Also, more special postures than common postures were found among "representative" postures. Our results confirmed that PSI is a relatively accurate index of similarity in synergy space both with synthetic data and real experimental data. Further, we used PSI to identify postures that are "representative" in the sense that they have a greater overlap (in synergy space) with a large number of postures. After confirming that it performs well with the synthetic dataset, we used it to analyze the experimental data. First, we tested the performance of this index using a synthetic dataset. We developed a posture similarity index (PSI), that represents the similarity between posture in the synergy (Principal component) space. We used principal component analysis to extract kinematic synergies from this 21-DoF data. We modeled the hand as a 21-DoF system and computed the corresponding joint angles. A 16-sensor electromagnetic tracking system captured the kinematics of individual finger phalanges (segments). Twelve right-handed volunteers performed 70 postures, and lifted and held 30 objects (total of 100 different postures, each performed five times). In this study, we developed an index called the posture similarity index to quantify the similarity between two human hand postures. Attempts to compare postures of the hand have been made for use in robotics and animation industries. The central nervous system (CNS) uses different strategies in different manipulation tasks based on task requirements. The human hand can perform a range of manipulation tasks, from holding a pen to holding a hammer.
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