Grasp synergies lead to the identification of underlying patterns to develop control strategies for five-fingered prosthetic hands or exoskeletons. Data gloves play a crucial role in the study of human grasping and co...
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Grasp synergies lead to the identification of underlying patterns to develop control strategies for five-fingered prosthetic hands or exoskeletons. Data gloves play a crucial role in the study of human grasping and could provide insights into grasp synergies. This article presents the design and implementation of a data glove that has been fabricated using 3-D-printing technology and enhanced with instrumentation. The glove utilizes flexible sensors for the fingers and force sensors integrated into the glove at the fingertips to accurately capture grasp postures and forces. Understanding the kinematics and dynamics of human grasp including reach-to-grasp is undertaken. A comprehensive study involving ten healthy subjects was conducted. Grasp synergy analysis is carried out to identify underlying patterns for grasping. Correlation analysis showed a strong synergy, especially between index and middle fingers with a 0.95 correlation coefficient. Principal component analysis (PCA) facilitated dimensionality reduction, revealing that three principal components (PCs) capture over 97% of the variance in grasp postures, underscoring the complexity and synergy of hand movements. Grasp classification experiments validated the efficacy of PCA-based synergy, achieving high classification accuracies (95.84%-92.34%) and demonstrating the method's competitive performance in scenarios requiring reduced sensor complexity, as confirmed by confusion matrices and comparative analysis with existing methodologies. The t-distributed stochastic neighbor embedding (t-SNE) visualization showcased clusters of grasp postures and forces, unveiling similarities and patterns among different grasp types (GTs). These findings could serve as a comprehensive guide in the design and control of five-fingered robotic hands and exoskeletons for rehabilitation applications, enabling the replication of natural hand movements.
Renewable energy is a booking technology nowadays where several engineering applications are centralized on such technique. However, integrating sports, gaming, and renewable energy in a full system has not been devel...
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ISBN:
(数字)9798331529604
ISBN:
(纸本)9798331529611
Renewable energy is a booking technology nowadays where several engineering applications are centralized on such technique. However, integrating sports, gaming, and renewable energy in a full system has not been developed so far. Therefore, this paper proposes the GreenFit, a renewable energy and interactive entertainment system. This novel smart trainer comprises two interconnected components that combine electrical power generation and engaging gaming experiences. Firstly, the system focuses on renewable energy generation during physical activities. A generator installed on the bicycle captures the electricity generated by pedaling, creating a self-sustaining source of green energy. This eco-friendly approach not only encourages sustainable practices but also involves users directly in the energy generation process during their workouts. Secondly, the Smart Trainer features an interactive gaming interface, enhancing the fitness experience. Equipped with sensors that respond to the user's pedaling, the connected game behaves in real-time to match the workout's pace and intensity. The virtual environment reacts instantly to user movements, including braking, steering, and moving left or right, providing an immersive and responsive gaming experience. To sum up, this research paper focuses specifically on the development and evaluation of the GreenFit system, highlighting its innovative integration of renewable energy and gaming. Testing and validation yield in very good results.
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