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In this paper, an integrated framework with multiple sensory information for analysing human hand motions is proposed, and it consists of components of system integration, signal preprocessing, correlation study of sensory information and human motion recognition based on manipulation intention. Three types of sensors are employed in the framework to simultaneously capture the finger angle trajectory, the hand contact force and the forearm electromyography (EMG) signal. The signal preprocessing module is to facilitate the rapid acquisition of human hand tasks by automatically synchronising and segmenting the manipulation primitives. Correlations of the sensory information are studied by using Empirical Copula and demonstrate there exist significant relationships between muscle signals and finger trajectories and between muscle signals and contact forces. In addition, motion recognition based on the EMG intention is investigated by using both Gaussian Mixture Models (GMMs) and Support Vector Machine (SVM) and discussion of the comparative results is presented.