Burn injuries often result in severe hand complications, including joint contractures and nerve damage, sometimes leading to amputation. Despite early treatment, hypertrophic scarring frequently hampers hand function recovery, and the thick raised scar blocks electromyography (EMG) sensing. A promising solution involves motion-mimicking robotic finger prostheses tailored to individual patient requirements. By utilizing the versatility of motion-capturing technology on a sound finger, a robotic finger prosthesis can mimic the movement of a sound finger simultaneously with less latency than EMG-based sensory mechanisms through hypertrophic scars. This case study evaluated the clinical efficacy of a customized three-dimensional printed robotic finger prosthesis in a 24-year-old man who sustained left second finger loss due to electrical burns. Despite undergoing reconstructive surgery, the patient struggled with manual dexterity. Following the adoption of a personalized robotic finger prosthesis with a finger motion-capturing device, significant improvements in grip strength and daily task performance were observed. This innovative approach has advantages such as customization, reduced latency time for finger movements, and affordability from low-cost manufacturing, suggesting its potential for broader adoption among burn-induced amputees.
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http://dx.doi.org/10.1093/jbcr/irae194 | DOI Listing |
Background: The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) multimodal lifestyle intervention yielded cognitive and other health benefits in older adults at risk of cognitive decline. The two-year multinational randomized controlled LETHE trial evaluates the feasibility of a digitally supported, adapted FINGER intervention among at-risk older adults. Technology is used to complement in-person activities, for the intervention delivery, personalize recommendations, and collect digital biomarkers.
View Article and Find Full Text PDFMicromachines (Basel)
November 2024
Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China.
The proliferation of flexible pressure sensors has generated new demands for high-sensitivity and low-cost sensors. Here, we propose an elegant strategy to address this challenge by taking a ridge-mimicking, gradient-varying, spatially ordered microstructure as the sensing layer, with laser processing and interdigitated electrodes as the upper and lower electrode layers. Simultaneously, the entire structure is encapsulated with polyimide (PI) tape for protection, and the fabrication process is relatively feasible, facilitating easy scaling.
View Article and Find Full Text PDFNatl Sci Rev
January 2025
CAS Key Laboratory of Organic Solids, Beijing National Laboratory for Molecular Sciences (BNLMS), CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
In the face of advancements in microrobotics, intelligent control and precision medicine, artificial muscle actuation systems must meet demands for precise control, high stability, environmental adaptability and high integration miniaturization. Carbon materials, being lightweight, strong and highly conductive and flexible, show great potential for artificial muscles. Inspired by the butterfly's proboscis, we have developed a carbon-based artificial muscle, hydrogen-substituted graphdiyne muscle (HsGDY-M), fabricated efficiently using an emerging hydrogen-substituted graphdiyne (HsGDY) film with an asymmetrical surface structure.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electrical Engineering, California Polytechnic State University, San Luis Obispo, California, USA.
Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inconvenient and painful. To address this issue, photoplethysmography (PPG) presents a promising non-invasive alternative for estimating blood glucose levels. In this study, we propose an innovative 1-second signal segmentation method and evaluate the performance of three advanced deep learning models using a novel dataset to estimate blood glucose levels from PPG signals.
View Article and Find Full Text PDFJ Neural Eng
January 2025
Electrical and Computer Engineering Department, University of New Brunswick, 3 Bailey Dr., Fredericton, New Brunswick, E3B5A3, CANADA.
Objective: While myoelectric control has been commercialized in prosthetics for decades, its adoption for more general human-machine interaction has been slow. Although high accuracies can be achieved across many gestures, current control approaches are prone to false activations in real-world conditions. This is because the same electromyogram (EMG) signals generated during the elicitation of gestures are also naturally activated when performing activities of daily living (ADLs), such as when driving to work or while typing on a keyboard.
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