase of the movement. The results also showed that the stroke subjects without assistance and the control subjects performed similarly.
Conclusions: The robot-assistances used in the study were found to be a possible way to raise the catching efficiency and efficiency of the pick-and-place movements in subacute and chronic subjects. The observed movement parameters showed that robot-assistances we used for our virtual task should be improved to maximize physical activity.
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http://dx.doi.org/10.1186/1743-0003-7-52 | DOI Listing |
Vaccines (Basel)
November 2024
Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA 02114, USA.
: Human Papillomavirus (HPV) is the primary cause of cervical cancer. Single-dose HPV vaccination can effectively prevent high-risk HPV infection that causes cervical cancer and accelerate progress toward achieving cervical cancer elimination goals. We modelled the potential impact of adopting single-dose HPV vaccination strategies on health and economic outcomes in Kenya, where a two-dose schedule is the current standard.
View Article and Find Full Text PDFJ Funct Morphol Kinesiol
October 2024
Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413305, Taiwan.
Background/objectives: Research on elite weightlifting performance is crucial for understanding the underlying attributes of efficient techniques. This study aimed to analyze the foot characteristics of elite female weightlifters in the 59 kg category during the snatch.
Methods: Publicly available videos from the International Weightlifting Federation World Weightlifting Championships (2018-2021) were analyzed.
Water Res
January 2025
Division of Engineering and Applied Science, Linde-Robinson Laboratory, California Institute of Technology, Pasadena, CA 91125, USA. Electronic address:
J Natl Cancer Inst Monogr
November 2024
Early Detection, Prevention and Infections Branch, International Agency for Research on Cancer, Lyon, France.
Background: In low- and middle-income countries, resource constraints remain a critical factor limiting access to cervical cancer preventive measures. The option of single-dose immunization could help improve access to human papillomavirus vaccination and attain cervical cancer elimination.
Methods: With simulation models adapted to country-specific data and scenarios for single-dose protection derived from International Agency for Research on Cancer India vaccine trial data, we estimated the expected impact of single-dose vaccination in India, Rwanda, and Brazil, three countries with varying profiles of cervical cancer risk and vaccination timelines.
Front Artif Intell
October 2024
Oxford Internet Institute, University of Oxford, Oxford, United Kingdom.
Coverage of ChatGPT-style large language models (LLMs) in the media has focused on their eye-catching achievements, including solving advanced mathematical problems and reaching expert proficiency in medical examinations. But the gradual adoption of LLMs in agriculture, an industry which touches every human life, has received much less public scrutiny. In this short perspective, we examine risks and opportunities related to more widespread adoption of language models in food production systems.
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