ArticleFrontiers in computational neuroscience2025
Intelligent rehabilitation in an aging population: empowering human-machine interaction for hand function rehabilitation through 3D deep learning and point cloud.
Article in Frontiers in computational neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
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- Application of AI and deep learning technology for IPE education under dual track cultivation model.Scientific reports · 2025Article
- Radar-Based Gesture Recognition Using Adaptive Top-K Selection and Multi-Stream CNNs.Sensors (Basel, Switzerland) · 2025Article
- DeepPHSI: attention-driven CNN-LSTM fusion for hyperspectral origin traceability acrossRSC advances · 2025Article
- Durian peel-seed biochar for efficient methylene blue removal from water: synthesis, characterization, and adsorption performance.RSC advances · 2025Article
- Three-Dimensional Environment Mapping with a Rotary-Driven Lidar in Real Time.Sensors (Basel, Switzerland) · 2025Article
- A Bio-Inspired Adaptive Probability IVYPSO Algorithm with Adaptive Strategy for Backpropagation Neural Network Optimization in Predicting High-Performance Concrete Strength.Biomimetics (Basel, Switzerland) · 2025Article
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9 authors.
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Abstract
Human-machine interaction and computational neuroscience have brought unprecedented application prospects to the field of medical rehabilitation, especially for the elderly population, where the decline and recovery of hand function have become a significant concern. Responding to the special needs under the context of normalized epidemic prevention and control and the aging trend of the population, this research proposes a method based on a 3D deep learning model to process laser sensor point cloud data, aiming to achieve non-contact gesture surface feature analysis for application in the field of intelligent rehabilitation of human-machine interaction hand functions. By integrating key technologies such as the collection of hand surface point clouds, local feature extraction, and abstraction and enhancement of dimensional information, this research has constructed an accurate gesture surface feature analysis system. In terms of experimental results, this research validated the superior performance of the proposed model in recognizing hand surface point clouds, with an average accuracy of 88.72%. The research findings are of significant importance for promoting the development of non-contact intelligent rehabilitation technology for hand functions and enhancing the safe and comfortable interaction methods for the elderly and rehabilitation patients.
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