This study investigates the dynamics of geometric hand synergies during manipulation tasks, contrasting them with grasping movements. The objective is to identify dominant synergies and their contribution to dexterous tasks. Using an 18-DoF Dataglove, we recorded the hand movements of six able-bodied participants during 18 daily activities. To analyze the data, principal component analysis (PCA) was used to determine the synergies for each subject. A k-means clustering approach was applied to compare synergies across subjects and identify similarities. Furthermore, the most relevant joints were identified based on their contributions to the principal components, with those exhibiting the highest loadings in the dominant synergy considered key for manipulation. Results revealed that manipulation tasks are described by 7 dominant synergies. Additionally, to perform the tasks a greater joint independence is required compared to grasping, with significant contributions from the proximal interphalangeal (PIP) joints of the middle, ring, and little fingers.Clinical relevance- Identifying the most involved hand joints during manipulation will help guide the design of the next generation of advanced bionic limbs.