Human Hand Motion Analysis with Multisensory Information

Abstract

In order to study and analyse human hand motions which contain multimodal information, a generalised framework integrating multiple sensors is proposed and consists of modules of sensor integration, signal preprocessing, correlation study of sensory information and motion identification. Three types of sensors are integrated to simultaneously capture the finger angle trajectories, the hand contact forces and the forearm electromyography (EMG) signals. To facilitate the rapid acquisition of human hand tasks, methods to automatically synchronise and segment manipulation primitives are developed in the signal preprocessing module. 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, recognising different hand grasps and manipulations based on the EMG signals is investigated by using Fuzzy Gaussian Mixture Models (FGMMs) and results of comparative experiments show FGMMs outperform Gaussian Mixture Models (GMMs) and Support Vector Machine (SVM) with a higher recognition rate. The proposed framework integrating the stateof-the-art sensor technology with the developed algorithms provides researchers a versatile and adaptable platform for human hand motion analysis and has potential applications especially in robotic hand or prosthetic hand control and Human Computer Interaction (HCI).

Multithreshold Image Segmentation and Computer Simulation Based on Interactive Processing System

“…is study explains the wide application of computer simulation technology. According to the characteristics of interference image, we can judge the properties of the recorded objects, the effect of the recorded images, and the processing methods adopted [13]. e ability of studying phenomenon processing has been greatly improved.…”

Soft human–machine interfaces: design, sensing and stimulation

“…Conventional rigid HMIs have been widely applied in robotic systems, such as rehabilitation robots [2,3], motion gesture monitoring [4], and biological health monitoring [5]. However, they generally lead to the uncomfortablity of the human body and unstable signal in motion [6][7][8] by directly laminating onto the skin surface [9,10].…”